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\n:::\n\n## 서울 R 미트업 (2023년) {.smaller}\n\n::: columns\n::: column\n::: r-stack\n\n![](img/seoul-r-meetup-september.jpg){.fragment width=\"320\"}\n\n::: fragment\n![](img/seoul-r-meetup-august.jpg){.fragment width=\"320\"}\n:::\n\n\n::: fragment\n![](img/seoul-r-meetup-july.jpg){.fragment width=\"320\"}\n:::\n\n::: fragment\n![](img/seoulr-r-meetup-june.jpg){.fragment width=\"320\"}\n:::\n\n::: fragment\n![](img/seoul-r-meetup-202305.jpg){.fragment width=\"320\"}\n:::\n\n::: fragment\n![](img/seoul-r-meetup-AsiaR.jpg){.fragment width=\"320\"}\n:::\n\n::: fragment\n![](img/seoul-r-meetup-202304.jpg){.fragment width=\"320\"}\n:::\n\n\n:::\n:::\n\n::: column\n## 참여와 연대\n\n- **서울R미트업 meetup.com**: [link](https://www.meetup.com/seoul-r-meetup)\n - **23년 4월 서울 R 미트업**: [link](https://www.meetup.com/seoul-r-meetup/events/292232899/)\n - **23년 5월 서울 R 미트업**: [link](https://www.meetup.com/seoul-r-meetup/events/292966674/)\n - **23년 6월 서울 R 미트업**: [link](https://www.meetup.com/seoul-r-meetup/events/293485362/)\n - **23년 7월 서울 R 미트업**: [link](https://www.meetup.com/seoul-r-meetup/events/293929603/)\n - **23년 8월 서울 R 미트업**: [link](https://www.meetup.com/seoul-r-meetup/events/294651161/)\n - **23년 9월 서울 R 미트업**: [link](https://www.meetup.com/seoul-r-meetup/events/295338359/)\n- 세계 R 미트업 현황 (Global R Meetup Dashboard): [link](https://r-community.org/usergroups/)\n- 한국 R 사용자회 (Korea R User Group): [link](https://r2bit.com/)\n- 한국 R 컨퍼런스 (Korea R Conference): [link](https://use-r.kr/)\n- 유튜브 채널 (Youtube Channel): [link](https://www.youtube.com/channel/UCW-epmIvjBEhhVXw_F0Nqbw)\n- 페이스북 그룹 (Facebook Group): [link](https://www.facebook.com/groups/tidyverse)\n:::\n:::\n\n\n\n# {background-image=\"img/meetup_logo.jpg\"}\n\n::: r-fit-text\n\n2024년 인공지능 데이터 사이언스\n



\n:::\n\n## 서울 R 미트업 {.smaller}\n\n::: columns\n::: column\n::: r-stack\n\n![](img/meetup/20240430_poster.png){.fragment width=\"320\"}\n\n::: fragment\n![](img/meetup/20240430_poster.png){.fragment width=\"320\"}\n:::\n\n\n\n:::\n:::\n\n::: column\n## 참여와 연대\n\n- **서울R미트업 meetup.com**: [link](https://www.meetup.com/seoul-r-meetup)\n - **23년 4월 서울 R 미트업**: [link](https://www.meetup.com/seoul-r-meetup/events/300281000/)\n - **23년 5월 서울 R 미트업**: [link]()\n - **23년 6월 서울 R 미트업**: [link]()\n - **23년 7월 서울 R 미트업**: [link]()\n - **23년 8월 서울 R 미트업**: [link]()\n - **23년 9월 서울 R 미트업**: [link]()\n- Resources for General Help with R - Regional R communities [link](https://contributor.r-project.org/r-help)\n- 세계 R 미트업 현황 (Global R Meetup Dashboard): [link](https://r-community.org/usergroups/)\n- 한국 R 사용자회 (Korea R User Group): [link](https://r2bit.com/)\n- 한국 R 컨퍼런스 (Korea R Conference): [link](https://use-r.kr/)\n- 유튜브 채널 (Youtube Channel): [link](https://www.youtube.com/channel/UCW-epmIvjBEhhVXw_F0Nqbw)\n- 페이스북 그룹 (Facebook Group): [link](https://www.facebook.com/groups/tidyverse)\n:::\n:::\n\n\n# 챗GPT 도서출판\n\n## 챗GPT 데이터 과학 도서 {.smaller}\n\n::: panel-tabset\n### 챗GPT 시리즈\n\n![](img/book/chatGPT_series3.png){fig-align=\"center\" width=\"552\"}\n\n### 데이터 과학 \n\n::: {#chatGPT-ds layout-ncol=\"2\"}\n![](img/book/cover_ds.jpg){fig-align=\"center\" width=\"250\"}\n\n![](img/book/ds_logo.png){fig-align=\"center\" width=\"300\"}\n:::\n\n📘 교보 POD 종이책: \n\n📗 교보 전자책: \n\n🌐 웹사이트: \n\n💻 소스코드: [@lee2024ds]\n\n### 코딩 \n\n::: {#chatGPT-coding layout-ncol=\"2\"}\n![](img/book/cover_coding.jpg){fig-align=\"center\" width=\"250\"}\n\n![](img/book/coding_logo.png){fig-align=\"center\" width=\"300\"}\n:::\n\n📘 교보 POD 종이책: \n\n📗 교보 전자책: \n\n🌐 웹사이트: \n\n💻 소스코드: [@lee2024coding]\n\n### 쿼토\n\n::: {#chatGPT-sql layout-ncol=\"2\"}\n![](img/book/cover_quarto.jpg){fig-align=\"center\" width=\"250\"}\n\n![](img/book/quarto_logo){fig-align=\"center\" width=\"300\"}\n:::\n\n📘 교보 POD 종이책: \n\n📗 교보 전자책: \n\n🌐 웹사이트: \n\n💻 소스코드: [@lee2024quarto]\n\n### SQL\n\n::: {layout-ncol=\"2\"}\n![](img/book/sql_cover.jpg){fig-align=\"center\" width=\"250\"}\n\n![](img/book/sql_logo.png){fig-align=\"center\" width=\"300\"}\n:::\n\n📘 교보 POD 종이책: \n\n📗 교보 전자책: \n\n🌐 웹사이트: \n\n💻 소스코드: [@lee2024sql]\n\n### 유닉스 쉘\n\n::: {#chatGPT-shell layout-ncol=\"2\"}\n![](img/book/cover_unix.jpg){fig-align=\"center\" width=\"250\"}\n\n![](img/book/hex_chatGPT_unix_shell_02.png){fig-align=\"center\" width=\"300\"}\n:::\n\n📘 교보 POD 종이책: \n\n📗 교보 전자책: [https://bit.ly/KzUdOU](https://bit.ly/3KzUdOU)\n\n🌐 웹사이트: \n\n💻 소스코드: [https://bit.ly/3wrgeuP](https://bit.ly/49p0I1f) [@lee2023shell]\n\n📰 뉴스기사: [챗GPT가 던진 새 가능성, 데이터 과학 분야 통해 미리 살펴보기](https://omn.kr/25f0i)\n\n\n### 확률통계\n\n::: {#chatGPT-ds layout-ncol=\"2\"}\n\n준비중\n\n:::\n\n📘 교보 POD 종이책: 준비중\n\n📗 교보 전자책: 준비중\n\n🌐 웹사이트: \n\n💻 소스코드: \n\n:::\n\n# **글쓰기**
챗GPT 인터페이스 - 오피스 전쟁 \n\n## 구글 검색의 종말(?)\n\n![](img/chatGPT-search.jpg){fig-align=\"center\" width=\"700\"}\n\n## 글쓰기 역사\n\n
\n\n![](img/writing_history.jpg)\n\n::: aside\n- [문자와 종이 역사](https://r2bit.com/quarto/writing_history.html)\n:::\n\n## 오피스 전쟁 {.smaller}\n\n| 항목 | MS 오피스 [^1] | 구글 오피스 [^2] | 리브레 오피스 [^3] | 한컴 오피스 [^4] |\n|:-------------:|:-------------:|:-------------:|:-------------:|:-------------:|\n| 문서 작성 및 편집 | 워드, 엑셀, 파워포인트 등의 데스크톱 및 웹 앱 제공 | 구글 문서, 스프레드시트, 프레젠테이션 등의 웹 앱 제공 | 라이터, 캘크, 임프레스 등의 데스크톱 앱 제공 | 한글, 한셀, 한쇼 등의 데스크톱 및 모바일 앱 제공 |\n| 협업 및 공유 | 원드라이브를 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움 | 구글 드라이브를 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정하고 실시간으로 확인 가능 | 드롭박스나 구글 드라이브와 연동하여 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움 | 넷프리즘을 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움 |\n| 이메일 및 채팅 | Outlook을 통해 비즈니스 메일 제공. Teams을 통해 채팅 및 화상 회의 가능 | Gmail을 통해 비즈니스 메일 제공. Chat과 Meet을 통해 채팅 및 화상 회의 가능 | 이메일 및 채팅 기능 없음 | 이메일 및 채팅 기능 없음 |\n| 가격 | 1인당 월 5\\~20달러 다양한 요금제 제공. 데스크톱 앱 사용 가능한 요금제는 12.5달러부터 시작 | 1인당 월 6\\~18달러까지 다양한 요금제 제공 | 완전 무료 | 개인용은 무료로 사용 가능하며 업무용은 별도로 견적 요청 필요 |\n\n[^1]: https://www.microsoft.com/ko-kr/microsoft-365/business\n\n[^2]: https://workspace.google.com/intl/ko_kr/\n\n[^3]: https://www.libreoffice.org/\n\n[^4]: https://www.hancom.com/product/productMain.do\n\n## 오피스 전쟁: 패러다임 충돌\n\n![](img/writing-evolution.jpg)\n\n## 챗GPT 시대 사무실\n\n::: panel-tabset\n### 챗GPT 사용원인\n\n![](img/chatGPT_why.jpg){width=\"600\"}\n\n### 인터페이스\n\n![](img/chatGPT_interface.jpg){width=\"500\"}\n\n### 챗GPT AS-IS\n\n![](img/chatGPT-AS-IS.jpg)\n\n### 챗GPT TO-BE\n\n![](img/chatGPT-TO-BE.jpg)\n:::\n\n## 챗GPT가 이룬 대통합\n\n![](img/chatGPT-Text-to-X.jpg)\n\n## 인터페이스 (Interface) {.smaller}\n\n::: panel-tabset\n### 진화과정\n\n


\n\n```{mermaid}\nflowchart TB\n \n subgraph A[\"사용자 인터페이스\"]\n direction LR\n CLI[\"CLI
Command Line Interface
1950~\"] --> GUI[\"GUI
Graphic User Inferface
1970~\"]\n GUI --> WI[\"Web Interface
1994~\"]\n WI --> MI[\"Mobile Interface
2007~\"]\n MI --> NUI[\"LUI
Language User Interface
2023~\"]\n\n end\n\n\nclass A nodeStyle\n\nclassDef nodeStyle fill:#fcfbfa,stroke:#000000,stroke-width:0.7px,font-weight:bold,font-size:14px;\n```\n\n### CLI\n\n
\n\n![](img/ui_cli.jpg)\n\n- \n\n### GUI\n\n
\n\n![](img/ui_gui.jpg)\n\n- \n\n### WUI\n\n

\n\n![](img/ui_web.jpg)\n\n### MUI\n\n![](img/ui_mi.jpg)\n\n- \n\n### LUI\n\n![](img/sam_altman_LUI.jpg)\n\n- \n:::\n\n## 챗GPT NUI {.smaller}\n\n::: panel-tabset\n### 1:1 채팅\n\n- 챗GPT (ChatGPT)\n\n![](img/chatgpt_writing.gif){fig-align=\"center\" width=\"634\"}\n\n### 클릭하여 완료\n\n- OpenAI Playground\n\n![](img/ui_playground.jpg){fig-align=\"center\" width=\"645\"}\n\n### 자동완성\n\n- GitHub Copilot\n\n![](img/ui_copilot.gif){fig-align=\"center\"}\n\n- \n\n### 명령 팔레트\n\n::: columns\n::: column\n#### 사례\n\n::: r-stack\n![](img/command-tree.gif)\n\n::: fragment\n![](img/generate_code.gif)\n:::\n\n::: fragment\n{{< video https://blog.replit.com/images/ai/aimode_future.mp4 width=\"500\" height=\"400\" >}}\n:::\n:::\n\n- \n- \n:::\n\n::: column\n#### Command Palette 역사\n\n- 60년대로 거슬러 올라가는 명령 팔레트는 최근 기술 업계에서 다시 인기를 얻고 있으며 사용자 중심 소프트웨어의 필수적인 부분으로 간주됨.\n- CLI는 개인용 컴퓨터가 대중화되기 전 초기 컴퓨팅을 지배했으며 사용자가 시스템을 작동하기 위해 특정 명령을 알아야 했다. 1984년 Apple의 매킨토시와 함께 도입된 그래픽 사용자 인터페이스(GUI)는 중요한 혁신으로 여겨졌지만, 소프트웨어의 복잡성이 증가함에 따라 GUI만으로는 탐색하기가 점점 더 어려워졌다.\n- 사용자가 항목 이름의 짧은 약어입력하여 앱과 파일을 검색할 수 있는 런처가 점점 더 보편화 되었다. 예를 들어 2001년에 출시된 Mac OS X용 LaunchBar, Quicksilver, 2005년에 출시된 Apple의 시스템 전체 Spotlight가 있다. 검색과 CLI의 결합은 **Command Palette**의 탄생으로 이어졌다.\n- 오늘날 우리가 알고 있는 현대식 명령 팔레트는 2011년 Sublime Text 2 베타 Joe Skinner가 처음 소개. 명령줄 사용에 익숙한 프로그래머들이 단축키를 모두 외울 필요 없이 더 빠르게 작업할 수 있어 중요한 발전이 됨.\n:::\n:::\n:::\n\n---\neditor: \n markdown: \n wrap: 72\n---\n\n# 생성형 AI
데이터 사이언스\n\n## 포스터 제작\n\n::: panel-tabset\n### 포스터 시안\n\n![](img/plugin_canva.jpg){fig-align=\"center\" width=\"359\"}\n\n### 포스터 편집\n\n![](img/plugin_canva_edit.jpg){fig-align=\"center\" width=\"580\"}\n\n### 최종 포스터\n\n![](img/plugin_canva_poster.jpg){fig-align=\"center\" width=\"353\"}\n:::\n\n- [ShareGPT: Canva\n 사용사례](https://chat.openai.com/share/9689351d-6f4b-475c-96e4-618e46aca82b)\n\n## API 프로그래밍 {.smaller}\n\n::: panel-tabset\n### 준비 프롬프트\n\n> 웹사이트를 읽고 다음 지령을 준비해\n> https://apis.map.kakao.com/web/documentation/\n\n``` markdown\nKakao 지도 API 문서는 다양한 지도 관련 기능을 제공한다. 이 문서는 지도 객체 생성, 이벤트, 메서드 등에 대한 상세한 정보를 담고 있다. 아래는 주요 내용의 요약이다.\n\n### 지도 객체 생성\n- `kakao.maps.Map(container, options)`: 지도를 생성한다.\n - `container`: 지도가 표시될 HTML element\n - `options`: 중심 좌표, 확대 수준, 지도 종류 등을 설정할 수 있다.\n\n### 주요 메서드\n- `setCenter(latlng)`: 지도의 중심 좌표를 설정한다.\n- `getCenter()`: 지도의 중심 좌표를 반환한다.\n- `setLevel(level, options)`: 지도의 확대 수준을 설정한다.\n- `getLevel()`: 지도의 확대 수준을 반환한다.\n- `setMapTypeId(mapTypeId)`: 지도의 타입을 설정한다.\n- `getMapTypeId()`: 지도의 타입을 반환한다.\n\n### 이벤트\n- `center_changed`: 중심 좌표가 변경되면 발생한다.\n- `zoom_start`: 확대 수준이 변경되기 직전에 발생한다.\n- `zoom_changed`: 확대 수준이 변경되면 발생한다.\n- `click`: 지도를 클릭하면 발생한다.\n- `dblclick`: 지도를 더블클릭하면 발생한다.\n\n이 외에도 다양한 메서드와 이벤트가 있어, 개발자가 원하는 기능을 쉽게 구현할 수 있다.\n```\n\n### 코드제작 프롬프트롬\n\n> 주소를 넘기면 위경도 좌표를 반환하는 R 코드를 작성해줘.\n\nR에서 주소를 위경도 좌표로 변환하기 위해 `httr` 패키지를 사용하여 Kakao\nAPI를 호출한 예시 코드이다.\n\n``` r\n# 필요한 패키지를 설치하고 불러온다\ninstall.packages(\"httr\")\ninstall.packages(\"jsonlite\")\nlibrary(httr)\nlibrary(jsonlite)\n\n# 주소를 위경도로 변환하는 함수를 정의한다\nget_latlng <- function(address) {\n # Kakao API 키를 설정한다 (본인의 API 키로 교체해야 한다)\n api_key <- \"YOUR_KAKAO_API_KEY\"\n \n # API URL과 파라미터를 설정한다\n url <- \"https://dapi.kakao.com/v2/local/search/address.json\"\n params <- list(query = address)\n headers <- add_headers(\"Authorization\" = paste(\"KakaoAK\", api_key))\n \n # API를 호출한다\n response <- GET(url, query = params, headers)\n \n # 응답을 확인하고 JSON을 파싱한다\n if (status_code(response) == 200) {\n content <- content(response, \"text\", encoding = \"UTF-8\")\n json_data <- fromJSON(content)\n \n # 위경도를 추출한다\n if (json_data$meta$total_count > 0) {\n lat <- json_data$documents[[1]]$y\n lng <- json_data$documents[[1]]$x\n return(list(lat = lat, lng = lng))\n } else {\n return(NULL)\n }\n } else {\n stop(\"API request failed\")\n }\n}\n\n# 함수를 테스트한다\naddress <- \"서울특별시 강남구 테헤란로 212\"\nresult <- get_latlng(address)\nprint(paste(\"Latitude:\", result$lat, \"Longitude:\", result$lng))\n```\n\n### 코드수정\n\n::: {.cell}\n\n```{.r .cell-code code-line-numbers=\"10\"}\nlibrary(httr)\nlibrary(jsonlite)\n\n# 주소를 위경도로 변환하는 함수를 정의한다\nget_latlng <- function(address) {\n # Kakao API 키를 설정한다 (본인의 API 키로 교체해야 한다)\n api_key <- Sys.getenv(\"KAKAO_MAP_API_KEY\")\n \n # API URL과 파라미터를 설정한다\n url <- \"https://dapi.kakao.com/v2/local/search/address.json\"\n params <- list(query = address)\n headers <- add_headers(\"Authorization\" = paste(\"KakaoAK\", api_key))\n \n # API를 호출한다\n response <- GET(url, query = params, headers)\n \n # 응답을 확인하고 JSON을 파싱한다\n if (status_code(response) == 200) {\n content <- content(response, \"text\", encoding = \"UTF-8\")\n json_data <- fromJSON(content)\n \n # 위경도를 추출한다\n if (json_data$meta$total_count > 0) {\n lat <- json_data$documents[[1]]$y\n lng <- json_data$documents[[1]]$x\n return(list(lat = lat, lng = lng))\n } else {\n return(NULL)\n }\n } else {\n stop(\"API request failed\")\n }\n}\n\n# 가톨릭관동대학교 주소\naddress <- \"강원도 강릉시 범일로579번길 24\"\nresult <- get_latlng(address)\nprint(paste(\"Latitude:\", result$lat, \"Longitude:\", result$lng))\n```\n:::\n\n### 작업결과\n\n![](img/plugin_map.png){fig-align=\"center\" width=\"700\"}\n:::\n\n- [ShareGPT: API\n 사용사례](https://chat.openai.com/share/dcfee23a-0853-454b-9d8c-f76fc4474cb6),\n [플러그인(Plugin) - 칸바, 카카오맵,\n 울프람](https://r2bit.com/chatGPT/plugin.html)\n\n## 사실확인 (Wolfram) {.smaller}\n\n::: panel-tabset\n### 지구는 평평해?\n\n울프람 알파(Wolfram Alpha) 사용해서 사실관계를 확인해보자.\n\n> \"지구는 평평하다\"라는 주장을 검증해줘.\n\n``` \n\"지구는 평평하다\"라는 주장에 대해 Wolfram Alpha를 이용해 검증해보았습니다.\n\n### 입력 해석\n- 질문: 지구는 평평하다?\n\n### 결과\n- 답변: 아니다.\n - 지역적으로만 그렇다. 그리고 그것도 특정 지역, 예를 들어 미국 중서부에서만 해당된다.\n\n자세한 정보는 [Wolfram|Alpha 웹사이트](https://www6b3.wolframalpha.com/input?i=Is+the+Earth+flat%3F)에서 확인할 수 있다.\n\n이처럼 Wolfram Alpha는 다양한 주장이나 정보에 대한 팩트체크를 할 수 있는 유용한 도구이다.\n```\n\n- [Wolfram\\|Alpha\n 웹사이트](https://www6b3.wolframalpha.com/input?i=Is+the+Earth+flat%3F)\n\n### 라면은 건강에 안 좋아?\n\n> 라면을 많이 먹으면 건강에 정말 안좋은가?\n\nWolfram Alpha에서는 \"라면을 많이 먹으면 건강에 정말 안 좋은가?\"에 대한\n구체적인 답변을 제공하지 못했다. 그러나 일반적으로 라면은 높은 나트륨,\n지방, 인공 첨가물 등을 포함하고 있어, 과도하게 섭취할 경우 다음과 같은\n건강 문제가 발생할 수 있다:\n\n1. **고혈압**: 라면에는 나트륨이 많이 들어있다. 나트륨 과다 섭취는\n 혈압을 높일 수 있다.\n\n2. **비만**: 라면은 칼로리가 높고, 지방도 많이 들어있다. 지속적으로\n 라면을 많이 먹으면 체중 증가의 위험이 있다.\n\n3. **소화기 문제**: 라면은 소화가 잘 되지 않는 식품 중 하나이다. 과도한\n 섭취는 소화기에 부담을 줄 수 있다.\n\n4. **영양 불균형**: 라면은 영양소가 적고, 나트륨이나 지방 등의 불필요한\n 성분이 많다. 이로 인해 영양 불균형이 발생할 수 있다.\n\n5. **심혈관 질환**: 고혈압, 높은 콜레스테롤 등은 심혈관 질환의 위험을\n 높인다.\n\n라면을 즐길 때는 이러한 점들을 고려하여 적당한 양과 빈도로 섭취하는 것이\n중요하다.\n:::\n\n## 수능문제 풀이 {.smaller}\n\n::: panel-tabset\n### 2023 수능 수학\n\n2023학년도 대학수학능력시험 수학영역 문제를 풀어보자.\n\n![](img/plugin_wolfram.jpg)\n\n### 문제 풀이\n\n> $\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}$ 값은?\n\n식 $$\n\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n$$ 의 값은 $4$입니다.\n\n정확한 형태로는 $2^{(2 - \\sqrt{2}) (2 + \\sqrt{2})}$로 표현됩니다.\n\n자세한 정보는 [Wolfram\\|Alpha\n웹사이트](https://www6b3.wolframalpha.com/input?i=%284+%2F+%282%5E%28sqrt%282%29%29%29%29%5E%282+%2B+sqrt%282%29%29)에서\n확인할 수 있다.\n\n### 풀이 과정\n\n> 과정도 설명해줘.\n\n::: columns\n::: column\n물론이다. 주어진 식은 $$\n\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n$$이다. 이 식을 단계별로 분해해보자.\n\n- 단계 1: 분모를 간단하게 만들기
먼저, 분모인 $2^{\\sqrt{2}}$를\n 간단하게 만들기 위해 분자와 분모에 $2^{\\sqrt{2}}$를 곱한다. 이렇게\n 하면:\n\n$$\n\\left(\\frac{4 \\times 2^{\\sqrt{2}}}{2^{\\sqrt{2}} \\times 2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n$$\n\n- 단계 2: 분모를 합치기
이제 분모의\n $2^{\\sqrt{2}} \\times 2^{\\sqrt{2}}$를 합쳐 $2^{2\\sqrt{2}}$로 만든다.\n\n$$\n\\left(\\frac{4 \\times 2^{\\sqrt{2}}}{2^{2\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n$$\n:::\n\n::: column\n- 단계 3: 분자를 합치기
분자의 $4 \\times 2^{\\sqrt{2}}$를 합쳐\n $2^2 \\times 2^{\\sqrt{2}} = 2^{2+\\sqrt{2}}$로 만든다.\n\n$$\n\\left(\\frac{2^{2+\\sqrt{2}}}{2^{2\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n$$\n\n- 단계 4: 지수 법칙 적용
지수 법칙에 따라 분자와 분모의 지수를\n 빼준다.\n\n$$\n\\left(2^{(2+\\sqrt{2}) - 2\\sqrt{2}}\\right)^{2+\\sqrt{2}} = \\left(2^{(2 - \\sqrt{2})(2 + \\sqrt{2})}\\right)^{2+\\sqrt{2}}\n$$\n\n- 단계 5: 최종 계산
마지막으로, $(2 - \\sqrt{2})(2 + \\sqrt{2})$를\n 계산하면 $4 - 2 = 2$가 된다. 따라서 최종 결과는 $2^2 = 4$이다.\n\n이렇게 해서 $$\n\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}} = 4\n$$임을 확인할 수 있다.\n:::\n:::\n:::\n\n::: aside\n[[ShareGPT: 팩트체크와 수학문제\n풀이](https://chat.openai.com/share/e3e57271-9956-4348-abe9-4a87bc56a984)]{.aside}\n:::\n\n## OpenAI Advanced Data Analysis {.smaller}\n\n
\n\n> Open AI Code Interpreter → Advanced Data Analysis → 챗GPT4\n\n
\n\n![](img/km_code_interpreter.png)\n\n::: aside\n[생성AI 광명시 데이터 분석\n사례](https://r2bit.com/map_challenge/km_BitGPT.html)\n:::\n\n## RStudio Copilot\n\n> `Tools -> Global Options -> Copilot -> Enable Github Copilot`\n\n![](img/rstudio_copilot.jpg)\n\n## 웹앱(Shiny App) 개발 사례\n\n![](img/shiny_app_ci.gif)\n\n## 광명시 보좌관\n\n![](img/km_ai.gif){fig-align=\"center\" width=\"728\"}\n\n- [소스코드](https://github.com/bit2r/BitStat2)\n\n## RTutor & PandasAI\n\n![](img/rtutor_pandasai.png){fig-align=\"center\" width=\"450\"}\n\n::: aside\n- [챗GPT 데이터 과학](https://r2bit.com/gpt-ds/)\n:::\n\n# 도구(문서)의 인간
디지털 저작\n\n
\n\n![](img/homo-faber.jpg)\n\n## 데이터 과학 과거와 현재 {.smaller}\n\n

\n\n::: columns\n::: column\n### 과거\n\n
\n\n![](slideshow/202212-youtube/fig/jobshop.gif)\n:::\n\n::: column\n### 현재\n\n
\n\n![](slideshow/202212-youtube/fig/quarto_to_go.gif)\n:::\n:::\n\n## 글쓰기 진화 {.smaller}\n\n::: panel-tabset\n### 글쓰기 역사\n\n![](img/writing_history.jpg)\n\n### 생산성과 자동화를 넘어\n\n![](img/writing-evolution.jpg)\n\n### 개발과 저작\n\n![](img/mermaid_docu_sw.jpg)\n\n\n\n\n\n\n\n\n\n\n\n package1 -->\n product1 -->\n distribute1 -->\n service1 -->\n\n\n\n\n\n\n\n\n\n\n package2 -->\n product2 -->\n distribute2 -->\n service2 -->\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n:::\n\n## 데이터 과학 → 디지털 글쓰기 {.smaller}\n\n::: panel-tabset\n### 데이터 과학\n\n![](img/horst-eco-r4ds.png)\n\n### 문서 구성요소\n\n![](img/data_science_document.jpg)\n\n### 문서 복잡성\n\n![](img/markdown-writing.png){width=\"880\"}\n\n### 문서 도구 개발 역사\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n \n \n
연도도구개발자
1978TeXDonald Knuth
1984Literate ProgrammingDonald Knuth
1988Mathematica NotebooksStephen Wolfram
2001IPythonFernando Perez
2003Emacs org-modeCarsten Dominik
2004MarkdownJohn Gruber
2005Sage NotebookWilliam Stein
2006PandocJohn MacFarlane
2009GitHub Flavored MarkdownTom Preston-Werner
2011iPython NotebookFernando Perez
2012knitrYihui Xie
2014Project JupyterFernando Perez
2020QuartoJ.J. Allaire
\n
\n```\n\n:::\n:::\n:::\n\n\n## 디지털 글쓰기 - 쿼토 {.smaller}\n\n::: panel-tabset\n### 삼권분립\n\n![](img/three-pillar.png){fig-align=\"center\" width=\"615\"}\n\n::: aside\n[챗GPT 디지털 글쓰기](https://r2bit.com/gpt-writing/)\n:::\n\n\n### 쿼토 소개\n\n
\n\n![](img/horst_quarto_schematic.png)\n\n### 쿼토 작업흐름\n\n
\n
\n\n![](img/quarto_workflow_mermaid.png)\n\n\n\n\n\n\n\n 주피터 -->\n 마크다운 -->\n 팬독 -->\n pdf -->\n html -->\n docx -->\n knitr -->\n 마크다운 -->\n\n\n### 쿼토 \n\n| 기능 | R 마크다운 | 쿼토(Quarto) |\n|:-----------------:|:-----------------------------|:-----------------------|\n| 파일형식 | [html_document](https://pkgs.rstudio.com/rmarkdown/reference/html_document.html) / [pdf_document](https://pkgs.rstudio.com/rmarkdown/reference/pdf_document.html) / [word_document](https://pkgs.rstudio.com/rmarkdown/reference/word_document.html) | [html](https://quarto.org/docs/output-formats/html-basics.html) / [pdf](https://quarto.org/docs/output-formats/pdf-basics.html) / [docx](https://quarto.org/docs/output-formats/ms-word.html) |\n| 비머(Beamer) | [beamer_presentation](https://pkgs.rstudio.com/rmarkdown/reference/beamer_presentation.html) | [beamer](https://quarto.org/docs/presentations/beamer.html) |\n| 파워포인트(PPT) | [powerpoint_presentation](https://pkgs.rstudio.com/rmarkdown/reference/powerpoint_presentation.html) | [pptx](https://quarto.org/docs/presentations/powerpoint.html) |\n| 웹 슬라이드 | [xaringan](https://bookdown.org/yihui/rmarkdown/xaringan.html) / [ioslides](https://bookdown.org/yihui/rmarkdown/ioslides-presentation.html) / [revealjs](https://bookdown.org/yihui/rmarkdown/revealjs.html) | [revealjs](https://quarto.org/docs/presentations/revealjs/) |\n| 고급 레이아웃 | [tufte](https://bookdown.org/yihui/rmarkdown/tufte-handouts.html) / [distill](https://rstudio.github.io/distill/figures.html) | [Quarto Article Layout](https://quarto.org/docs/authoring/article-layout.html) |\n| 상호 참조 | [html_document2](https://bookdown.org/yihui/bookdown/a-single-document.html) / [pdf_document2](https://bookdown.org/yihui/bookdown/a-single-document.html) / [word_document2](https://bookdown.org/yihui/bookdown/a-single-document.html) | [Quarto Crossrefs](https://quarto.org/docs/authoring/cross-references.html) |\n| 웹사이트/블로그 | [blogdown](https://pkgs.rstudio.com/blogdown/) / [distill](https://pkgs.rstudio.com/distill/) | [Quarto Websites](https://quarto.org/docs/websites/) / [Quarto Blogs](https://quarto.org/docs/websites/website-blog.html) |\n| 책 | [bookdown](https://pkgs.rstudio.com/bookdown/) | [Quarto Books](https://quarto.org/docs/books/) |\n| 인터랙티브 문서 | [Shiny Documents](https://bookdown.org/yihui/rmarkdown/shiny-documents.html) | [Quarto Interactive Documents](https://quarto.org/docs/interactive/shiny/) |\n| 페이지 HTML | [pagedown](https://github.com/rstudio/pagedown) | 출시 예정 |\n| 학술 논문 | [rticles](https://pkgs.rstudio.com/rticles/) | [Quarto Journal Articles](https://quarto.org/docs/journals/) |\n| 대쉬보드 | [flexdashboard](https://pkgs.rstudio.com/flexdashboard/) | [Quarto Dashboards](https://quarto.org/docs/dashboards/) |\n| 인터랙티브 자습서 | [learnr](https://pkgs.rstudio.com/learnr/) | 계획 없음 |\n\n: R 마크다운과 쿼토 비교 {#tbl-quarto-rmarkdown}\n\n:::\n\n\n# 참고문헌\n\n::: {#refs}\n:::\n\n\n \n \n\n# 통계 → 데이터 사이언스 → 챗GPT 인공지능\n\n## 교육과정 {.smaller}\n\n::: panel-tabset\n### 종이와 연필\n\n![](img/cku_statistics.jpg){fig-align=\"center\" width=\"413\"}\n\n### 통계 보고서\n\n![](img/cku_sas_word.png){fig-align=\"center\" width=\"715\"}\n\n### DS 입문\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n \n \n
\n
\n 연세대\n 데이터 사이언스 입문\n
주차학습내용날짜
1데이터 과학 (Data Science)3월7일
2프로그래밍 기초와 마크다운 보고서3월14일
3데이터와 커뮤니티 찾기, 도움 청하기, 프로젝트 설정3월21일
4데이터 수집-
5자료구조(Data Structures)4월4일
6데이터프레임과 SQL4월11일
7버전제어, 협업, 그리고 저작권과 라이선스4월18일
8중간고사4월25일
9시각화 (Visualization)5월2일
10정규표현식5월9일
11데이터 과학 프로그래밍5월16일
12데이터 과학 제품-
13함수형 프로그래밍5월30일
14데이터 과학 저작6월6일(현충일)
15R 팩키지6월13일
16기말고사6월20일
\n
\n```\n\n:::\n:::\n\n- [연세대 \"데이터 과학 입문\"](https://statkclee.github.io/yonsei/)\n\n### 고급 DS\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n \n \n
\n
\n 연세대\n 데이터 (사이언스) 공학\n
주차학습내용날짜
1데이터 사이언스 언어9월6일
2[언어] R9월20일
3[언어] 파이썬9월27일
4[언어] API 프로그래밍10월4일
5데이터 사이언스 자료구조10월11일
6[자료구조] 시계열 데이터10월18일
7중간고사10월25일
8[자료구조] 지리 공간 데이터11월01일
9[자료구조] NLP - 텍스트 마이닝(R)11월08일
10[자료구조] NLP - 파이썬11월15일
11빅데이터 시각화11월22일
12기계학습 - 예측모형11월29일
13DevOps - 도커, HPC, 스파크12월06일
14제품화12월13일
15기말고사12월20일
\n
\n```\n\n:::\n:::\n\n- [연세대 \"데이터 (사이언스) 공학\"](https://statkclee.github.io/yonsei2/)\n\n### GS 칼텍스\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n \n \n
\n
\n GS 칼텍스\n 시민 데이터 과학\n
일정과정과정상세데이터셋

1주차

\n

Introduction

\n
- Quantifying performance indicators
- Stats and Probability for Data Science\">
    \n
  • Defining and solving problems
    - Quantifying performance indicators
    - Stats and Probability for Data Science
  • \n
\n
-

2주차

\n

Data Science

\n
- Kaggle: MPG / Titanic EDA 개념과 Data science 전체흐름 습득
- EDA, Feature engineering, Visualization, Correlating 등 preprocessing 작업
- Model, predict and evaluation 의 전체 과정을 습득\">
    \n
  • Python 기초 문법 복습 및 NumPy, Pandas 등 연습
    - Kaggle: MPG / Titanic EDA 개념과 Data science 전체흐름 습득
    - EDA, Feature engineering, Visualization, Correlating 등 preprocessing 작업
    - Model, predict and evaluation 의 전체 과정을 습득
  • \n
\n
-

3주차

\n

Regression and Prediction

\n
    \n
  • Regression에 대한 개념 및 이론 설명
  • \n
\n

Breast Cancer, California housing prices, Bike-sharing

\n

3주차

\n

Regression and Prediction

\n
- Linear Regression 중심, CART, SVM, Ensemble기법 적용
- Training & Evaluation (MSE, MAE, RMSE, RMSLE, R-Squared, etc.)
- Basic hyperparameter tuning 수행\">
    \n
  • EDA 및 preprocessing
    - Linear Regression 중심, CART, SVM, Ensemble기법 적용
    - Training & Evaluation (MSE, MAE, RMSE, RMSLE, R-Squared, etc.)
    - Basic hyperparameter tuning 수행
  • \n
\n
-

4주차

\n

Classification and Hypothesis Testing

\n
    \n
  • Classification과 필요 개념 및 이론 설명
  • \n
\n

Titanic, Iris, Penuins, San Francisco Crime

\n

4주차

\n

Classification and Hypothesis Testing

\n
- Training & Evaluation / Basic hyperparameter tuning 수행\">
    \n
  • Logistic Regression과 Decision Tree, SVC, Random Forest, XGBoost, LGBM 등 적용
    - Training & Evaluation / Basic hyperparameter tuning 수행
  • \n
\n
-

5주차

\n

Case Studies and Projects

\n
- Case Study 및 Final Quiz 해설
- 개인 별 Project 인터뷰 및 Grouping / 공통과제 선정
- Project 진행, 발표, Best Project 선정 및 공유\">
    \n
  • 기존 수업 이론/실습 개념 바탕 Case Study 3종 (+Final Quiz)
    - Case Study 및 Final Quiz 해설
    - 개인 별 Project 인터뷰 및 Grouping / 공통과제 선정
    - Project 진행, 발표, Best Project 선정 및 공유
  • \n
\n
-
\n
\n```\n\n:::\n:::\n\n- [GS 칼텍스 데이터 사이언스](https://r2bit.com/curriculum/)\n\n\n### GS 칼텍스\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n \n \n
\n
\n GS 칼텍스\n 데이터 과학\n
일정과목명학습상세데이터셋
00주차환경설정학습 도구 설치 및 학습환경파머 펭귄, BitData
01주차디지털 글쓰기(보고서)마크다운 / QuartoBitData
02주차명령라인 데이터 분석자동화(Shell)SWC
03주차버전 제어버전제어/협업(Git / GitHub / GitLab)
04주차프로그래밍SQLData Carpentry, DVD 렌탈
05주차프로그래밍시각화gapminder
06주차프로그래밍파이썬
07주차대쉬보드Flexdashboard / Shinydashboard
08주차기계학습tidymodels / scikit-learn
09주차데이터 과학 제품RESTful API
10주차특수 데이터시계열(Time Series)KOSPI
11주차특수 데이터공간정보(Geospatial) / 텍스트 분석미디어오늘
12주차비정형 데이터텍스트와 이미지 (딥러닝)
\n
\n```\n\n:::\n:::\n\n- [GS 칼텍스 데이터 사이언스](https://r2bit.com/curriculum/)\n\n\n\n### 챗GPT DS {.smaller}\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n
\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n\n\n \n\n\n \n \n \n \n\n\n \n\n\n \n \n \n \n\n\n \n\n\n \n \n \n \n\n\n \n\n\n \n\n\n \n \n \n
\n
\n 챗GPT 데이터 사이언스\n 건국대 전문교육과정\n
강의주제강의내용수업방법
1 주차 - 2023-07-01
데이터 사이언스 언어챗GPT와 데이터 사이언스핸즈온 실습
데이터 사이언스 언어Tidyverse와 챗GPT핸즈온 실습
데이터 사이언스 언어파이썬/R/SQL 그리고 API핸즈온 실습
2 주차 - 2023-07-08
소프트웨어 카펜트리 (기본기)자동화: 유닉스 쉘핸즈온 실습
소프트웨어 카펜트리 (기본기)버전제어: Git / GitHub핸즈온 실습
3 주차 - 2023-07-15
소프트웨어 카펜트리 (기본기)파이썬 / R핸즈온 실습
소프트웨어 카펜트리 (기본기)데이터베이스핸즈온 실습
4 주차 - 2023-07-22
데이터 과학 자료구조숫자/범주 --> 데이터프레임핸즈온 실습
데이터 과학 자료구조날짜/공간정보/네트워크핸즈온 실습
데이터 과학 자료구조텍스트/이미지/오디오핸즈온 실습
\n
\n
\n
\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n\n\n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n\n\n \n\n\n \n \n \n \n\n\n \n\n\n \n\n\n \n \n \n \n\n\n \n\n\n \n\n\n \n \n \n
강의주제강의내용수업방법
5 주차 - 2023-07-29
디지털 글쓰기, 대쉬보드, API 개발디지털 보고서(PDF, HTML, PPT)핸즈온 실습
디지털 글쓰기, 대쉬보드, API 개발Serverless 대쉬보드, R/Python 대쉬보드핸즈온 실습
디지털 글쓰기, 대쉬보드, API 개발기계학습 모형 API 배포핸즈온 실습
- 주차 - 2023-08-05
휴가휴가-
6 주차 - 2023-08-12
챗GPT와 오픈소스(오픈소스) 거대언어모형(LLM)핸즈온 실습
챗GPT와 오픈소스Write/Draw/Coding/Reasoning핸즈온 실습
챗GPT와 오픈소스AI 시대 데이터 사이언스 제품개발 방법핸즈온 실습
7 주차 - 2023-08-19
Prompt Engineering사용자 및 개발자를 위한 프롬프트 공학핸즈온 실습
Prompt EngineeringOpenAI API 기반 NLP 개발핸즈온 실습
Prompt EngineeringOPL(OpenAI, Pinecone, Langchain) 프레임워크핸즈온 실습
8 주차 - 2023-08-26
챗GPT AI 앱 프로젝트오픈소스 LLM 기반 AI 데이터 분석 제품핸즈온 실습
챗GPT AI 앱 프로젝트데이터 사이언스 AutoGPT핸즈온 실습
챗GPT AI 앱 프로젝트챗GPT AI 프로젝트 발표핸즈온 실습
\n
\n
\n
\n```\n\n:::\n:::\n\n### 평가\n\n
\n\n![](img/education_evaluation.jpg)\n\n- [데이터 사이언스 MCQ](https://r2bit.com/curriculum/mcq_stat.html)\n\n:::\n\n\n\n", + "supporting": [], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": { + "include-after-body": [ + "\n\n\n" + ] + }, + "engineDependencies": {}, + "preserve": {}, + "postProcess": true + } +} \ No newline at end of file diff --git a/_freeze/slideshow/202404-sch/chatgpt-data-science/execute-results/html.json b/_freeze/slideshow/202404-sch/chatgpt-data-science/execute-results/html.json new file mode 100644 index 0000000..5f572cb --- /dev/null +++ b/_freeze/slideshow/202404-sch/chatgpt-data-science/execute-results/html.json @@ -0,0 +1,15 @@ +{ + "hash": "febd361feb85b0a1da2e48f8711d4ae6", + "result": { + "engine": "knitr", + "markdown": "---\neditor: \n markdown: \n wrap: 72\n---\n\n\n# 생성형 AI
데이터 사이언스\n\n## 포스터 제작\n\n::: panel-tabset\n### 포스터 시안\n\n![](img/plugin_canva.jpg){fig-align=\"center\" width=\"359\"}\n\n### 포스터 편집\n\n![](img/plugin_canva_edit.jpg){fig-align=\"center\" width=\"580\"}\n\n### 최종 포스터\n\n![](img/plugin_canva_poster.jpg){fig-align=\"center\" width=\"353\"}\n:::\n\n- [ShareGPT: Canva\n 사용사례](https://chat.openai.com/share/9689351d-6f4b-475c-96e4-618e46aca82b)\n\n## API 프로그래밍 {.smaller}\n\n::: panel-tabset\n### 준비 프롬프트\n\n> 웹사이트를 읽고 다음 지령을 준비해\n> https://apis.map.kakao.com/web/documentation/\n\n``` markdown\nKakao 지도 API 문서는 다양한 지도 관련 기능을 제공한다. 이 문서는 지도 객체 생성, 이벤트, 메서드 등에 대한 상세한 정보를 담고 있다. 아래는 주요 내용의 요약이다.\n\n### 지도 객체 생성\n- `kakao.maps.Map(container, options)`: 지도를 생성한다.\n - `container`: 지도가 표시될 HTML element\n - `options`: 중심 좌표, 확대 수준, 지도 종류 등을 설정할 수 있다.\n\n### 주요 메서드\n- `setCenter(latlng)`: 지도의 중심 좌표를 설정한다.\n- `getCenter()`: 지도의 중심 좌표를 반환한다.\n- `setLevel(level, options)`: 지도의 확대 수준을 설정한다.\n- `getLevel()`: 지도의 확대 수준을 반환한다.\n- `setMapTypeId(mapTypeId)`: 지도의 타입을 설정한다.\n- `getMapTypeId()`: 지도의 타입을 반환한다.\n\n### 이벤트\n- `center_changed`: 중심 좌표가 변경되면 발생한다.\n- `zoom_start`: 확대 수준이 변경되기 직전에 발생한다.\n- `zoom_changed`: 확대 수준이 변경되면 발생한다.\n- `click`: 지도를 클릭하면 발생한다.\n- `dblclick`: 지도를 더블클릭하면 발생한다.\n\n이 외에도 다양한 메서드와 이벤트가 있어, 개발자가 원하는 기능을 쉽게 구현할 수 있다.\n```\n\n### 코드제작 프롬프트롬\n\n> 주소를 넘기면 위경도 좌표를 반환하는 R 코드를 작성해줘.\n\nR에서 주소를 위경도 좌표로 변환하기 위해 `httr` 패키지를 사용하여 Kakao\nAPI를 호출한 예시 코드이다.\n\n``` r\n# 필요한 패키지를 설치하고 불러온다\ninstall.packages(\"httr\")\ninstall.packages(\"jsonlite\")\nlibrary(httr)\nlibrary(jsonlite)\n\n# 주소를 위경도로 변환하는 함수를 정의한다\nget_latlng <- function(address) {\n # Kakao API 키를 설정한다 (본인의 API 키로 교체해야 한다)\n api_key <- \"YOUR_KAKAO_API_KEY\"\n \n # API URL과 파라미터를 설정한다\n url <- \"https://dapi.kakao.com/v2/local/search/address.json\"\n params <- list(query = address)\n headers <- add_headers(\"Authorization\" = paste(\"KakaoAK\", api_key))\n \n # API를 호출한다\n response <- GET(url, query = params, headers)\n \n # 응답을 확인하고 JSON을 파싱한다\n if (status_code(response) == 200) {\n content <- content(response, \"text\", encoding = \"UTF-8\")\n json_data <- fromJSON(content)\n \n # 위경도를 추출한다\n if (json_data$meta$total_count > 0) {\n lat <- json_data$documents[[1]]$y\n lng <- json_data$documents[[1]]$x\n return(list(lat = lat, lng = lng))\n } else {\n return(NULL)\n }\n } else {\n stop(\"API request failed\")\n }\n}\n\n# 함수를 테스트한다\naddress <- \"서울특별시 강남구 테헤란로 212\"\nresult <- get_latlng(address)\nprint(paste(\"Latitude:\", result$lat, \"Longitude:\", result$lng))\n```\n\n### 코드수정\n\n\n::: {.cell}\n\n```{.r .cell-code code-line-numbers=\"10\"}\nlibrary(httr)\nlibrary(jsonlite)\n\n# 주소를 위경도로 변환하는 함수를 정의한다\nget_latlng <- function(address) {\n # Kakao API 키를 설정한다 (본인의 API 키로 교체해야 한다)\n api_key <- Sys.getenv(\"KAKAO_MAP_API_KEY\")\n \n # API URL과 파라미터를 설정한다\n url <- \"https://dapi.kakao.com/v2/local/search/address.json\"\n params <- list(query = address)\n headers <- add_headers(\"Authorization\" = paste(\"KakaoAK\", api_key))\n \n # API를 호출한다\n response <- GET(url, query = params, headers)\n \n # 응답을 확인하고 JSON을 파싱한다\n if (status_code(response) == 200) {\n content <- content(response, \"text\", encoding = \"UTF-8\")\n json_data <- fromJSON(content)\n \n # 위경도를 추출한다\n if (json_data$meta$total_count > 0) {\n lat <- json_data$documents[[1]]$y\n lng <- json_data$documents[[1]]$x\n return(list(lat = lat, lng = lng))\n } else {\n return(NULL)\n }\n } else {\n stop(\"API request failed\")\n }\n}\n\n# 가톨릭관동대학교 주소\naddress <- \"강원도 강릉시 범일로579번길 24\"\nresult <- get_latlng(address)\nprint(paste(\"Latitude:\", result$lat, \"Longitude:\", result$lng))\n```\n:::\n\n\n### 작업결과\n\n![](img/plugin_map.png){fig-align=\"center\" width=\"700\"}\n:::\n\n- [ShareGPT: API\n 사용사례](https://chat.openai.com/share/dcfee23a-0853-454b-9d8c-f76fc4474cb6),\n [플러그인(Plugin) - 칸바, 카카오맵,\n 울프람](https://r2bit.com/chatGPT/plugin.html)\n\n## 사실확인 (Wolfram) {.smaller}\n\n::: panel-tabset\n### 지구는 평평해?\n\n울프람 알파(Wolfram Alpha) 사용해서 사실관계를 확인해보자.\n\n> \"지구는 평평하다\"라는 주장을 검증해줘.\n\n``` \n\"지구는 평평하다\"라는 주장에 대해 Wolfram Alpha를 이용해 검증해보았습니다.\n\n### 입력 해석\n- 질문: 지구는 평평하다?\n\n### 결과\n- 답변: 아니다.\n - 지역적으로만 그렇다. 그리고 그것도 특정 지역, 예를 들어 미국 중서부에서만 해당된다.\n\n자세한 정보는 [Wolfram|Alpha 웹사이트](https://www6b3.wolframalpha.com/input?i=Is+the+Earth+flat%3F)에서 확인할 수 있다.\n\n이처럼 Wolfram Alpha는 다양한 주장이나 정보에 대한 팩트체크를 할 수 있는 유용한 도구이다.\n```\n\n- [Wolfram\\|Alpha\n 웹사이트](https://www6b3.wolframalpha.com/input?i=Is+the+Earth+flat%3F)\n\n### 라면은 건강에 안 좋아?\n\n> 라면을 많이 먹으면 건강에 정말 안좋은가?\n\nWolfram Alpha에서는 \"라면을 많이 먹으면 건강에 정말 안 좋은가?\"에 대한\n구체적인 답변을 제공하지 못했다. 그러나 일반적으로 라면은 높은 나트륨,\n지방, 인공 첨가물 등을 포함하고 있어, 과도하게 섭취할 경우 다음과 같은\n건강 문제가 발생할 수 있다:\n\n1. **고혈압**: 라면에는 나트륨이 많이 들어있다. 나트륨 과다 섭취는\n 혈압을 높일 수 있다.\n\n2. **비만**: 라면은 칼로리가 높고, 지방도 많이 들어있다. 지속적으로\n 라면을 많이 먹으면 체중 증가의 위험이 있다.\n\n3. **소화기 문제**: 라면은 소화가 잘 되지 않는 식품 중 하나이다. 과도한\n 섭취는 소화기에 부담을 줄 수 있다.\n\n4. **영양 불균형**: 라면은 영양소가 적고, 나트륨이나 지방 등의 불필요한\n 성분이 많다. 이로 인해 영양 불균형이 발생할 수 있다.\n\n5. **심혈관 질환**: 고혈압, 높은 콜레스테롤 등은 심혈관 질환의 위험을\n 높인다.\n\n라면을 즐길 때는 이러한 점들을 고려하여 적당한 양과 빈도로 섭취하는 것이\n중요하다.\n:::\n\n## 수능문제 풀이 {.smaller}\n\n::: panel-tabset\n### 2023 수능 수학\n\n2023학년도 대학수학능력시험 수학영역 문제를 풀어보자.\n\n![](img/plugin_wolfram.jpg)\n\n### 문제 풀이\n\n> $\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}$ 값은?\n\n식 $$\n\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n$$ 의 값은 $4$입니다.\n\n정확한 형태로는 $2^{(2 - \\sqrt{2}) (2 + \\sqrt{2})}$로 표현됩니다.\n\n자세한 정보는 [Wolfram\\|Alpha\n웹사이트](https://www6b3.wolframalpha.com/input?i=%284+%2F+%282%5E%28sqrt%282%29%29%29%29%5E%282+%2B+sqrt%282%29%29)에서\n확인할 수 있다.\n\n### 풀이 과정\n\n> 과정도 설명해줘.\n\n::: columns\n::: column\n물론이다. 주어진 식은 $$\n\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n$$이다. 이 식을 단계별로 분해해보자.\n\n- 단계 1: 분모를 간단하게 만들기
먼저, 분모인 $2^{\\sqrt{2}}$를\n 간단하게 만들기 위해 분자와 분모에 $2^{\\sqrt{2}}$를 곱한다. 이렇게\n 하면:\n\n$$\n\\left(\\frac{4 \\times 2^{\\sqrt{2}}}{2^{\\sqrt{2}} \\times 2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n$$\n\n- 단계 2: 분모를 합치기
이제 분모의\n $2^{\\sqrt{2}} \\times 2^{\\sqrt{2}}$를 합쳐 $2^{2\\sqrt{2}}$로 만든다.\n\n$$\n\\left(\\frac{4 \\times 2^{\\sqrt{2}}}{2^{2\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n$$\n:::\n\n::: column\n- 단계 3: 분자를 합치기
분자의 $4 \\times 2^{\\sqrt{2}}$를 합쳐\n $2^2 \\times 2^{\\sqrt{2}} = 2^{2+\\sqrt{2}}$로 만든다.\n\n$$\n\\left(\\frac{2^{2+\\sqrt{2}}}{2^{2\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n$$\n\n- 단계 4: 지수 법칙 적용
지수 법칙에 따라 분자와 분모의 지수를\n 빼준다.\n\n$$\n\\left(2^{(2+\\sqrt{2}) - 2\\sqrt{2}}\\right)^{2+\\sqrt{2}} = \\left(2^{(2 - \\sqrt{2})(2 + \\sqrt{2})}\\right)^{2+\\sqrt{2}}\n$$\n\n- 단계 5: 최종 계산
마지막으로, $(2 - \\sqrt{2})(2 + \\sqrt{2})$를\n 계산하면 $4 - 2 = 2$가 된다. 따라서 최종 결과는 $2^2 = 4$이다.\n\n이렇게 해서 $$\n\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}} = 4\n$$임을 확인할 수 있다.\n:::\n:::\n:::\n\n::: aside\n[[ShareGPT: 팩트체크와 수학문제\n풀이](https://chat.openai.com/share/e3e57271-9956-4348-abe9-4a87bc56a984)]{.aside}\n:::\n\n## OpenAI Advanced Data Analysis {.smaller}\n\n
\n\n> Open AI Code Interpreter → Advanced Data Analysis → 챗GPT4\n\n
\n\n![](img/km_code_interpreter.png)\n\n::: aside\n[생성AI 광명시 데이터 분석\n사례](https://r2bit.com/map_challenge/km_BitGPT.html)\n:::\n\n## RStudio Copilot\n\n> `Tools -> Global Options -> Copilot -> Enable Github Copilot`\n\n![](img/rstudio_copilot.jpg)\n\n## 웹앱(Shiny App) 개발 사례\n\n![](img/shiny_app_ci.gif)\n\n## 광명시 보좌관\n\n![](img/km_ai.gif){fig-align=\"center\" width=\"728\"}\n\n- [소스코드](https://github.com/bit2r/BitStat2)\n\n## RTutor & PandasAI\n\n![](img/rtutor_pandasai.png){fig-align=\"center\" width=\"450\"}\n\n::: aside\n- [챗GPT 데이터 과학](https://r2bit.com/gpt-ds/)\n:::\n", + "supporting": [], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": {}, + "engineDependencies": {}, + "preserve": {}, + "postProcess": true + } +} \ No newline at end of file diff --git a/_freeze/slideshow/202404-sch/curriculum/execute-results/html.json b/_freeze/slideshow/202404-sch/curriculum/execute-results/html.json new file mode 100644 index 0000000..b5002b6 --- /dev/null +++ b/_freeze/slideshow/202404-sch/curriculum/execute-results/html.json @@ -0,0 +1,15 @@ +{ + "hash": "47515989bcfc0c96d4ded333d14ef11d", + "result": { + "engine": "knitr", + "markdown": "# 통계 → 데이터 사이언스 → 챗GPT 인공지능\n\n## 교육과정 {.smaller}\n\n::: panel-tabset\n### 종이와 연필\n\n![](img/cku_statistics.jpg){fig-align=\"center\" width=\"413\"}\n\n### 통계 보고서\n\n![](img/cku_sas_word.png){fig-align=\"center\" width=\"715\"}\n\n### DS 입문\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nlibrary(rvest)\n\nlibrary(gt)\nlibrary(gtExtras)\n\nds_url <- \"https://statkclee.github.io/yonsei/\"\nds_html <- read_html(ds_url)\n\nds_html |> \n html_elements('table') |> \n html_table() %>%\n .[[1]] |> \n slice(2:n()) |> \n separate(X2, into = c(\"학습내용\", \"날짜\"), sep = \"-\") |> \n separate(학습내용, into = c(\"주차\", \"학습내용\"), sep = \"\\\\.\") |> \n select(주차, 학습내용, 날짜) |> \n drop_na() |> \n mutate(날짜 = str_trim(날짜),\n 학습내용 = str_trim(학습내용)) |> \n mutate(날짜 = if_else(str_detect(날짜, \"^\\\\d\"), 날짜, NA_character_)) |> \n gt() |> \n gt_theme_pff() |> \n sub_missing(\n columns = 날짜,\n missing_text = \"-\"\n ) |> \n cols_align(\"center\") |> \n opt_table_font(\n font = list(\n google_font(name = \"Sunflower\"),\n \"NanumGothic\"\n )\n ) %>% \n tab_style(\n style = list(\n \"font-variant: small-caps;\"\n ),\n locations = cells_body(columns = everything())\n ) %>% \n tab_header(\n title =\n htmltools::tagList(\n htmltools::tags$div(\n style = htmltools::css(\n `text-align` = \"center\"\n ),\n htmltools::HTML(\n web_image(\"img/curriculum_yonsei.png\")\n )\n ),\n htmltools::tags$div(\n htmltools::tags$strong(\"연세대\"),\n \"데이터 사이언스 입문\"\n )\n )\n ) |> \n tab_options(\n table.width = \"65%\",\n heading.title.font.size = px(12)\n )\n```\n\n::: {.cell-output-display}\n\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n \n \n
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\n 연세대\n 데이터 사이언스 입문\n
주차학습내용날짜
1데이터 과학 (Data Science)3월7일
2프로그래밍 기초와 마크다운 보고서3월14일
3데이터와 커뮤니티 찾기, 도움 청하기, 프로젝트 설정3월21일
4데이터 수집-
5자료구조(Data Structures)4월4일
6데이터프레임과 SQL4월11일
7버전제어, 협업, 그리고 저작권과 라이선스4월18일
8중간고사4월25일
9시각화 (Visualization)5월2일
10정규표현식5월9일
11데이터 과학 프로그래밍5월16일
12데이터 과학 제품-
13함수형 프로그래밍5월30일
14데이터 과학 저작6월6일(현충일)
15R 팩키지6월13일
16기말고사6월20일
\n
\n```\n\n:::\n:::\n\n\n- [연세대 \"데이터 과학 입문\"](https://statkclee.github.io/yonsei/)\n\n### 고급 DS\n\n\n::: {.cell}\n\n```{.r .cell-code}\nds2_url <- \"https://statkclee.github.io/yonsei2/\"\nds2_html <- read_html(ds2_url)\n\nds2_html |> \n html_elements('table') |> \n html_table() %>%\n .[[1]] |> \n slice(2:n()) |> \n separate(X2, into = c(\"학습내용\", \"날짜\"), sep = \"∽̱\") |> \n separate(학습내용, into = c(\"주차\", \"학습내용\"), sep = \"\\\\.\") |> \n select(주차, 학습내용, 날짜) |> \n drop_na() |> \n mutate(날짜 = str_trim(날짜),\n 학습내용 = str_trim(학습내용)) |> \n mutate(날짜 = if_else(str_detect(날짜, \"^\\\\d\"), 날짜, NA_character_)) |> \n gt() |> \n gt_theme_pff() |> \n sub_missing(\n columns = 날짜,\n missing_text = \"-\"\n ) |> \n cols_align(\"center\") |> \n opt_table_font(\n font = list(\n google_font(name = \"Sunflower\"),\n \"NanumGothic\"\n )\n ) %>% \n tab_style(\n style = list(\n \"font-variant: small-caps;\"\n ),\n locations = cells_body(columns = everything())\n ) %>% \n tab_header(\n title =\n htmltools::tagList(\n htmltools::tags$div(\n style = htmltools::css(\n `text-align` = \"center\"\n ),\n htmltools::HTML(\n web_image(\"img/curriculum_yonsei2.png\")\n )\n ),\n htmltools::tags$div(\n htmltools::tags$strong(\"연세대\"),\n \"데이터 (사이언스) 공학\"\n )\n )\n ) |> \n tab_options(\n table.width = \"65%\",\n heading.title.font.size = px(12)\n )\n```\n\n::: {.cell-output-display}\n\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n \n \n
\n
\n 연세대\n 데이터 (사이언스) 공학\n
주차학습내용날짜
1데이터 사이언스 언어9월6일
2[언어] R9월20일
3[언어] 파이썬9월27일
4[언어] API 프로그래밍10월4일
5데이터 사이언스 자료구조10월11일
6[자료구조] 시계열 데이터10월18일
7중간고사10월25일
8[자료구조] 지리 공간 데이터11월01일
9[자료구조] NLP - 텍스트 마이닝(R)11월08일
10[자료구조] NLP - 파이썬11월15일
11빅데이터 시각화11월22일
12기계학습 - 예측모형11월29일
13DevOps - 도커, HPC, 스파크12월06일
14제품화12월13일
15기말고사12월20일
\n
\n```\n\n:::\n:::\n\n\n- [연세대 \"데이터 (사이언스) 공학\"](https://statkclee.github.io/yonsei2/)\n\n### GS 칼텍스\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n \n \n
\n
\n GS 칼텍스\n 시민 데이터 과학\n
일정과정과정상세데이터셋

1주차

\n

Introduction

\n
- Quantifying performance indicators
- Stats and Probability for Data Science\">
    \n
  • Defining and solving problems
    - Quantifying performance indicators
    - Stats and Probability for Data Science
  • \n
\n
-

2주차

\n

Data Science

\n
- Kaggle: MPG / Titanic EDA 개념과 Data science 전체흐름 습득
- EDA, Feature engineering, Visualization, Correlating 등 preprocessing 작업
- Model, predict and evaluation 의 전체 과정을 습득\">
    \n
  • Python 기초 문법 복습 및 NumPy, Pandas 등 연습
    - Kaggle: MPG / Titanic EDA 개념과 Data science 전체흐름 습득
    - EDA, Feature engineering, Visualization, Correlating 등 preprocessing 작업
    - Model, predict and evaluation 의 전체 과정을 습득
  • \n
\n
-

3주차

\n

Regression and Prediction

\n
    \n
  • Regression에 대한 개념 및 이론 설명
  • \n
\n

Breast Cancer, California housing prices, Bike-sharing

\n

3주차

\n

Regression and Prediction

\n
- Linear Regression 중심, CART, SVM, Ensemble기법 적용
- Training & Evaluation (MSE, MAE, RMSE, RMSLE, R-Squared, etc.)
- Basic hyperparameter tuning 수행\">
    \n
  • EDA 및 preprocessing
    - Linear Regression 중심, CART, SVM, Ensemble기법 적용
    - Training & Evaluation (MSE, MAE, RMSE, RMSLE, R-Squared, etc.)
    - Basic hyperparameter tuning 수행
  • \n
\n
-

4주차

\n

Classification and Hypothesis Testing

\n
    \n
  • Classification과 필요 개념 및 이론 설명
  • \n
\n

Titanic, Iris, Penuins, San Francisco Crime

\n

4주차

\n

Classification and Hypothesis Testing

\n
- Training & Evaluation / Basic hyperparameter tuning 수행\">
    \n
  • Logistic Regression과 Decision Tree, SVC, Random Forest, XGBoost, LGBM 등 적용
    - Training & Evaluation / Basic hyperparameter tuning 수행
  • \n
\n
-

5주차

\n

Case Studies and Projects

\n
- Case Study 및 Final Quiz 해설
- 개인 별 Project 인터뷰 및 Grouping / 공통과제 선정
- Project 진행, 발표, Best Project 선정 및 공유\">
    \n
  • 기존 수업 이론/실습 개념 바탕 Case Study 3종 (+Final Quiz)
    - Case Study 및 Final Quiz 해설
    - 개인 별 Project 인터뷰 및 Grouping / 공통과제 선정
    - Project 진행, 발표, Best Project 선정 및 공유
  • \n
\n
-
\n
\n```\n\n:::\n:::\n\n\n- [GS 칼텍스 데이터 사이언스](https://r2bit.com/curriculum/)\n\n\n### GS 칼텍스\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n\n\n\n \n \n \n
\n
\n GS 칼텍스\n 데이터 과학\n
일정과목명학습상세데이터셋
00주차환경설정학습 도구 설치 및 학습환경파머 펭귄, BitData
01주차디지털 글쓰기(보고서)마크다운 / QuartoBitData
02주차명령라인 데이터 분석자동화(Shell)SWC
03주차버전 제어버전제어/협업(Git / GitHub / GitLab)
04주차프로그래밍SQLData Carpentry, DVD 렌탈
05주차프로그래밍시각화gapminder
06주차프로그래밍파이썬
07주차대쉬보드Flexdashboard / Shinydashboard
08주차기계학습tidymodels / scikit-learn
09주차데이터 과학 제품RESTful API
10주차특수 데이터시계열(Time Series)KOSPI
11주차특수 데이터공간정보(Geospatial) / 텍스트 분석미디어오늘
12주차비정형 데이터텍스트와 이미지 (딥러닝)
\n
\n```\n\n:::\n:::\n\n\n- [GS 칼텍스 데이터 사이언스](https://r2bit.com/curriculum/)\n\n\n\n### 챗GPT DS {.smaller}\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nlibrary(readxl)\nlibrary(gt)\nlibrary(gtExtras)\nextrafont::loadfonts()\n\ncurriculum_raw <- read_excel(glue::glue(\"{here::here()}/data/건국대_커리큘럼.xlsx\"))\n\ncurriculum <- curriculum_raw %>% \n fill(everything(), .direction=\"down\") %>% \n mutate(날짜 = as.Date(날짜)) %>% \n mutate(주차 = glue::glue(\"{주차} 주차\"))\n\n## 첫번째\ncurriculum_first <- curriculum %>% \n filter(dplyr::row_number() <=10)\n\ncurriculum_first_gt <- curriculum_first %>% \n gt(groupname_col = c(\"주차\", \"날짜\")) %>% \n cols_align(align = \"center\") %>% \n opt_table_font(\n font = list(\n google_font(name = \"Sunflower\"),\n \"NanumGothic\"\n )\n ) %>% \n gt_theme_pff() %>% \n tab_style(\n style = list(\n \"font-variant: small-caps;\"\n ),\n locations = cells_body(columns = everything())\n ) %>% \n tab_header(\n title =\n htmltools::tagList(\n htmltools::tags$div(\n style = htmltools::css(\n `text-align` = \"center\"\n ),\n htmltools::HTML(\n web_image(\"img/konkuk/tools.png\")\n )\n ),\n htmltools::tags$div(\n htmltools::tags$strong(\"챗GPT 데이터 사이언스\"),\n \"건국대 전문교육과정\"\n )\n )\n ) \n\n## 두번째\ncurriculum_second <- curriculum %>% \n filter(dplyr::row_number() > 10)\n\ncurriculum_second_gt <- curriculum_second %>% \n gt(groupname_col = c(\"주차\", \"날짜\")) %>% \n cols_align(align = \"center\") %>% \n opt_table_font(\n font = list(\n google_font(name = \"Sunflower\"),\n \"NanumGothic\"\n )\n ) %>% \n gt_theme_pff() %>% \n tab_style(\n style = list(\n \"font-variant: small-caps;\"\n ),\n locations = cells_body(columns = everything())\n ) \n\n## 결합\n\nlisted_tables <- list(curriculum_first_gt, curriculum_second_gt)\n\ngt_two_column_layout(listed_tables)\n```\n\n::: {.cell-output-display}\n\n```{=html}\n
\n
\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n\n\n \n\n\n \n \n \n \n\n\n \n\n\n \n \n \n \n\n\n \n\n\n \n \n \n \n\n\n \n\n\n \n\n\n \n \n \n
\n
\n 챗GPT 데이터 사이언스\n 건국대 전문교육과정\n
강의주제강의내용수업방법
1 주차 - 2023-07-01
데이터 사이언스 언어챗GPT와 데이터 사이언스핸즈온 실습
데이터 사이언스 언어Tidyverse와 챗GPT핸즈온 실습
데이터 사이언스 언어파이썬/R/SQL 그리고 API핸즈온 실습
2 주차 - 2023-07-08
소프트웨어 카펜트리 (기본기)자동화: 유닉스 쉘핸즈온 실습
소프트웨어 카펜트리 (기본기)버전제어: Git / GitHub핸즈온 실습
3 주차 - 2023-07-15
소프트웨어 카펜트리 (기본기)파이썬 / R핸즈온 실습
소프트웨어 카펜트리 (기본기)데이터베이스핸즈온 실습
4 주차 - 2023-07-22
데이터 과학 자료구조숫자/범주 --> 데이터프레임핸즈온 실습
데이터 과학 자료구조날짜/공간정보/네트워크핸즈온 실습
데이터 과학 자료구조텍스트/이미지/오디오핸즈온 실습
\n
\n
\n
\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n\n\n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n\n\n \n\n\n \n \n \n \n\n\n \n\n\n \n\n\n \n \n \n \n\n\n \n\n\n \n\n\n \n \n \n
강의주제강의내용수업방법
5 주차 - 2023-07-29
디지털 글쓰기, 대쉬보드, API 개발디지털 보고서(PDF, HTML, PPT)핸즈온 실습
디지털 글쓰기, 대쉬보드, API 개발Serverless 대쉬보드, R/Python 대쉬보드핸즈온 실습
디지털 글쓰기, 대쉬보드, API 개발기계학습 모형 API 배포핸즈온 실습
- 주차 - 2023-08-05
휴가휴가-
6 주차 - 2023-08-12
챗GPT와 오픈소스(오픈소스) 거대언어모형(LLM)핸즈온 실습
챗GPT와 오픈소스Write/Draw/Coding/Reasoning핸즈온 실습
챗GPT와 오픈소스AI 시대 데이터 사이언스 제품개발 방법핸즈온 실습
7 주차 - 2023-08-19
Prompt Engineering사용자 및 개발자를 위한 프롬프트 공학핸즈온 실습
Prompt EngineeringOpenAI API 기반 NLP 개발핸즈온 실습
Prompt EngineeringOPL(OpenAI, Pinecone, Langchain) 프레임워크핸즈온 실습
8 주차 - 2023-08-26
챗GPT AI 앱 프로젝트오픈소스 LLM 기반 AI 데이터 분석 제품핸즈온 실습
챗GPT AI 앱 프로젝트데이터 사이언스 AutoGPT핸즈온 실습
챗GPT AI 앱 프로젝트챗GPT AI 프로젝트 발표핸즈온 실습
\n
\n
\n
\n```\n\n:::\n:::\n\n\n### 평가\n\n
\n\n![](img/education_evaluation.jpg)\n\n- [데이터 사이언스 MCQ](https://r2bit.com/curriculum/mcq_stat.html)\n\n:::\n\n\n", + "supporting": [], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": {}, + "engineDependencies": {}, + "preserve": {}, + "postProcess": true + } +} \ No newline at end of file diff --git a/_freeze/slideshow/202404-sch/quarto-digital-writing/execute-results/html.json b/_freeze/slideshow/202404-sch/quarto-digital-writing/execute-results/html.json new file mode 100644 index 0000000..0a572af --- /dev/null +++ b/_freeze/slideshow/202404-sch/quarto-digital-writing/execute-results/html.json @@ -0,0 +1,17 @@ +{ + "hash": "19583a26b24c5b7333125aa4b45dd37b", + "result": { + "engine": "knitr", + "markdown": "# 도구(문서)의 인간
디지털 저작\n\n
\n\n![](img/homo-faber.jpg)\n\n## 데이터 과학 과거와 현재 {.smaller}\n\n

\n\n::: columns\n::: column\n### 과거\n\n
\n\n![](slideshow/202212-youtube/fig/jobshop.gif)\n:::\n\n::: column\n### 현재\n\n
\n\n![](slideshow/202212-youtube/fig/quarto_to_go.gif)\n:::\n:::\n\n## 글쓰기 진화 {.smaller}\n\n::: panel-tabset\n### 글쓰기 역사\n\n![](img/writing_history.jpg)\n\n### 생산성과 자동화를 넘어\n\n![](img/writing-evolution.jpg)\n\n### 개발과 저작\n\n![](img/mermaid_docu_sw.jpg)\n\n\n\n\n\n\n\n\n\n\n\n package1 -->\n product1 -->\n distribute1 -->\n service1 -->\n\n\n\n\n\n\n\n\n\n\n package2 -->\n product2 -->\n distribute2 -->\n service2 -->\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n:::\n\n## 데이터 과학 → 디지털 글쓰기 {.smaller}\n\n::: panel-tabset\n### 데이터 과학\n\n![](img/horst-eco-r4ds.png)\n\n### 문서 구성요소\n\n![](img/data_science_document.jpg)\n\n### 문서 복잡성\n\n![](img/markdown-writing.png){width=\"880\"}\n\n### 문서 도구 개발 역사\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nlibrary(rvest)\nlibrary(gt)\nlibrary(gtExtras)\n\nhistory_html <- read_html('https://jjallaire.quarto.pub/notebook-publishing/#/history') \n\nhistory_url <- history_html |> \n html_element('table') |> \n html_elements('a') |> \n html_attr(\"href\")\n\nhistory_tbl <- history_html |> \n html_element('table') |> \n html_table() |> \n set_names(c(\"연도\", \"도구\", \"개발자\")) |> \n mutate(url = history_url) |> \n mutate(link = str_glue(\"{도구}\")) |> \n select(연도, link, 개발자) |> \n add_row(연도 = 2020, link = \"Quarto\",\n 개발자 = \"J.J. Allaire\")\n\nhistory_tbl |>\n write_csv(\"../../data/history_tbl.csv\")\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(tidyverse)\nlibrary(rvest)\nlibrary(gt)\nlibrary(gtExtras)\n\nhistory_tbl <- \n read_csv(glue::glue(\"{here::here()}/data/history_tbl.csv\"))\n\nhistory_tbl |> \n mutate(link = map(link, gt::html)) |> \n select(연도, 도구=link, 개발자) |> \n gt() |> \n # gt_theme_hangul() |> \n cols_align(\"center\")\n```\n\n::: {.cell-output-display}\n\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n\n\n \n \n \n
연도도구개발자
1978TeXDonald Knuth
1984Literate ProgrammingDonald Knuth
1988Mathematica NotebooksStephen Wolfram
2001IPythonFernando Perez
2003Emacs org-modeCarsten Dominik
2004MarkdownJohn Gruber
2005Sage NotebookWilliam Stein
2006PandocJohn MacFarlane
2009GitHub Flavored MarkdownTom Preston-Werner
2011iPython NotebookFernando Perez
2012knitrYihui Xie
2014Project JupyterFernando Perez
2020QuartoJ.J. Allaire
\n
\n```\n\n:::\n:::\n\n:::\n\n\n## 디지털 글쓰기 - 쿼토 {.smaller}\n\n::: panel-tabset\n### 삼권분립\n\n![](img/three-pillar.png){fig-align=\"center\" width=\"615\"}\n\n::: aside\n[챗GPT 디지털 글쓰기](https://r2bit.com/gpt-writing/)\n:::\n\n\n### 쿼토 소개\n\n
\n\n![](img/horst_quarto_schematic.png)\n\n### 쿼토 작업흐름\n\n
\n
\n\n![](img/quarto_workflow_mermaid.png)\n\n\n\n\n\n\n\n 주피터 -->\n 마크다운 -->\n 팬독 -->\n pdf -->\n html -->\n docx -->\n knitr -->\n 마크다운 -->\n\n\n### 쿼토 \n\n| 기능 | R 마크다운 | 쿼토(Quarto) |\n|:-----------------:|:-----------------------------|:-----------------------|\n| 파일형식 | [html_document](https://pkgs.rstudio.com/rmarkdown/reference/html_document.html) / [pdf_document](https://pkgs.rstudio.com/rmarkdown/reference/pdf_document.html) / [word_document](https://pkgs.rstudio.com/rmarkdown/reference/word_document.html) | [html](https://quarto.org/docs/output-formats/html-basics.html) / [pdf](https://quarto.org/docs/output-formats/pdf-basics.html) / [docx](https://quarto.org/docs/output-formats/ms-word.html) |\n| 비머(Beamer) | [beamer_presentation](https://pkgs.rstudio.com/rmarkdown/reference/beamer_presentation.html) | [beamer](https://quarto.org/docs/presentations/beamer.html) |\n| 파워포인트(PPT) | [powerpoint_presentation](https://pkgs.rstudio.com/rmarkdown/reference/powerpoint_presentation.html) | [pptx](https://quarto.org/docs/presentations/powerpoint.html) |\n| 웹 슬라이드 | [xaringan](https://bookdown.org/yihui/rmarkdown/xaringan.html) / [ioslides](https://bookdown.org/yihui/rmarkdown/ioslides-presentation.html) / [revealjs](https://bookdown.org/yihui/rmarkdown/revealjs.html) | [revealjs](https://quarto.org/docs/presentations/revealjs/) |\n| 고급 레이아웃 | [tufte](https://bookdown.org/yihui/rmarkdown/tufte-handouts.html) / [distill](https://rstudio.github.io/distill/figures.html) | [Quarto Article Layout](https://quarto.org/docs/authoring/article-layout.html) |\n| 상호 참조 | [html_document2](https://bookdown.org/yihui/bookdown/a-single-document.html) / [pdf_document2](https://bookdown.org/yihui/bookdown/a-single-document.html) / [word_document2](https://bookdown.org/yihui/bookdown/a-single-document.html) | [Quarto Crossrefs](https://quarto.org/docs/authoring/cross-references.html) |\n| 웹사이트/블로그 | [blogdown](https://pkgs.rstudio.com/blogdown/) / [distill](https://pkgs.rstudio.com/distill/) | [Quarto Websites](https://quarto.org/docs/websites/) / [Quarto Blogs](https://quarto.org/docs/websites/website-blog.html) |\n| 책 | [bookdown](https://pkgs.rstudio.com/bookdown/) | [Quarto Books](https://quarto.org/docs/books/) |\n| 인터랙티브 문서 | [Shiny Documents](https://bookdown.org/yihui/rmarkdown/shiny-documents.html) | [Quarto Interactive Documents](https://quarto.org/docs/interactive/shiny/) |\n| 페이지 HTML | [pagedown](https://github.com/rstudio/pagedown) | 출시 예정 |\n| 학술 논문 | [rticles](https://pkgs.rstudio.com/rticles/) | [Quarto Journal Articles](https://quarto.org/docs/journals/) |\n| 대쉬보드 | [flexdashboard](https://pkgs.rstudio.com/flexdashboard/) | [Quarto Dashboards](https://quarto.org/docs/dashboards/) |\n| 인터랙티브 자습서 | [learnr](https://pkgs.rstudio.com/learnr/) | 계획 없음 |\n\n: R 마크다운과 쿼토 비교 {#tbl-quarto-rmarkdown}\n\n:::\n\n", + "supporting": [ + "quarto-digital-writing_files" + ], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": {}, + "engineDependencies": {}, + "preserve": {}, + "postProcess": true + } +} \ No newline at end of file diff --git a/chatGPT_Seoul_HS_20230522.ipynb b/chatGPT_Seoul_HS_20230522.ipynb index 656d329..d8bd9d4 100644 --- a/chatGPT_Seoul_HS_20230522.ipynb +++ b/chatGPT_Seoul_HS_20230522.ipynb @@ -26,7 +26,7 @@ " chunk_output_type: console\n", "---" ], - "id": "084164a0" + "id": "23f98daa" }, { "cell_type": "markdown", @@ -699,7 +699,7 @@ "\n", "### `Sympy`" ], - "id": "268f9950" + "id": "0b46387d" }, { "cell_type": "code", @@ -712,7 +712,7 @@ "Diophantus_puzzle = x/6 + x/12 + x/7 + 5 + x/2 + 4 - x\n", "solve(Diophantus_puzzle, x, dict = True)" ], - "id": "d42f2bf4", + "id": "60edf4ca", "execution_count": null, "outputs": [] }, @@ -1361,7 +1361,7 @@ "::: {#refs}\n", ":::\n" ], - "id": "289d6623" + "id": "24ca7b5c" } ], "metadata": { diff --git a/chatgpt_sch_202404.qmd b/chatgpt_sch_202404.qmd new file mode 100644 index 0000000..28f6bda --- /dev/null +++ b/chatgpt_sch_202404.qmd @@ -0,0 +1,53 @@ +--- +title: "챗GPT 데이터 사이언스" +subtitle: "데이터 사이언스 패러다임 전환" +author: "이광춘" +institute: "한국 R 사용자회" +date: 2024-04-29 +format: + insper-revealjs: + self-contained: false + code-overflow: wrap + footer: | + 한국 R 사용자회 [chatGPT](https://r2bit.com/chatGPT/). theme [insper](https://github.com/padsInsper/quarto-insper-theme) 소스코드 [GitHub](https://github.com/padsInsper/quarto-insper-theme). +date-format: long +lang: ko +filters: + - lightbox +lightbox: auto +bibliography: bibliography.bib +csl: apa-single-spaced.csl +editor_options: + chunk_output_type: console +--- + +## 목차 + +1. 들어가며 + - 서울 R 미트업 + - 챗GPT 데이터 과학 (도서) + - 챗GPT 데이터 과학 교과과정 + +2. 챗GPT 데이터 과학 + - 사무노동 (a.k.a 오피스 전쟁) + - 챗GPT 데이터 사이언스 + - 디지털 저작 (코딩 + 글쓰기) + +3. 질의응답! + +{{< include slideshow/202404-sch/seoul-r-meeup.qmd >}} + +{{< include slideshow/202404-sch/book.qmd >}} + +{{< include slideshow/202404-sch/office-interface.qmd >}} + +{{< include slideshow/202404-sch/chatgpt-data-science.qmd >}} + +{{< include slideshow/202404-sch/quarto-digital-writing.qmd >}} + +{{< include slideshow/202404-sch/reference.qmd >}} + +{{< include slideshow/202404-sch/curriculum.qmd >}} + + + diff --git a/docs/chatgpt_sch_202404.html b/docs/chatgpt_sch_202404.html new file mode 100644 index 0000000..a9fccf1 --- /dev/null +++ b/docs/chatgpt_sch_202404.html @@ -0,0 +1,5876 @@ + + + + + + + + + + + + + + + + + + + + 발표자료 | 한국 R 사용자회 - 챗GPT 데이터 사이언스 + + + + + + + + + + + + + + + + + + + + +
+
+ +
+

챗GPT 데이터 사이언스

+

데이터 사이언스 패러다임 전환

+ +
+
+
+이광춘 +
+

+ 한국 R 사용자회 +

+
+
+ +

2024년 4월 29일

+
+
+

목차

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    +
  1. 들어가며 +
      +
    • 서울 R 미트업
    • +
    • 챗GPT 데이터 과학 (도서)
    • +
    • 챗GPT 데이터 과학 교과과정
    • +
  2. +
  3. 챗GPT 데이터 과학 +
      +
    • 사무노동 (a.k.a 오피스 전쟁)
    • +
    • 챗GPT 데이터 사이언스
    • +
    • 디지털 저작 (코딩 + 글쓰기)
    • +
  4. +
  5. 질의응답!
  6. +
+ +
+
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+

+
+

2023년 챗GPT 데이터 사이언스



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서울 R 미트업 (2023년)

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참여와 연대

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  • 서울R미트업 meetup.com: link +
      +
    • 23년 4월 서울 R 미트업: link
    • +
    • 23년 5월 서울 R 미트업: link
    • +
    • 23년 6월 서울 R 미트업: link
    • +
    • 23년 7월 서울 R 미트업: link
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    • 23년 8월 서울 R 미트업: link
    • +
    • 23년 9월 서울 R 미트업: link
    • +
  • +
  • 세계 R 미트업 현황 (Global R Meetup Dashboard): link
  • +
  • 한국 R 사용자회 (Korea R User Group): link
  • +
  • 한국 R 컨퍼런스 (Korea R Conference): link
  • +
  • 유튜브 채널 (Youtube Channel): link
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  • 페이스북 그룹 (Facebook Group): link
  • +
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2024년 인공지능 데이터 사이언스



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서울 R 미트업

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참여와 연대

+
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  • 서울R미트업 meetup.com: link +
      +
    • 23년 4월 서울 R 미트업: link
    • +
    • 23년 5월 서울 R 미트업: link
    • +
    • 23년 6월 서울 R 미트업: link
    • +
    • 23년 7월 서울 R 미트업: link
    • +
    • 23년 8월 서울 R 미트업: link
    • +
    • 23년 9월 서울 R 미트업: link
    • +
  • +
  • Resources for General Help with R - Regional R communities link
  • +
  • 세계 R 미트업 현황 (Global R Meetup Dashboard): link
  • +
  • 한국 R 사용자회 (Korea R User Group): link
  • +
  • 한국 R 컨퍼런스 (Korea R Conference): link
  • +
  • 유튜브 채널 (Youtube Channel): link
  • +
  • 페이스북 그룹 (Facebook Group): link
  • +
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챗GPT 도서출판

+ +
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챗GPT 데이터 과학 도서

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📘 교보 POD 종이책: https://bit.ly/4cVDxxL

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📗 교보 전자책: https://bit.ly/4dccQ8n

+

🌐 웹사이트: https://bit.ly/4909pOB

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💻 소스코드: https://bit.ly/3wXugES (이광춘, 2024b)

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📘 교보 POD 종이책: https://bit.ly/4a8v1JS

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📗 교보 전자책: https://bit.ly/4auasHU

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🌐 웹사이트: https://bit.ly/48V8u1T

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💻 소스코드: https://bit.ly/48RqMB9 (이광춘, 2024c)

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📘 교보 POD 종이책: https://bit.ly/3wElKuk

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📗 교보 전자책: https://bit.ly/3IkWE6f

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🌐 웹사이트: https://bit.ly/3P7PVjX

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💻 소스코드: https://bit.ly/3InK46m (이광춘, 2024d)

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📘 교보 POD 종이책: https://bit.ly/3OJmMeT

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📗 교보 전자책: https://bit.ly/48fujZD

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🌐 웹사이트: https://bit.ly/48gYn7d

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💻 소스코드: https://bit.ly/3wrgeuP (이광춘, 2024a)

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📘 교보 POD 종이책: https://bit.ly/434h5y9

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📗 교보 전자책: https://bit.ly/KzUdOU

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🌐 웹사이트: https://bit.ly/3I4FpWS

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💻 소스코드: https://bit.ly/3wrgeuP (이광춘·신종화, 2023)

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📰 뉴스기사: 챗GPT가 던진 새 가능성, 데이터 과학 분야 통해 미리 살펴보기

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준비중

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📘 교보 POD 종이책: 준비중

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📗 교보 전자책: 준비중

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🌐 웹사이트: https://r2bit.com/gpt-stat/

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💻 소스코드: https://github.com/bit2r/gpt-stat/

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글쓰기
챗GPT 인터페이스 - 오피스 전쟁

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구글 검색의 종말(?)

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글쓰기 역사

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+

+ +
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+

오피스 전쟁

+ +++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
항목MS 오피스 1구글 오피스 2리브레 오피스 3한컴 오피스 4
문서 작성 및 편집워드, 엑셀, 파워포인트 등의 데스크톱 및 웹 앱 제공구글 문서, 스프레드시트, 프레젠테이션 등의 웹 앱 제공라이터, 캘크, 임프레스 등의 데스크톱 앱 제공한글, 한셀, 한쇼 등의 데스크톱 및 모바일 앱 제공
협업 및 공유원드라이브를 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움구글 드라이브를 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정하고 실시간으로 확인 가능드롭박스나 구글 드라이브와 연동하여 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움넷프리즘을 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움
이메일 및 채팅Outlook을 통해 비즈니스 메일 제공. Teams을 통해 채팅 및 화상 회의 가능Gmail을 통해 비즈니스 메일 제공. Chat과 Meet을 통해 채팅 및 화상 회의 가능이메일 및 채팅 기능 없음이메일 및 채팅 기능 없음
가격1인당 월 5~20달러 다양한 요금제 제공. 데스크톱 앱 사용 가능한 요금제는 12.5달러부터 시작1인당 월 6~18달러까지 다양한 요금제 제공완전 무료개인용은 무료로 사용 가능하며 업무용은 별도로 견적 요청 필요
+
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오피스 전쟁: 패러다임 충돌

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챗GPT 시대 사무실

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챗GPT가 이룬 대통합

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인터페이스 (Interface)

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+ +
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+




+
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+

+
+
flowchart TB
+    
+    subgraph A["사용자 인터페이스"]
+    direction LR
+        CLI["CLI<br>Command Line Interface<br> 1950~"]     --> GUI["GUI <br> Graphic User Inferface<br> 1970~"]
+        GUI               --> WI["Web Interface <br> 1994~"]
+        WI --> MI["Mobile Interface <br> 2007~"]
+        MI --> NUI["LUI <br> Language User Interface <br> 2023~"]
+
+    end
+
+
+class A nodeStyle
+
+classDef nodeStyle fill:#fcfbfa,stroke:#000000,stroke-width:0.7px,font-weight:bold,font-size:14px;
+
+
+

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+



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챗GPT NUI

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  • 챗GPT (ChatGPT)
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  • OpenAI Playground
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사례

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Command Palette 역사

+
    +
  • 60년대로 거슬러 올라가는 명령 팔레트는 최근 기술 업계에서 다시 인기를 얻고 있으며 사용자 중심 소프트웨어의 필수적인 부분으로 간주됨.
  • +
  • CLI는 개인용 컴퓨터가 대중화되기 전 초기 컴퓨팅을 지배했으며 사용자가 시스템을 작동하기 위해 특정 명령을 알아야 했다. 1984년 Apple의 매킨토시와 함께 도입된 그래픽 사용자 인터페이스(GUI)는 중요한 혁신으로 여겨졌지만, 소프트웨어의 복잡성이 증가함에 따라 GUI만으로는 탐색하기가 점점 더 어려워졌다.
  • +
  • 사용자가 항목 이름의 짧은 약어입력하여 앱과 파일을 검색할 수 있는 런처가 점점 더 보편화 되었다. 예를 들어 2001년에 출시된 Mac OS X용 LaunchBar, Quicksilver, 2005년에 출시된 Apple의 시스템 전체 Spotlight가 있다. 검색과 CLI의 결합은 Command Palette의 탄생으로 이어졌다.
  • +
  • 오늘날 우리가 알고 있는 현대식 명령 팔레트는 2011년 Sublime Text 2 베타 Joe Skinner가 처음 소개. 명령줄 사용에 익숙한 프로그래머들이 단축키를 모두 외울 필요 없이 더 빠르게 작업할 수 있어 중요한 발전이 됨.
  • +
+
+
+
+
+
+
+
+
+

생성형 AI
데이터 사이언스

+ +
+
+

포스터 제작

+
+ +
+
+
+
+

+
+
+
+
+
+
+

+
+
+
+
+
+
+

+
+
+
+
+
+ +
+
+

API 프로그래밍

+
+ +
+
+
+

웹사이트를 읽고 다음 지령을 준비해 https://apis.map.kakao.com/web/documentation/

+
+
Kakao 지도 API 문서는 다양한 지도 관련 기능을 제공한다. 이 문서는 지도 객체 생성, 이벤트, 메서드 등에 대한 상세한 정보를 담고 있다. 아래는 주요 내용의 요약이다.
+
+### 지도 객체 생성
+- `kakao.maps.Map(container, options)`: 지도를 생성한다.
+  - `container`: 지도가 표시될 HTML element
+  - `options`: 중심 좌표, 확대 수준, 지도 종류 등을 설정할 수 있다.
+
+### 주요 메서드
+- `setCenter(latlng)`: 지도의 중심 좌표를 설정한다.
+- `getCenter()`: 지도의 중심 좌표를 반환한다.
+- `setLevel(level, options)`: 지도의 확대 수준을 설정한다.
+- `getLevel()`: 지도의 확대 수준을 반환한다.
+- `setMapTypeId(mapTypeId)`: 지도의 타입을 설정한다.
+- `getMapTypeId()`: 지도의 타입을 반환한다.
+
+### 이벤트
+- `center_changed`: 중심 좌표가 변경되면 발생한다.
+- `zoom_start`: 확대 수준이 변경되기 직전에 발생한다.
+- `zoom_changed`: 확대 수준이 변경되면 발생한다.
+- `click`: 지도를 클릭하면 발생한다.
+- `dblclick`: 지도를 더블클릭하면 발생한다.
+
+이 외에도 다양한 메서드와 이벤트가 있어, 개발자가 원하는 기능을 쉽게 구현할 수 있다.
+
+
+
+

주소를 넘기면 위경도 좌표를 반환하는 R 코드를 작성해줘.

+
+

R에서 주소를 위경도 좌표로 변환하기 위해 httr 패키지를 사용하여 Kakao API를 호출한 예시 코드이다.

+
# 필요한 패키지를 설치하고 불러온다
+install.packages("httr")
+install.packages("jsonlite")
+library(httr)
+library(jsonlite)
+
+# 주소를 위경도로 변환하는 함수를 정의한다
+get_latlng <- function(address) {
+  # Kakao API 키를 설정한다 (본인의 API 키로 교체해야 한다)
+  api_key <- "YOUR_KAKAO_API_KEY"
+  
+  # API URL과 파라미터를 설정한다
+  url <- "https://dapi.kakao.com/v2/local/search/address.json"
+  params <- list(query = address)
+  headers <- add_headers("Authorization" = paste("KakaoAK", api_key))
+  
+  # API를 호출한다
+  response <- GET(url, query = params, headers)
+  
+  # 응답을 확인하고 JSON을 파싱한다
+  if (status_code(response) == 200) {
+    content <- content(response, "text", encoding = "UTF-8")
+    json_data <- fromJSON(content)
+    
+    # 위경도를 추출한다
+    if (json_data$meta$total_count > 0) {
+      lat <- json_data$documents[[1]]$y
+      lng <- json_data$documents[[1]]$x
+      return(list(lat = lat, lng = lng))
+    } else {
+      return(NULL)
+    }
+  } else {
+    stop("API request failed")
+  }
+}
+
+# 함수를 테스트한다
+address <- "서울특별시 강남구 테헤란로 212"
+result <- get_latlng(address)
+print(paste("Latitude:", result$lat, "Longitude:", result$lng))
+
+
+
+
library(httr)
+library(jsonlite)
+
+# 주소를 위경도로 변환하는 함수를 정의한다
+get_latlng <- function(address) {
+  # Kakao API 키를 설정한다 (본인의 API 키로 교체해야 한다)
+  api_key <- Sys.getenv("KAKAO_MAP_API_KEY")
+  
+  # API URL과 파라미터를 설정한다
+  url <- "https://dapi.kakao.com/v2/local/search/address.json"
+  params <- list(query = address)
+  headers <- add_headers("Authorization" = paste("KakaoAK", api_key))
+  
+  # API를 호출한다
+  response <- GET(url, query = params, headers)
+  
+  # 응답을 확인하고 JSON을 파싱한다
+  if (status_code(response) == 200) {
+    content <- content(response, "text", encoding = "UTF-8")
+    json_data <- fromJSON(content)
+    
+    # 위경도를 추출한다
+    if (json_data$meta$total_count > 0) {
+      lat <- json_data$documents[[1]]$y
+      lng <- json_data$documents[[1]]$x
+      return(list(lat = lat, lng = lng))
+    } else {
+      return(NULL)
+    }
+  } else {
+    stop("API request failed")
+  }
+}
+
+# 가톨릭관동대학교 주소
+address <- "강원도 강릉시 범일로579번길 24"
+result <- get_latlng(address)
+print(paste("Latitude:", result$lat, "Longitude:", result$lng))
+
+
+
+
+
+

+
+
+
+
+
+ +
+
+

사실확인 (Wolfram)

+
+ +
+
+

울프람 알파(Wolfram Alpha) 사용해서 사실관계를 확인해보자.

+
+

“지구는 평평하다”라는 주장을 검증해줘.

+
+
"지구는 평평하다"라는 주장에 대해 Wolfram Alpha를 이용해 검증해보았습니다.
+
+### 입력 해석
+- 질문: 지구는 평평하다?
+
+### 결과
+- 답변: 아니다.
+  - 지역적으로만 그렇다. 그리고 그것도 특정 지역, 예를 들어 미국 중서부에서만 해당된다.
+
+자세한 정보는 [Wolfram|Alpha 웹사이트](https://www6b3.wolframalpha.com/input?i=Is+the+Earth+flat%3F)에서 확인할 수 있다.
+
+이처럼 Wolfram Alpha는 다양한 주장이나 정보에 대한 팩트체크를 할 수 있는 유용한 도구이다.
+ +
+
+
+

라면을 많이 먹으면 건강에 정말 안좋은가?

+
+

Wolfram Alpha에서는 “라면을 많이 먹으면 건강에 정말 안 좋은가?”에 대한 구체적인 답변을 제공하지 못했다. 그러나 일반적으로 라면은 높은 나트륨, 지방, 인공 첨가물 등을 포함하고 있어, 과도하게 섭취할 경우 다음과 같은 건강 문제가 발생할 수 있다:

+
    +
  1. 고혈압: 라면에는 나트륨이 많이 들어있다. 나트륨 과다 섭취는 혈압을 높일 수 있다.

  2. +
  3. 비만: 라면은 칼로리가 높고, 지방도 많이 들어있다. 지속적으로 라면을 많이 먹으면 체중 증가의 위험이 있다.

  4. +
  5. 소화기 문제: 라면은 소화가 잘 되지 않는 식품 중 하나이다. 과도한 섭취는 소화기에 부담을 줄 수 있다.

  6. +
  7. 영양 불균형: 라면은 영양소가 적고, 나트륨이나 지방 등의 불필요한 성분이 많다. 이로 인해 영양 불균형이 발생할 수 있다.

  8. +
  9. 심혈관 질환: 고혈압, 높은 콜레스테롤 등은 심혈관 질환의 위험을 높인다.

  10. +
+

라면을 즐길 때는 이러한 점들을 고려하여 적당한 양과 빈도로 섭취하는 것이 중요하다.

+
+
+
+
+
+

수능문제 풀이

+
+ +
+
+

2023학년도 대학수학능력시험 수학영역 문제를 풀어보자.

+

+
+
+
+

\(\left(\frac{4}{2^{\sqrt{2}}}\right)^{2+\sqrt{2}}\) 값은?

+
+

\[ +\left(\frac{4}{2^{\sqrt{2}}}\right)^{2+\sqrt{2}} +\] 의 값은 \(4\)입니다.

+

정확한 형태로는 \(2^{(2 - \sqrt{2}) (2 + \sqrt{2})}\)로 표현됩니다.

+

자세한 정보는 Wolfram|Alpha 웹사이트에서 확인할 수 있다.

+
+
+
+

과정도 설명해줘.

+
+
+
+

물론이다. 주어진 식은 \[ +\left(\frac{4}{2^{\sqrt{2}}}\right)^{2+\sqrt{2}} +\]이다. 이 식을 단계별로 분해해보자.

+
    +
  • 단계 1: 분모를 간단하게 만들기
    먼저, 분모인 \(2^{\sqrt{2}}\)를 간단하게 만들기 위해 분자와 분모에 \(2^{\sqrt{2}}\)를 곱한다. 이렇게 하면:
  • +
+

\[ +\left(\frac{4 \times 2^{\sqrt{2}}}{2^{\sqrt{2}} \times 2^{\sqrt{2}}}\right)^{2+\sqrt{2}} +\]

+
    +
  • 단계 2: 분모를 합치기
    이제 분모의 \(2^{\sqrt{2}} \times 2^{\sqrt{2}}\)를 합쳐 \(2^{2\sqrt{2}}\)로 만든다.
  • +
+

\[ +\left(\frac{4 \times 2^{\sqrt{2}}}{2^{2\sqrt{2}}}\right)^{2+\sqrt{2}} +\]

+
+
    +
  • 단계 3: 분자를 합치기
    분자의 \(4 \times 2^{\sqrt{2}}\)를 합쳐 \(2^2 \times 2^{\sqrt{2}} = 2^{2+\sqrt{2}}\)로 만든다.
  • +
+

\[ +\left(\frac{2^{2+\sqrt{2}}}{2^{2\sqrt{2}}}\right)^{2+\sqrt{2}} +\]

+
    +
  • 단계 4: 지수 법칙 적용
    지수 법칙에 따라 분자와 분모의 지수를 빼준다.
  • +
+

\[ +\left(2^{(2+\sqrt{2}) - 2\sqrt{2}}\right)^{2+\sqrt{2}} = \left(2^{(2 - \sqrt{2})(2 + \sqrt{2})}\right)^{2+\sqrt{2}} +\]

+
    +
  • 단계 5: 최종 계산
    마지막으로, \((2 - \sqrt{2})(2 + \sqrt{2})\)를 계산하면 \(4 - 2 = 2\)가 된다. 따라서 최종 결과는 \(2^2 = 4\)이다.
  • +
+

이렇게 해서 \[ +\left(\frac{4}{2^{\sqrt{2}}}\right)^{2+\sqrt{2}} = 4 +\]임을 확인할 수 있다.

+
+
+
+
+
+ +
+
+

OpenAI Advanced Data Analysis

+


+
+

Open AI Code Interpreter → Advanced Data Analysis → 챗GPT4

+
+


+

+ +
+
+

RStudio Copilot

+
+

Tools -> Global Options -> Copilot -> Enable Github Copilot

+
+

+
+
+

웹앱(Shiny App) 개발 사례

+

+
+
+

광명시 보좌관

+
+
+

+
+
+ +
+
+

RTutor & PandasAI

+
+
+

+
+
+ +
+
+
+

도구(문서)의 인간
디지털 저작

+


+

+
+
+

데이터 과학 과거와 현재

+



+
+
+

과거

+


+

+
+

현재

+


+

+
+
+
+
+

글쓰기 진화

+
+ +
+
+

+
+
+

+
+
+

+ + + + + + + + + +
        <!-- sourcecode --> package1 -->
+        <!-- package1 --> product1 -->
+        <!-- product1 --> distribute1 -->
+        <!-- distribute1 --> service1 -->
+ + + + + + + + + +

package2 –> product2 –> distribute2 –> service2 –>

+ + + + + + + + + + + + + +
+
+
+
+
+

데이터 과학 → 디지털 글쓰기

+
+ +
+
+

+
+
+

+
+
+

+
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
연도도구개발자
1978TeXDonald Knuth
1984Literate ProgrammingDonald Knuth
1988Mathematica NotebooksStephen Wolfram
2001IPythonFernando Perez
2003Emacs org-modeCarsten Dominik
2004MarkdownJohn Gruber
2005Sage NotebookWilliam Stein
2006PandocJohn MacFarlane
2009GitHub Flavored MarkdownTom Preston-Werner
2011iPython NotebookFernando Perez
2012knitrYihui Xie
2014Project JupyterFernando Perez
2020QuartoJ.J. Allaire
+ +
+
+
+
+
+
+
+
+
+

디지털 글쓰기 - 쿼토

+
+ +
+
+
+
+

+
+
+ +
+
+


+

+
+
+



+

+ + + + + + +

주피터 –> 마크다운 –> 팬독 –> pdf –> html –> docx –> knitr –> 마크다운 –>

+
+
+
+
+
+표 1: R 마크다운과 쿼토 비교 +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
기능R 마크다운쿼토(Quarto)
파일형식html_document / pdf_document / word_documenthtml / pdf / docx
비머(Beamer)beamer_presentationbeamer
파워포인트(PPT)powerpoint_presentationpptx
웹 슬라이드xaringan / ioslides / revealjsrevealjs
고급 레이아웃tufte / distillQuarto Article Layout
상호 참조html_document2 / pdf_document2 / word_document2Quarto Crossrefs
웹사이트/블로그blogdown / distillQuarto Websites / Quarto Blogs
bookdownQuarto Books
인터랙티브 문서Shiny DocumentsQuarto Interactive Documents
페이지 HTMLpagedown출시 예정
학술 논문rticlesQuarto Journal Articles
대쉬보드flexdashboardQuarto Dashboards
인터랙티브 자습서learnr계획 없음
+
+
+
+
+
+
+
+
+

참고문헌

+
+
+이광춘. (2024a). 챗GPT SQL. 한국 R 사용자회. +
+
+이광춘. (2024b). 챗GPT 데이터 과학. 한국 R 사용자회. +
+
+이광춘. (2024c). 챗GPT 코딩. 한국 R 사용자회. +
+
+이광춘. (2024d). 챗GPT 쿼토. 한국 R 사용자회. +
+
+이광춘·신종화. (2023). 챗GPT 유닉스 쉘: 데이터 과학 자동화 도구. 한국 R 사용자회. +
+
+
+ +
+
+

통계 → 데이터 사이언스 → 챗GPT 인공지능

+ +
+
+

교육과정

+
+ +
+
+
+
+

+
+
+
+
+
+
+

+
+
+
+
+
+
+
+
+ + + +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+연세대 데이터 사이언스 입문 +
주차학습내용날짜
1데이터 과학 (Data Science)3월7일
2프로그래밍 기초와 마크다운 보고서3월14일
3데이터와 커뮤니티 찾기, 도움 청하기, 프로젝트 설정3월21일
4데이터 수집-
5자료구조(Data Structures)4월4일
6데이터프레임과 SQL4월11일
7버전제어, 협업, 그리고 저작권과 라이선스4월18일
8중간고사4월25일
9시각화 (Visualization)5월2일
10정규표현식5월9일
11데이터 과학 프로그래밍5월16일
12데이터 과학 제품-
13함수형 프로그래밍5월30일
14데이터 과학 저작6월6일(현충일)
15R 팩키지6월13일
16기말고사6월20일
+ +
+
+
+
+ +
+
+
+
+
+
+ + + +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+연세대 데이터 (사이언스) 공학 +
주차학습내용날짜
1데이터 사이언스 언어9월6일
2[언어] R9월20일
3[언어] 파이썬9월27일
4[언어] API 프로그래밍10월4일
5데이터 사이언스 자료구조10월11일
6[자료구조] 시계열 데이터10월18일
7중간고사10월25일
8[자료구조] 지리 공간 데이터11월01일
9[자료구조] NLP - 텍스트 마이닝(R)11월08일
10[자료구조] NLP - 파이썬11월15일
11빅데이터 시각화11월22일
12기계학습 - 예측모형11월29일
13DevOps - 도커, HPC, 스파크12월06일
14제품화12월13일
15기말고사12월20일
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+GS 칼텍스 시민 데이터 과학 +
일정과정과정상세데이터셋

1주차

Introduction

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  • Defining and solving problems
    - Quantifying performance indicators
    - Stats and Probability for Data Science
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2주차

Data Science

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  • Python 기초 문법 복습 및 NumPy, Pandas 등 연습
    - Kaggle: MPG / Titanic EDA 개념과 Data science 전체흐름 습득
    - EDA, Feature engineering, Visualization, Correlating 등 preprocessing 작업
    - Model, predict and evaluation 의 전체 과정을 습득
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3주차

Regression and Prediction

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  • Regression에 대한 개념 및 이론 설명
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Breast Cancer, California housing prices, Bike-sharing

3주차

Regression and Prediction

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  • EDA 및 preprocessing
    - Linear Regression 중심, CART, SVM, Ensemble기법 적용
    - Training & Evaluation (MSE, MAE, RMSE, RMSLE, R-Squared, etc.)
    - Basic hyperparameter tuning 수행
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4주차

Classification and Hypothesis Testing

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  • Classification과 필요 개념 및 이론 설명
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Titanic, Iris, Penuins, San Francisco Crime

4주차

Classification and Hypothesis Testing

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  • Logistic Regression과 Decision Tree, SVC, Random Forest, XGBoost, LGBM 등 적용
    - Training & Evaluation / Basic hyperparameter tuning 수행
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5주차

Case Studies and Projects

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  • 기존 수업 이론/실습 개념 바탕 Case Study 3종 (+Final Quiz)
    - Case Study 및 Final Quiz 해설
    - 개인 별 Project 인터뷰 및 Grouping / 공통과제 선정
    - Project 진행, 발표, Best Project 선정 및 공유
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+GS 칼텍스 데이터 과학 +
일정과목명학습상세데이터셋
00주차환경설정학습 도구 설치 및 학습환경파머 펭귄, BitData
01주차디지털 글쓰기(보고서)마크다운 / QuartoBitData
02주차명령라인 데이터 분석자동화(Shell)SWC
03주차버전 제어버전제어/협업(Git / GitHub / GitLab)
04주차프로그래밍SQLData Carpentry, DVD 렌탈
05주차프로그래밍시각화gapminder
06주차프로그래밍파이썬
07주차대쉬보드Flexdashboard / Shinydashboard
08주차기계학습tidymodels / scikit-learn
09주차데이터 과학 제품RESTful API
10주차특수 데이터시계열(Time Series)KOSPI
11주차특수 데이터공간정보(Geospatial) / 텍스트 분석미디어오늘
12주차비정형 데이터텍스트와 이미지 (딥러닝)
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+챗GPT 데이터 사이언스 건국대 전문교육과정 +
강의주제강의내용수업방법
1 주차 - 2023-07-01
데이터 사이언스 언어챗GPT와 데이터 사이언스핸즈온 실습
데이터 사이언스 언어Tidyverse와 챗GPT핸즈온 실습
데이터 사이언스 언어파이썬/R/SQL 그리고 API핸즈온 실습
2 주차 - 2023-07-08
소프트웨어 카펜트리 (기본기)자동화: 유닉스 쉘핸즈온 실습
소프트웨어 카펜트리 (기본기)버전제어: Git / GitHub핸즈온 실습
3 주차 - 2023-07-15
소프트웨어 카펜트리 (기본기)파이썬 / R핸즈온 실습
소프트웨어 카펜트리 (기본기)데이터베이스핸즈온 실습
4 주차 - 2023-07-22
데이터 과학 자료구조숫자/범주 --> 데이터프레임핸즈온 실습
데이터 과학 자료구조날짜/공간정보/네트워크핸즈온 실습
데이터 과학 자료구조텍스트/이미지/오디오핸즈온 실습
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강의주제강의내용수업방법
5 주차 - 2023-07-29
디지털 글쓰기, 대쉬보드, API 개발디지털 보고서(PDF, HTML, PPT)핸즈온 실습
디지털 글쓰기, 대쉬보드, API 개발Serverless 대쉬보드, R/Python 대쉬보드핸즈온 실습
디지털 글쓰기, 대쉬보드, API 개발기계학습 모형 API 배포핸즈온 실습
- 주차 - 2023-08-05
휴가휴가-
6 주차 - 2023-08-12
챗GPT와 오픈소스(오픈소스) 거대언어모형(LLM)핸즈온 실습
챗GPT와 오픈소스Write/Draw/Coding/Reasoning핸즈온 실습
챗GPT와 오픈소스AI 시대 데이터 사이언스 제품개발 방법핸즈온 실습
7 주차 - 2023-08-19
Prompt Engineering사용자 및 개발자를 위한 프롬프트 공학핸즈온 실습
Prompt EngineeringOpenAI API 기반 NLP 개발핸즈온 실습
Prompt EngineeringOPL(OpenAI, Pinecone, Langchain) 프레임워크핸즈온 실습
8 주차 - 2023-08-26
챗GPT AI 앱 프로젝트오픈소스 LLM 기반 AI 데이터 분석 제품핸즈온 실습
챗GPT AI 앱 프로젝트데이터 사이언스 AutoGPT핸즈온 실습
챗GPT AI 앱 프로젝트챗GPT AI 프로젝트 발표핸즈온 실습
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    diff --git a/docs/listings.json b/docs/listings.json index 77da79d..0f907bc 100644 --- a/docs/listings.json +++ b/docs/listings.json @@ -3,6 +3,7 @@ "listing": "/index.html", "items": [ "dashboard_202404.html", + "chatgpt_sch_202404.html", "sejong_hs_202401.html", "sejong_202311.html", "nice_202310.html", diff --git a/docs/search.json b/docs/search.json index 37609f1..386d3db 100644 --- a/docs/search.json +++ b/docs/search.json @@ -4,7 +4,7 @@ "href": "index.html", "title": "발표자료", "section": "", - "text": "통계(데이터 과학), 기계학습, 딥러닝 관련 발표자료와 앱 공개를 통해 통계 대중화와 디지털 불평등 해소를 위해 함께 노력하고 있습니다.\n\n\n한국 R 사용자회\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n차세대 대쉬보드\n\n\nflexdashboard → 쿼토 대시보드\n\n\n\n서울 R 미트업\n\n\n챗GPT\n\n\n인공지능\n\n\nR마크다운\n\n\n대시보드\n\n\n데이터 과학\n\n\nflexdashboard\n\n\n쿼토 대시보드\n\n\n한국 R 사용자회\n\n\n쿼토(Quarto)\n\n\n선거\n\n\n시각화\n\n\nR/Tidyverse\n\n\n\n\n2024 년 04 월 30일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 디지털 글쓰기 with R\n\n\n과학기술 증거기반 글쓰기\n\n\n\n세종과학예술영재학교\n\n\n챗GPT\n\n\n디지털 글쓰기\n\n\n데이터 과학\n\n\n생성형 AI\n\n\nAdvanced Data Analysis\n\n\n한국 R 사용자회\n\n\n쿼토(Quarto)\n\n\nCode Interpreter\n\n\n통계\n\n\nR/Tidyverse\n\n\n\n\n2024 년 01 월 23일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 데이터 사이언스\n\n\n행정과 규제를 넘어서\n\n\n\n행정\n\n\n세종대\n\n\n대학교육\n\n\n디지털 글쓰기\n\n\n데이터 사이언스\n\n\n생성형 AI\n\n\nAdvanced Data Analysis\n\n\n한국 R 사용자회\n\n\n챗GPT\n\n\nCode Interpreter\n\n\n통계\n\n\n수능\n\n\n일자리\n\n\n\n\n2023 년 11 월 20일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 데이터 사이언스\n\n\n전기차 충전소\n\n\n\n지도\n\n\n나이스디앤알\n\n\n전기충전소\n\n\n디지털 글쓰기\n\n\n데이터 사이언스\n\n\n생성형 AI\n\n\nAdvanced Data Analysis\n\n\n한국 R 사용자회\n\n\n챗GPT\n\n\nCode Interpreter\n\n\n\n\n2023 년 10 월 27일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 데이터 사이언스\n\n\n표준현장실습학기제\n\n\n\n행정\n\n\n세종대\n\n\n표준현장실습학기제\n\n\n디지털 글쓰기\n\n\n데이터 사이언스\n\n\n생성형 AI\n\n\nAdvanced Data Analysis\n\n\n한국 R 사용자회\n\n\n챗GPT\n\n\nCode Interpreter\n\n\n통계\n\n\n\n\n2023 년 10 월 24일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n생성형 AI 데이터 사이언스\n\n\n경제적/교육적 함의\n\n\n\n공공 데이터\n\n\n오픈 데이터 포럼\n\n\n데이터 사이언스\n\n\n생성형 AI\n\n\nAdvanced Data Analysis\n\n\n한국 R 사용자회\n\n\n챗GPT\n\n\nCode Interpreter\n\n\n\n\n2023 년 09 월 20일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n대학교육에서 챗GPT 활용방안\n\n\n교안과 평가를 중심으로\n\n\n\n교안\n\n\n평가\n\n\n대학교육\n\n\n데이터 사이언스\n\n\n생성형 AI\n\n\nAdvanced Data Analysis\n\n\n한국 R 사용자회\n\n\n챗GPT\n\n\nCode Interpreter\n\n\n통계\n\n\n수능\n\n\n일자리\n\n\n\n\n2023 년 09 월 19일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n공공 빅데이터 분석활용\n\n\n방위사업청\n\n\n\n공공 데이터\n\n\n통계\n\n\n데이터 문해력\n\n\n커뮤니케이션\n\n\n데이터 과학 사례\n\n\n한국 R 사용자회\n\n\n챗GPT\n\n\nCode Interpreter\n\n\n\n\n2023 년 09 월 18일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT (데이터 과학) 디지털 글쓰기\n\n\n부산광역시\n\n\n\n챗GPT\n\n\n사람과 기계 업무분업\n\n\n디지털 글쓰기\n\n\n데이터 사이언스\n\n\n한국 R 사용자회\n\n\n쿼토(Quarto)\n\n\n문서화 프로그래밍\n\n\n부산광역시\n\n\n디지털 전환\n\n\n\n\n2023 년 09 월 18일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 데이터 사이언스 문서화 프로그래밍\n\n\n건국대 미래지식교육원 전문교육과정\n\n\n\n챗GPT\n\n\n데이터 사이언스\n\n\n소프트웨어 카펜트리\n\n\n글로벌 수료증\n\n\n디지털 전환\n\n\nText-to-X\n\n\n건국대\n\n\n미래지식교육원\n\n\n전문교육과정\n\n\n한국 R 사용자회\n\n\nAI 앱\n\n\nR/Python/SQL\n\n\n프롬프트 엔지니어링\n\n\n\n\n2023 년 09 월 01일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT\n\n\n광명시\n\n\n\n챗GPT\n\n\n데이터 사이언스\n\n\n디지털 전환\n\n\nText-to-X\n\n\n광명시\n\n\n한국 R 사용자회\n\n\n챗GPT + 행정\n\n\n프롬프트 엔지니어링\n\n\n\n\n2023 년 08 월 11일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n후쿠시마 오염수 방류\n\n\n경기도 해안선 인접 구시군 영향\n\n\n\n후쿠시마 원전사고\n\n\n오염수 방류\n\n\n경기도 해안선\n\n\n국가/지방 항구\n\n\n평택, 화성, 안산, 김포\n\n\n\n\n2023 년 07 월 11일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n드림톡 콘서트 - 과학 행사\n\n\n오픈 소스 소프트웨어(코딩)\n\n\n\n오픈소스 소프트웨어\n\n\n디지털 글쓰기\n\n\n한국 R 사용자회\n\n\n오픈 통계 패키지\n\n\n쿼토(Quarto)\n\n\n하드웨어/소프트웨어\n\n\n챗GPT 사용자 인터페이스\n\n\n다양한 소프트웨어 개발방법\n\n\n계산기 프로그램\n\n\n문서화 프로그래밍 커뮤니티\n\n\n챗GPT 디지털 글쓰기 출판\n\n\n\n\n2023 년 07 월 10일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT: 사람과 AI 분업\n\n\n순천향대학교\n\n\n\n챗GPT\n\n\n디지털 글쓰기\n\n\nText-to-X\n\n\n사람과 기계 업무분업\n\n\n데이터 사이언스\n\n\n한국 R 사용자회\n\n\n쿼토(Quarto)\n\n\n문서화 프로그래밍\n\n\n순천향대학교\n\n\n디지털 전환\n\n\n\n\n2023 년 07 월 06일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n공공데이터 이해 및 데이터 활용역량\n\n\n챗GPT와 데이터 사이언스\n\n\n\n4차 산업혁명\n\n\n인공지능 (AI)\n\n\n한국 R 사용자회\n\n\n공공데이터\n\n\n광명시\n\n\n빅데이터\n\n\nText-to-X\n\n\n데이터 역량강화\n\n\n데이터 과학\n\n\n사무 자동화/지능화\n\n\n\n\n2023 년 07 월 04일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT와 데이터 사이언스\n\n\n건국대 미래지식교육원 전문교육과정\n\n\n\n챗GPT\n\n\n데이터 사이언스\n\n\n소프트웨어 카펜트리\n\n\n글로벌 수료증\n\n\n디지털 전환\n\n\nText-to-X\n\n\n건국대\n\n\n미래지식교육원\n\n\n전문교육과정\n\n\n한국 R 사용자회\n\n\nAI 앱\n\n\nR/Python/SQL\n\n\n프롬프트 엔지니어링\n\n\n\n\n2023 년 07 월 01일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 디지털 글쓰기 출판\n\n\n서울 교육청\n\n\n\n챗GPT\n\n\n디지털 글쓰기\n\n\n출판\n\n\n한국 R 사용자회\n\n\n쿼토(Quarto)\n\n\n문서화 프로그래밍\n\n\n서울교육청\n\n\nbitPublish\n\n\n\n\n2023 년 06 월 27일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 디지털 글쓰기 출판\n\n\n한국 R 사용자회 워크샵\n\n\n\n챗GPT\n\n\n디지털 글쓰기\n\n\n출판\n\n\n한국 R 사용자회\n\n\n쿼토(Quarto)\n\n\n문서화 프로그래밍\n\n\nR/Python/SQL/LaTeX\n\n\n프롬프트 엔지니어링\n\n\n\n\n2023 년 06 월 24일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT\n\n\n챗GPT 4차 산업혁명\n\n\n\n4차 산업혁명\n\n\n인공지능 (AI)\n\n\n한국 R 사용자회\n\n\n경기도\n\n\n인재개발원 신규자\n\n\nText-to-X\n\n\n프롬프트 엔지니어링\n\n\n챗GPT 활용\n\n\n사무 자동화/지능화\n\n\n\n\n2023 년 06 월 19일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT\n\n\n챗GPT 실무교육\n\n\n\n인공지능\n\n\nchatGPT 활용\n\n\n프롬프트 엔지니어링\n\n\n한국 R 사용자회\n\n\n안양시\n\n\n안양시 정보통신과\n\n\n엑셀\n\n\nText-to-Image\n\n\nDall-E\n\n\n\n\n2023 년 05 월 30일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT\n\n\n챗GPT 디지털 글쓰기\n\n\n\n인공지능\n\n\nchatGPT 활용\n\n\n디지털 글쓰기\n\n\n한국 R 사용자회\n\n\n경기도\n\n\n경기도 평행학습 포털\n\n\nGSEEK\n\n\n쿼토(Quarto)\n\n\n\n\n2023 년 05 월 30일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT\n\n\nAi 시대의 챗GPT 활용과 미래산업환경\n\n\n\n인공지능\n\n\nchatGPT 활용\n\n\n한국 R 사용자회\n\n\nGPT-3/3.5/4\n\n\n서울고등학교\n\n\n디지털 글쓰기\n\n\n미래산업환경\n\n\n\n\n2023 년 05 월 22일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT\n\n\n챗GPT 최신 동향\n\n\n\nchatGPT\n\n\n한국 R 사용자회\n\n\nGPT-3/3.5/4\n\n\n오픈소스 LLM\n\n\n디지털 글쓰기\n\n\n인공지능\n\n\n교보그룹\n\n\n디플래닉스\n\n\n\n\n2023 년 05 월 18일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n서울 R 미트업(23년5월)\n\n\n대통령 취임사 임베딩\n\n\n\nchatGPT\n\n\nText Embedding\n\n\n대통령 취임사\n\n\n한국 R 사용자회\n\n\n서울 R 미트업\n\n\n쿼토(Quarto)\n\n\n\n\n2023 년 05 월 11일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT\n\n\n챗GPT 최신 동향\n\n\n\nchatGPT\n\n\n인공지능\n\n\n한국 R 사용자회\n\n\nGPT-3/3.5/4\n\n\n오픈소스 SW\n\n\n\n\n2023 년 04 월 26일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n서울 R 미트업(23년4월)\n\n\n챗GPT와 디지털 글쓰기\n\n\n\nchatGPT\n\n\n디지털 글쓰기\n\n\n한국 R 사용자회\n\n\n서울 R 미트업\n\n\n쿼토(Quarto)\n\n\n\n\n2023 년 04 월 13일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nchatGPT\n\n\n진정한 기계와 동행 시작\n\n\n\nchatGPT\n\n\n기본기\n\n\n광명시\n\n\n한국 R 사용자회\n\n\n평생학습\n\n\n\n\n2023 년 03 월 24일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT와 지방행정\n\n\n챗GPT 등 인공지능 트렌드 관련 발표 및 지방행정 응용 가능성 논의\n\n\n\nchatGPT\n\n\n지방행정연구원\n\n\n인공지능\n\n\n한국 R 사용자회\n\n\n챗GPT연구회\n\n\nKRILA\n\n\n\n\n2023 년 03 월 20일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n데이터 문해력과 디지털 글쓰기\n\n\n인공지능을 지탱하는 기본기\n\n\n\n디지털 글쓰기\n\n\n데이터 문해력\n\n\n백암중\n\n\n과학창의재단\n\n\n드림톡\n\n\n\n\n2022 년 12 월 05일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n데이터 문해력과 디지털 글쓰기\n\n\n빛에듀(Bit-Edu)\n\n\n\n디지털 글쓰기\n\n\n마크다운\n\n\n빛에듀\n\n\nBit-Edu\n\n\n워드프로세서\n\n\n\n\n2022 년 12 월 02일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n로우코드 Shiny 앱 개발\n\n\nShiny 앱 개발은 로우코드 개발(?)\n\n\n\nquarto\n\n\nlow code platform\n\n\nshiny\n\n\n\n\n2022 년 11 월 05일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n데이터 사이언스에서 디자인과 아키텍쳐의 중요성\n\n\nEmerging Creativity Workshop\n\n\n\nrevealjs\n\n\n\n\n2022 년 10 월 11일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n기후 위기: CO2\n\n\nNOAA 하와이섬에서 측정한 CO2 추세를 면밀히 살펴보자.\n\n\n\nobservable js\n\n\n\n\n2022 년 09 월 12일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n파머 펭귄\n\n\n파머 펭귄 데이터로 Observables JS 인터랙티브 문서의 가능성과 한계\n\n\n\nobservable js\n\n\n\n\n2022 년 09 월 11일\n\n\n\n\n\n\n\n\n일치 없음" + "text": "통계(데이터 과학), 기계학습, 딥러닝 관련 발표자료와 앱 공개를 통해 통계 대중화와 디지털 불평등 해소를 위해 함께 노력하고 있습니다.\n\n\n한국 R 사용자회\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n차세대 대쉬보드\n\n\nflexdashboard → 쿼토 대시보드\n\n\n\n서울 R 미트업\n\n\n챗GPT\n\n\n인공지능\n\n\nR마크다운\n\n\n대시보드\n\n\n데이터 과학\n\n\nflexdashboard\n\n\n쿼토 대시보드\n\n\n한국 R 사용자회\n\n\n쿼토(Quarto)\n\n\n선거\n\n\n시각화\n\n\nR/Tidyverse\n\n\n\n\n2024 년 04 월 30일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 데이터 사이언스\n\n\n데이터 사이언스 패러다임 전환\n\n\n\n챗GPT\n\n\n인공지능\n\n\nR마크다운\n\n\n서울 R 미트업\n\n\n패러다임 전환\n\n\n데이터 과학\n\n\n한국 R 사용자회\n\n\n쿼토(Quarto)\n\n\nR/Tidyverse\n\n\nLLM\n\n\n\n\n2024 년 04 월 29일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 디지털 글쓰기 with R\n\n\n과학기술 증거기반 글쓰기\n\n\n\n세종과학예술영재학교\n\n\n챗GPT\n\n\n디지털 글쓰기\n\n\n데이터 과학\n\n\n생성형 AI\n\n\nAdvanced Data Analysis\n\n\n한국 R 사용자회\n\n\n쿼토(Quarto)\n\n\nCode Interpreter\n\n\n통계\n\n\nR/Tidyverse\n\n\n\n\n2024 년 01 월 23일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 데이터 사이언스\n\n\n행정과 규제를 넘어서\n\n\n\n행정\n\n\n세종대\n\n\n대학교육\n\n\n디지털 글쓰기\n\n\n데이터 사이언스\n\n\n생성형 AI\n\n\nAdvanced Data Analysis\n\n\n한국 R 사용자회\n\n\n챗GPT\n\n\nCode Interpreter\n\n\n통계\n\n\n수능\n\n\n일자리\n\n\n\n\n2023 년 11 월 20일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 데이터 사이언스\n\n\n전기차 충전소\n\n\n\n지도\n\n\n나이스디앤알\n\n\n전기충전소\n\n\n디지털 글쓰기\n\n\n데이터 사이언스\n\n\n생성형 AI\n\n\nAdvanced Data Analysis\n\n\n한국 R 사용자회\n\n\n챗GPT\n\n\nCode Interpreter\n\n\n\n\n2023 년 10 월 27일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 데이터 사이언스\n\n\n표준현장실습학기제\n\n\n\n행정\n\n\n세종대\n\n\n표준현장실습학기제\n\n\n디지털 글쓰기\n\n\n데이터 사이언스\n\n\n생성형 AI\n\n\nAdvanced Data Analysis\n\n\n한국 R 사용자회\n\n\n챗GPT\n\n\nCode Interpreter\n\n\n통계\n\n\n\n\n2023 년 10 월 24일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n생성형 AI 데이터 사이언스\n\n\n경제적/교육적 함의\n\n\n\n공공 데이터\n\n\n오픈 데이터 포럼\n\n\n데이터 사이언스\n\n\n생성형 AI\n\n\nAdvanced Data Analysis\n\n\n한국 R 사용자회\n\n\n챗GPT\n\n\nCode Interpreter\n\n\n\n\n2023 년 09 월 20일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n대학교육에서 챗GPT 활용방안\n\n\n교안과 평가를 중심으로\n\n\n\n교안\n\n\n평가\n\n\n대학교육\n\n\n데이터 사이언스\n\n\n생성형 AI\n\n\nAdvanced Data Analysis\n\n\n한국 R 사용자회\n\n\n챗GPT\n\n\nCode Interpreter\n\n\n통계\n\n\n수능\n\n\n일자리\n\n\n\n\n2023 년 09 월 19일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n공공 빅데이터 분석활용\n\n\n방위사업청\n\n\n\n공공 데이터\n\n\n통계\n\n\n데이터 문해력\n\n\n커뮤니케이션\n\n\n데이터 과학 사례\n\n\n한국 R 사용자회\n\n\n챗GPT\n\n\nCode Interpreter\n\n\n\n\n2023 년 09 월 18일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT (데이터 과학) 디지털 글쓰기\n\n\n부산광역시\n\n\n\n챗GPT\n\n\n사람과 기계 업무분업\n\n\n디지털 글쓰기\n\n\n데이터 사이언스\n\n\n한국 R 사용자회\n\n\n쿼토(Quarto)\n\n\n문서화 프로그래밍\n\n\n부산광역시\n\n\n디지털 전환\n\n\n\n\n2023 년 09 월 18일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 데이터 사이언스 문서화 프로그래밍\n\n\n건국대 미래지식교육원 전문교육과정\n\n\n\n챗GPT\n\n\n데이터 사이언스\n\n\n소프트웨어 카펜트리\n\n\n글로벌 수료증\n\n\n디지털 전환\n\n\nText-to-X\n\n\n건국대\n\n\n미래지식교육원\n\n\n전문교육과정\n\n\n한국 R 사용자회\n\n\nAI 앱\n\n\nR/Python/SQL\n\n\n프롬프트 엔지니어링\n\n\n\n\n2023 년 09 월 01일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT\n\n\n광명시\n\n\n\n챗GPT\n\n\n데이터 사이언스\n\n\n디지털 전환\n\n\nText-to-X\n\n\n광명시\n\n\n한국 R 사용자회\n\n\n챗GPT + 행정\n\n\n프롬프트 엔지니어링\n\n\n\n\n2023 년 08 월 11일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n후쿠시마 오염수 방류\n\n\n경기도 해안선 인접 구시군 영향\n\n\n\n후쿠시마 원전사고\n\n\n오염수 방류\n\n\n경기도 해안선\n\n\n국가/지방 항구\n\n\n평택, 화성, 안산, 김포\n\n\n\n\n2023 년 07 월 11일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n드림톡 콘서트 - 과학 행사\n\n\n오픈 소스 소프트웨어(코딩)\n\n\n\n오픈소스 소프트웨어\n\n\n디지털 글쓰기\n\n\n한국 R 사용자회\n\n\n오픈 통계 패키지\n\n\n쿼토(Quarto)\n\n\n하드웨어/소프트웨어\n\n\n챗GPT 사용자 인터페이스\n\n\n다양한 소프트웨어 개발방법\n\n\n계산기 프로그램\n\n\n문서화 프로그래밍 커뮤니티\n\n\n챗GPT 디지털 글쓰기 출판\n\n\n\n\n2023 년 07 월 10일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT: 사람과 AI 분업\n\n\n순천향대학교\n\n\n\n챗GPT\n\n\n디지털 글쓰기\n\n\nText-to-X\n\n\n사람과 기계 업무분업\n\n\n데이터 사이언스\n\n\n한국 R 사용자회\n\n\n쿼토(Quarto)\n\n\n문서화 프로그래밍\n\n\n순천향대학교\n\n\n디지털 전환\n\n\n\n\n2023 년 07 월 06일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n공공데이터 이해 및 데이터 활용역량\n\n\n챗GPT와 데이터 사이언스\n\n\n\n4차 산업혁명\n\n\n인공지능 (AI)\n\n\n한국 R 사용자회\n\n\n공공데이터\n\n\n광명시\n\n\n빅데이터\n\n\nText-to-X\n\n\n데이터 역량강화\n\n\n데이터 과학\n\n\n사무 자동화/지능화\n\n\n\n\n2023 년 07 월 04일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT와 데이터 사이언스\n\n\n건국대 미래지식교육원 전문교육과정\n\n\n\n챗GPT\n\n\n데이터 사이언스\n\n\n소프트웨어 카펜트리\n\n\n글로벌 수료증\n\n\n디지털 전환\n\n\nText-to-X\n\n\n건국대\n\n\n미래지식교육원\n\n\n전문교육과정\n\n\n한국 R 사용자회\n\n\nAI 앱\n\n\nR/Python/SQL\n\n\n프롬프트 엔지니어링\n\n\n\n\n2023 년 07 월 01일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 디지털 글쓰기 출판\n\n\n서울 교육청\n\n\n\n챗GPT\n\n\n디지털 글쓰기\n\n\n출판\n\n\n한국 R 사용자회\n\n\n쿼토(Quarto)\n\n\n문서화 프로그래밍\n\n\n서울교육청\n\n\nbitPublish\n\n\n\n\n2023 년 06 월 27일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 디지털 글쓰기 출판\n\n\n한국 R 사용자회 워크샵\n\n\n\n챗GPT\n\n\n디지털 글쓰기\n\n\n출판\n\n\n한국 R 사용자회\n\n\n쿼토(Quarto)\n\n\n문서화 프로그래밍\n\n\nR/Python/SQL/LaTeX\n\n\n프롬프트 엔지니어링\n\n\n\n\n2023 년 06 월 24일\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT\n\n\n챗GPT 4차 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실습\n\n\nPrompt Engineering\nOPL(OpenAI, Pinecone, Langchain) 프레임워크\n핸즈온 실습\n\n\n8 주차 - 2023-08-26\n\n\n챗GPT AI 앱 프로젝트\n오픈소스 LLM 기반 AI 데이터 분석 제품\n핸즈온 실습\n\n\n챗GPT AI 앱 프로젝트\n데이터 사이언스 AutoGPT\n핸즈온 실습\n\n\n챗GPT AI 앱 프로젝트\n챗GPT AI 프로젝트 발표\n핸즈온 실습\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n데이터 사이언스 MCQ" + }, + { + "objectID": "chatgpt_sch_202404.html#데이터-과학-과거와-현재", + "href": "chatgpt_sch_202404.html#데이터-과학-과거와-현재", + "title": "챗GPT 데이터 사이언스", + "section": "데이터 과학 과거와 현재", + "text": "데이터 과학 과거와 현재\n \n\n\n과거\n\n\n\n현재" + }, + { + "objectID": "chatgpt_sch_202404.html#글쓰기-진화", + "href": "chatgpt_sch_202404.html#글쓰기-진화", + "title": "챗GPT 데이터 사이언스", + "section": "글쓰기 진화", + "text": "글쓰기 진화\n\n글쓰기 역사생산성과 자동화를 넘어개발과 저작\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n <!-- sourcecode --> package1 -->\n <!-- package1 --> product1 -->\n <!-- product1 --> distribute1 -->\n <!-- distribute1 --> service1 -->\n\n\n\n\n\n\n\n\n\npackage2 –> product2 –> distribute2 –> service2 –>" + }, + { + "objectID": 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Allaire" + }, + { + "objectID": "chatgpt_sch_202404.html#디지털-글쓰기", + "href": "chatgpt_sch_202404.html#디지털-글쓰기", + "title": "챗GPT 데이터 사이언스", + "section": "디지털 글쓰기", + "text": "디지털 글쓰기\n\n삼권분립한글 디지털 글쓰기 책\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 디지털 글쓰기" + }, + { + "objectID": "chatgpt_sch_202404.html#쿼토", + "href": "chatgpt_sch_202404.html#쿼토", + "title": "챗GPT 데이터 사이언스", + "section": "쿼토", + "text": "쿼토\n\n쿼토 소개쿼토 작업흐름쿼토\n\n\n\n\n\n\n \n\n\n\n\n\n\ngraph LR\n qmd --> 주피터\n 주피터 --> 마크다운\n 마크다운 --> 팬독\n 팬독 --> pdf\n 팬독 --> html\n 팬독 --> docx\n qmd --> knitr\n knitr --> 마크다운\n\n\n\n\n그림 1: 저작 컴파일 출판 쿼토 작업흐름\n\n\n\n\n\n\n\n\n\n\n표 1: R 마크다운과 쿼토 비교\n\n\n\n\n\n기능\nR 마크다운\n쿼토(Quarto)\n\n\n\n\n파일형식\nhtml_document / pdf_document / word_document\nhtml / pdf / docx\n\n\n비머(Beamer)\nbeamer_presentation\nbeamer\n\n\n파워포인트(PPT)\npowerpoint_presentation\npptx\n\n\n웹 슬라이드\nxaringan / ioslides / revealjs\nrevealjs\n\n\n고급 레이아웃\ntufte / distill\nQuarto Article Layout\n\n\n상호 참조\nhtml_document2 / pdf_document2 / word_document2\nQuarto Crossrefs\n\n\n웹사이트/블로그\nblogdown / distill\nQuarto Websites / Quarto Blogs\n\n\n책\nbookdown\nQuarto Books\n\n\n인터랙티브 문서\nShiny Documents\nQuarto Interactive Documents\n\n\n페이지 HTML\npagedown\n출시 예정\n\n\n학술 논문\nrticles\nQuarto Journal Articles\n\n\n대쉬보드\nflexdashboard\nQuarto Dashboards\n\n\n인터랙티브 자습서\nlearnr\n계획 없음\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n한국 R 사용자회 chatGPT. theme insper 소스코드 GitHub." + }, + { + "objectID": "chatgpt_sch_202404.html#디지털-글쓰기---쿼토", + "href": "chatgpt_sch_202404.html#디지털-글쓰기---쿼토", + "title": "챗GPT 데이터 사이언스", + "section": "디지털 글쓰기 - 쿼토", + "text": "디지털 글쓰기 - 쿼토\n\n삼권분립쿼토 소개쿼토 작업흐름쿼토\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n주피터 –> 마크다운 –> 팬독 –> pdf –> html –> docx –> knitr –> 마크다운 –> \n\n\n\n\n\n표 1: R 마크다운과 쿼토 비교\n\n\n\n\n\n기능\nR 마크다운\n쿼토(Quarto)\n\n\n\n\n파일형식\nhtml_document / pdf_document / word_document\nhtml / pdf / docx\n\n\n비머(Beamer)\nbeamer_presentation\nbeamer\n\n\n파워포인트(PPT)\npowerpoint_presentation\npptx\n\n\n웹 슬라이드\nxaringan / ioslides / revealjs\nrevealjs\n\n\n고급 레이아웃\ntufte / distill\nQuarto Article Layout\n\n\n상호 참조\nhtml_document2 / pdf_document2 / word_document2\nQuarto Crossrefs\n\n\n웹사이트/블로그\nblogdown / distill\nQuarto Websites / Quarto Blogs\n\n\n책\nbookdown\nQuarto Books\n\n\n인터랙티브 문서\nShiny Documents\nQuarto Interactive Documents\n\n\n페이지 HTML\npagedown\n출시 예정\n\n\n학술 논문\nrticles\nQuarto Journal Articles\n\n\n대쉬보드\nflexdashboard\nQuarto Dashboards\n\n\n인터랙티브 자습서\nlearnr\n계획 없음\n\n\n\n\n\n\n\n\n\n\n챗GPT 디지털 글쓰기" + }, + { + "objectID": "chatgpt_sch_202404.html#포스터-제작", + "href": "chatgpt_sch_202404.html#포스터-제작", + "title": "챗GPT 데이터 사이언스", + "section": "포스터 제작", + "text": "포스터 제작\n\n포스터 시안포스터 편집최종 포스터\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nShareGPT: Canva 사용사례" + }, + { + "objectID": "chatgpt_sch_202404.html#api-프로그래밍", + "href": "chatgpt_sch_202404.html#api-프로그래밍", + "title": "챗GPT 데이터 사이언스", + "section": "API 프로그래밍", + "text": "API 프로그래밍\n\n준비 프롬프트코드제작 프롬프트롬코드수정작업결과\n\n\n\n웹사이트를 읽고 다음 지령을 준비해 https://apis.map.kakao.com/web/documentation/\n\nKakao 지도 API 문서는 다양한 지도 관련 기능을 제공한다. 이 문서는 지도 객체 생성, 이벤트, 메서드 등에 대한 상세한 정보를 담고 있다. 아래는 주요 내용의 요약이다.\n\n### 지도 객체 생성\n- `kakao.maps.Map(container, options)`: 지도를 생성한다.\n - `container`: 지도가 표시될 HTML element\n - `options`: 중심 좌표, 확대 수준, 지도 종류 등을 설정할 수 있다.\n\n### 주요 메서드\n- `setCenter(latlng)`: 지도의 중심 좌표를 설정한다.\n- `getCenter()`: 지도의 중심 좌표를 반환한다.\n- `setLevel(level, options)`: 지도의 확대 수준을 설정한다.\n- `getLevel()`: 지도의 확대 수준을 반환한다.\n- `setMapTypeId(mapTypeId)`: 지도의 타입을 설정한다.\n- `getMapTypeId()`: 지도의 타입을 반환한다.\n\n### 이벤트\n- `center_changed`: 중심 좌표가 변경되면 발생한다.\n- `zoom_start`: 확대 수준이 변경되기 직전에 발생한다.\n- `zoom_changed`: 확대 수준이 변경되면 발생한다.\n- `click`: 지도를 클릭하면 발생한다.\n- `dblclick`: 지도를 더블클릭하면 발생한다.\n\n이 외에도 다양한 메서드와 이벤트가 있어, 개발자가 원하는 기능을 쉽게 구현할 수 있다.\n\n\n\n주소를 넘기면 위경도 좌표를 반환하는 R 코드를 작성해줘.\n\nR에서 주소를 위경도 좌표로 변환하기 위해 httr 패키지를 사용하여 Kakao API를 호출한 예시 코드이다.\n# 필요한 패키지를 설치하고 불러온다\ninstall.packages(\"httr\")\ninstall.packages(\"jsonlite\")\nlibrary(httr)\nlibrary(jsonlite)\n\n# 주소를 위경도로 변환하는 함수를 정의한다\nget_latlng <- function(address) {\n # Kakao API 키를 설정한다 (본인의 API 키로 교체해야 한다)\n api_key <- \"YOUR_KAKAO_API_KEY\"\n \n # API URL과 파라미터를 설정한다\n url <- \"https://dapi.kakao.com/v2/local/search/address.json\"\n params <- list(query = address)\n headers <- add_headers(\"Authorization\" = paste(\"KakaoAK\", api_key))\n \n # API를 호출한다\n response <- GET(url, query = params, headers)\n \n # 응답을 확인하고 JSON을 파싱한다\n if (status_code(response) == 200) {\n content <- content(response, \"text\", encoding = \"UTF-8\")\n json_data <- fromJSON(content)\n \n # 위경도를 추출한다\n if (json_data$meta$total_count > 0) {\n lat <- json_data$documents[[1]]$y\n lng <- json_data$documents[[1]]$x\n return(list(lat = lat, lng = lng))\n } else {\n return(NULL)\n }\n } else {\n stop(\"API request failed\")\n }\n}\n\n# 함수를 테스트한다\naddress <- \"서울특별시 강남구 테헤란로 212\"\nresult <- get_latlng(address)\nprint(paste(\"Latitude:\", result$lat, \"Longitude:\", result$lng))\n\n\n\nlibrary(httr)\nlibrary(jsonlite)\n\n# 주소를 위경도로 변환하는 함수를 정의한다\nget_latlng <- function(address) {\n # Kakao API 키를 설정한다 (본인의 API 키로 교체해야 한다)\n api_key <- Sys.getenv(\"KAKAO_MAP_API_KEY\")\n \n # API URL과 파라미터를 설정한다\n url <- \"https://dapi.kakao.com/v2/local/search/address.json\"\n params <- list(query = address)\n headers <- add_headers(\"Authorization\" = paste(\"KakaoAK\", api_key))\n \n # API를 호출한다\n response <- GET(url, query = params, headers)\n \n # 응답을 확인하고 JSON을 파싱한다\n if (status_code(response) == 200) {\n content <- content(response, \"text\", encoding = \"UTF-8\")\n json_data <- fromJSON(content)\n \n # 위경도를 추출한다\n if (json_data$meta$total_count > 0) {\n lat <- json_data$documents[[1]]$y\n lng <- json_data$documents[[1]]$x\n return(list(lat = lat, lng = lng))\n } else {\n return(NULL)\n }\n } else {\n stop(\"API request failed\")\n }\n}\n\n# 가톨릭관동대학교 주소\naddress <- \"강원도 강릉시 범일로579번길 24\"\nresult <- get_latlng(address)\nprint(paste(\"Latitude:\", result$lat, \"Longitude:\", result$lng))\n\n\n\n\n\n\n\n\n\n\n\n\nShareGPT: API 사용사례, 플러그인(Plugin) - 칸바, 카카오맵, 울프람" + }, + { + "objectID": "chatgpt_sch_202404.html#사실확인-wolfram", + "href": "chatgpt_sch_202404.html#사실확인-wolfram", + "title": "챗GPT 데이터 사이언스", + "section": "사실확인 (Wolfram)", + "text": "사실확인 (Wolfram)\n\n지구는 평평해?라면은 건강에 안 좋아?\n\n\n울프람 알파(Wolfram Alpha) 사용해서 사실관계를 확인해보자.\n\n“지구는 평평하다”라는 주장을 검증해줘.\n\n\"지구는 평평하다\"라는 주장에 대해 Wolfram Alpha를 이용해 검증해보았습니다.\n\n### 입력 해석\n- 질문: 지구는 평평하다?\n\n### 결과\n- 답변: 아니다.\n - 지역적으로만 그렇다. 그리고 그것도 특정 지역, 예를 들어 미국 중서부에서만 해당된다.\n\n자세한 정보는 [Wolfram|Alpha 웹사이트](https://www6b3.wolframalpha.com/input?i=Is+the+Earth+flat%3F)에서 확인할 수 있다.\n\n이처럼 Wolfram Alpha는 다양한 주장이나 정보에 대한 팩트체크를 할 수 있는 유용한 도구이다.\n\nWolfram|Alpha 웹사이트\n\n\n\n\n라면을 많이 먹으면 건강에 정말 안좋은가?\n\nWolfram Alpha에서는 “라면을 많이 먹으면 건강에 정말 안 좋은가?”에 대한 구체적인 답변을 제공하지 못했다. 그러나 일반적으로 라면은 높은 나트륨, 지방, 인공 첨가물 등을 포함하고 있어, 과도하게 섭취할 경우 다음과 같은 건강 문제가 발생할 수 있다:\n\n고혈압: 라면에는 나트륨이 많이 들어있다. 나트륨 과다 섭취는 혈압을 높일 수 있다.\n비만: 라면은 칼로리가 높고, 지방도 많이 들어있다. 지속적으로 라면을 많이 먹으면 체중 증가의 위험이 있다.\n소화기 문제: 라면은 소화가 잘 되지 않는 식품 중 하나이다. 과도한 섭취는 소화기에 부담을 줄 수 있다.\n영양 불균형: 라면은 영양소가 적고, 나트륨이나 지방 등의 불필요한 성분이 많다. 이로 인해 영양 불균형이 발생할 수 있다.\n심혈관 질환: 고혈압, 높은 콜레스테롤 등은 심혈관 질환의 위험을 높인다.\n\n라면을 즐길 때는 이러한 점들을 고려하여 적당한 양과 빈도로 섭취하는 것이 중요하다." + }, + { + "objectID": "chatgpt_sch_202404.html#수능문제-풀이", + "href": "chatgpt_sch_202404.html#수능문제-풀이", + "title": "챗GPT 데이터 사이언스", + "section": "수능문제 풀이", + "text": "수능문제 풀이\n\n2023 수능 수학문제 풀이풀이 과정\n\n\n2023학년도 대학수학능력시험 수학영역 문제를 풀어보자.\n\n\n\n\n\\(\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\\) 값은?\n\n식 \\[\n\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n\\] 의 값은 \\(4\\)입니다.\n정확한 형태로는 \\(2^{(2 - \\sqrt{2}) (2 + \\sqrt{2})}\\)로 표현됩니다.\n자세한 정보는 Wolfram|Alpha 웹사이트에서 확인할 수 있다.\n\n\n\n과정도 설명해줘.\n\n\n\n물론이다. 주어진 식은 \\[\n\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n\\]이다. 이 식을 단계별로 분해해보자.\n\n단계 1: 분모를 간단하게 만들기 먼저, 분모인 \\(2^{\\sqrt{2}}\\)를 간단하게 만들기 위해 분자와 분모에 \\(2^{\\sqrt{2}}\\)를 곱한다. 이렇게 하면:\n\n\\[\n\\left(\\frac{4 \\times 2^{\\sqrt{2}}}{2^{\\sqrt{2}} \\times 2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n\\]\n\n단계 2: 분모를 합치기 이제 분모의 \\(2^{\\sqrt{2}} \\times 2^{\\sqrt{2}}\\)를 합쳐 \\(2^{2\\sqrt{2}}\\)로 만든다.\n\n\\[\n\\left(\\frac{4 \\times 2^{\\sqrt{2}}}{2^{2\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n\\]\n\n\n단계 3: 분자를 합치기 분자의 \\(4 \\times 2^{\\sqrt{2}}\\)를 합쳐 \\(2^2 \\times 2^{\\sqrt{2}} = 2^{2+\\sqrt{2}}\\)로 만든다.\n\n\\[\n\\left(\\frac{2^{2+\\sqrt{2}}}{2^{2\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n\\]\n\n단계 4: 지수 법칙 적용 지수 법칙에 따라 분자와 분모의 지수를 빼준다.\n\n\\[\n\\left(2^{(2+\\sqrt{2}) - 2\\sqrt{2}}\\right)^{2+\\sqrt{2}} = \\left(2^{(2 - \\sqrt{2})(2 + \\sqrt{2})}\\right)^{2+\\sqrt{2}}\n\\]\n\n단계 5: 최종 계산 마지막으로, \\((2 - 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Allaire\")\n\nhistory_tbl |>\n write_csv(\"../../data/history_tbl.csv\")\n\n\nlibrary(tidyverse)\nlibrary(rvest)\nlibrary(gt)\nlibrary(gtExtras)\n\nhistory_tbl <- \n read_csv(glue::glue(\"{here::here()}/data/history_tbl.csv\"))\n\nhistory_tbl |> \n mutate(link = map(link, gt::html)) |> \n select(연도, 도구=link, 개발자) |> \n gt() |> \n # gt_theme_hangul() |> \n cols_align(\"center\")\n\n\n\n\n\n\n\n\n연도\n도구\n개발자\n\n\n\n\n1978\nTeX\nDonald Knuth\n\n\n1984\nLiterate Programming\nDonald Knuth\n\n\n1988\nMathematica Notebooks\nStephen Wolfram\n\n\n2001\nIPython\nFernando Perez\n\n\n2003\nEmacs org-mode\nCarsten Dominik\n\n\n2004\nMarkdown\nJohn Gruber\n\n\n2005\nSage Notebook\nWilliam Stein\n\n\n2006\nPandoc\nJohn MacFarlane\n\n\n2009\nGitHub Flavored Markdown\nTom Preston-Werner\n\n\n2011\niPython Notebook\nFernando Perez\n\n\n2012\nknitr\nYihui Xie\n\n\n2014\nProject Jupyter\nFernando Perez\n\n\n2020\nQuarto\nJ.J. Allaire\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 디지털 글쓰기\n\n삼권분립쿼토 소개쿼토 작업흐름쿼토\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n주피터 –> 마크다운 –> 팬독 –> pdf –> html –> docx –> knitr –> 마크다운 –> \n\n\n\n\n\n표 1: R 마크다운과 쿼토 비교\n\n\n\n\n\n기능\nR 마크다운\n쿼토(Quarto)\n\n\n\n\n파일형식\nhtml_document / pdf_document / word_document\nhtml / pdf / docx\n\n\n비머(Beamer)\nbeamer_presentation\nbeamer\n\n\n파워포인트(PPT)\npowerpoint_presentation\npptx\n\n\n웹 슬라이드\nxaringan / ioslides / revealjs\nrevealjs\n\n\n고급 레이아웃\ntufte / distill\nQuarto Article Layout\n\n\n상호 참조\nhtml_document2 / pdf_document2 / word_document2\nQuarto Crossrefs\n\n\n웹사이트/블로그\nblogdown / distill\nQuarto Websites / Quarto Blogs\n\n\n책\nbookdown\nQuarto Books\n\n\n인터랙티브 문서\nShiny Documents\nQuarto Interactive Documents\n\n\n페이지 HTML\npagedown\n출시 예정\n\n\n학술 논문\nrticles\nQuarto Journal Articles\n\n\n대쉬보드\nflexdashboard\nQuarto Dashboards\n\n\n인터랙티브 자습서\nlearnr\n계획 없음" + }, + { + "objectID": "slideshow/202404-sch/quarto-digital-writing.html#글쓰기-진화", + "href": "slideshow/202404-sch/quarto-digital-writing.html#글쓰기-진화", + "title": "도구(문서)의 인간 디지털 저작", + "section": "", + "text": "글쓰기 역사생산성과 자동화를 넘어개발과 저작\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n <!-- sourcecode --> package1 -->\n <!-- package1 --> product1 -->\n <!-- product1 --> distribute1 -->\n <!-- distribute1 --> service1 -->\n\n\n\n\n\n\n\n\n\npackage2 –> product2 –> distribute2 –> service2 –>" + }, + { + "objectID": "slideshow/202404-sch/quarto-digital-writing.html#데이터-과학-디지털-글쓰기", + "href": "slideshow/202404-sch/quarto-digital-writing.html#데이터-과학-디지털-글쓰기", + "title": "도구(문서)의 인간 디지털 저작", + "section": "", + "text": "데이터 과학문서 구성요소문서 복잡성문서 도구 개발 역사\n\n\n\n\n\n\n\n\n\n\n\n\nlibrary(tidyverse)\nlibrary(rvest)\nlibrary(gt)\nlibrary(gtExtras)\n\nhistory_html <- read_html('https://jjallaire.quarto.pub/notebook-publishing/#/history') \n\nhistory_url <- history_html |> \n html_element('table') |> \n html_elements('a') |> \n html_attr(\"href\")\n\nhistory_tbl <- history_html |> \n html_element('table') |> \n html_table() |> \n set_names(c(\"연도\", \"도구\", \"개발자\")) |> \n mutate(url = history_url) |> \n mutate(link = str_glue(\"<a href={url}>{도구}</a>\")) |> \n select(연도, link, 개발자) |> \n add_row(연도 = 2020, link = \"<a href=https://quarto.org>Quarto</a>\",\n 개발자 = \"J.J. Allaire\")\n\nhistory_tbl |>\n write_csv(\"../../data/history_tbl.csv\")\n\n\nlibrary(tidyverse)\nlibrary(rvest)\nlibrary(gt)\nlibrary(gtExtras)\n\nhistory_tbl <- \n read_csv(glue::glue(\"{here::here()}/data/history_tbl.csv\"))\n\nhistory_tbl |> \n mutate(link = map(link, gt::html)) |> \n select(연도, 도구=link, 개발자) |> \n gt() |> \n # gt_theme_hangul() |> \n cols_align(\"center\")\n\n\n\n\n\n\n\n\n연도\n도구\n개발자\n\n\n\n\n1978\nTeX\nDonald Knuth\n\n\n1984\nLiterate Programming\nDonald Knuth\n\n\n1988\nMathematica Notebooks\nStephen Wolfram\n\n\n2001\nIPython\nFernando Perez\n\n\n2003\nEmacs org-mode\nCarsten Dominik\n\n\n2004\nMarkdown\nJohn Gruber\n\n\n2005\nSage Notebook\nWilliam Stein\n\n\n2006\nPandoc\nJohn MacFarlane\n\n\n2009\nGitHub Flavored Markdown\nTom Preston-Werner\n\n\n2011\niPython Notebook\nFernando Perez\n\n\n2012\nknitr\nYihui Xie\n\n\n2014\nProject Jupyter\nFernando Perez\n\n\n2020\nQuarto\nJ.J. 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논문\nrticles\nQuarto Journal Articles\n\n\n대쉬보드\nflexdashboard\nQuarto Dashboards\n\n\n인터랙티브 자습서\nlearnr\n계획 없음" + }, + { + "objectID": "slideshow/202404-sch/dashboard.html", + "href": "slideshow/202404-sch/dashboard.html", + "title": "대쉬보드", + "section": "", + "text": "쿼토 코로나19 제작과정 (Kulkarni 2019)\n\n\n\n\n\n대시보드: https://statkclee.github.io/coronaDashboards/\n소스코드: https://github.com/statkclee/coronaDashboards/\n제작방법: https://r2bit.com/gpt-quarto/dw_dashboard.html\n\n\n\n\n\n\n\n\n\n\n\n\n대시보드: https://statkclee.github.io/president/social-dashboard.html\n소스코드: https://github.com/statkclee/president/blob/main/social-dashboard.Rmd\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n대시보드: https://statkclee.quarto.pub/\n소스코드: 공개준비중\n\n\n쿼토 코로나19 제작과정 (Kulkarni 2019)" + }, + { + "objectID": "slideshow/202404-sch/dashboard.html#코로나19-대쉬보드", + "href": "slideshow/202404-sch/dashboard.html#코로나19-대쉬보드", + "title": "대쉬보드", + "section": "", + "text": "쿼토 코로나19 제작과정 (Kulkarni 2019)\n\n\n\n\n\n대시보드: https://statkclee.github.io/coronaDashboards/\n소스코드: https://github.com/statkclee/coronaDashboards/\n제작방법: https://r2bit.com/gpt-quarto/dw_dashboard.html" + }, + { + "objectID": "slideshow/202404-sch/dashboard.html#flexdashboard", + "href": "slideshow/202404-sch/dashboard.html#flexdashboard", + "title": "대쉬보드", + "section": "", + "text": "대시보드: https://statkclee.github.io/president/social-dashboard.html\n소스코드: https://github.com/statkclee/president/blob/main/social-dashboard.Rmd" + }, + { + "objectID": "slideshow/202404-sch/dashboard.html#쿼토-대시보드", + "href": "slideshow/202404-sch/dashboard.html#쿼토-대시보드", + "title": "대쉬보드", + "section": "", + "text": "대시보드: https://statkclee.quarto.pub/\n소스코드: 공개준비중\n\n\n쿼토 코로나19 제작과정 (Kulkarni 2019)" + }, + { + "objectID": "slideshow/202404-sch/chatgpt-data-science.html", + "href": "slideshow/202404-sch/chatgpt-data-science.html", + "title": "생성형 AI 데이터 사이언스", + "section": "", + "text": "포스터 시안포스터 편집최종 포스터\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nShareGPT: Canva 사용사례\n\n\n\n\n\n준비 프롬프트코드제작 프롬프트롬코드수정작업결과\n\n\n\n웹사이트를 읽고 다음 지령을 준비해 https://apis.map.kakao.com/web/documentation/\n\nKakao 지도 API 문서는 다양한 지도 관련 기능을 제공한다. 이 문서는 지도 객체 생성, 이벤트, 메서드 등에 대한 상세한 정보를 담고 있다. 아래는 주요 내용의 요약이다.\n\n### 지도 객체 생성\n- `kakao.maps.Map(container, options)`: 지도를 생성한다.\n - `container`: 지도가 표시될 HTML element\n - `options`: 중심 좌표, 확대 수준, 지도 종류 등을 설정할 수 있다.\n\n### 주요 메서드\n- `setCenter(latlng)`: 지도의 중심 좌표를 설정한다.\n- `getCenter()`: 지도의 중심 좌표를 반환한다.\n- `setLevel(level, options)`: 지도의 확대 수준을 설정한다.\n- `getLevel()`: 지도의 확대 수준을 반환한다.\n- `setMapTypeId(mapTypeId)`: 지도의 타입을 설정한다.\n- `getMapTypeId()`: 지도의 타입을 반환한다.\n\n### 이벤트\n- `center_changed`: 중심 좌표가 변경되면 발생한다.\n- `zoom_start`: 확대 수준이 변경되기 직전에 발생한다.\n- `zoom_changed`: 확대 수준이 변경되면 발생한다.\n- `click`: 지도를 클릭하면 발생한다.\n- `dblclick`: 지도를 더블클릭하면 발생한다.\n\n이 외에도 다양한 메서드와 이벤트가 있어, 개발자가 원하는 기능을 쉽게 구현할 수 있다.\n\n\n\n주소를 넘기면 위경도 좌표를 반환하는 R 코드를 작성해줘.\n\nR에서 주소를 위경도 좌표로 변환하기 위해 httr 패키지를 사용하여 Kakao API를 호출한 예시 코드이다.\n# 필요한 패키지를 설치하고 불러온다\ninstall.packages(\"httr\")\ninstall.packages(\"jsonlite\")\nlibrary(httr)\nlibrary(jsonlite)\n\n# 주소를 위경도로 변환하는 함수를 정의한다\nget_latlng <- function(address) {\n # Kakao API 키를 설정한다 (본인의 API 키로 교체해야 한다)\n api_key <- \"YOUR_KAKAO_API_KEY\"\n \n # API URL과 파라미터를 설정한다\n url <- \"https://dapi.kakao.com/v2/local/search/address.json\"\n params <- list(query = address)\n headers <- add_headers(\"Authorization\" = paste(\"KakaoAK\", api_key))\n \n # API를 호출한다\n response <- GET(url, query = params, headers)\n \n # 응답을 확인하고 JSON을 파싱한다\n if (status_code(response) == 200) {\n content <- content(response, \"text\", encoding = \"UTF-8\")\n json_data <- fromJSON(content)\n \n # 위경도를 추출한다\n if (json_data$meta$total_count > 0) {\n lat <- json_data$documents[[1]]$y\n lng <- json_data$documents[[1]]$x\n return(list(lat = lat, lng = lng))\n } else {\n return(NULL)\n }\n } else {\n stop(\"API request failed\")\n }\n}\n\n# 함수를 테스트한다\naddress <- \"서울특별시 강남구 테헤란로 212\"\nresult <- get_latlng(address)\nprint(paste(\"Latitude:\", result$lat, \"Longitude:\", result$lng))\n\n\n\nlibrary(httr)\nlibrary(jsonlite)\n\n# 주소를 위경도로 변환하는 함수를 정의한다\nget_latlng <- function(address) {\n # Kakao API 키를 설정한다 (본인의 API 키로 교체해야 한다)\n api_key <- Sys.getenv(\"KAKAO_MAP_API_KEY\")\n \n # API URL과 파라미터를 설정한다\n url <- \"https://dapi.kakao.com/v2/local/search/address.json\"\n params <- list(query = address)\n headers <- add_headers(\"Authorization\" = paste(\"KakaoAK\", api_key))\n \n # API를 호출한다\n response <- GET(url, query = params, headers)\n \n # 응답을 확인하고 JSON을 파싱한다\n if (status_code(response) == 200) {\n content <- content(response, \"text\", encoding = \"UTF-8\")\n json_data <- fromJSON(content)\n \n # 위경도를 추출한다\n if (json_data$meta$total_count > 0) {\n lat <- json_data$documents[[1]]$y\n lng <- json_data$documents[[1]]$x\n return(list(lat = lat, lng = lng))\n } else {\n return(NULL)\n }\n } else {\n stop(\"API request failed\")\n }\n}\n\n# 가톨릭관동대학교 주소\naddress <- \"강원도 강릉시 범일로579번길 24\"\nresult <- get_latlng(address)\nprint(paste(\"Latitude:\", result$lat, \"Longitude:\", result$lng))\n\n\n\n\n\n\n\n\n\n\n\n\nShareGPT: API 사용사례, 플러그인(Plugin) - 칸바, 카카오맵, 울프람\n\n\n\n\n\n지구는 평평해?라면은 건강에 안 좋아?\n\n\n울프람 알파(Wolfram Alpha) 사용해서 사실관계를 확인해보자.\n\n“지구는 평평하다”라는 주장을 검증해줘.\n\n\"지구는 평평하다\"라는 주장에 대해 Wolfram Alpha를 이용해 검증해보았습니다.\n\n### 입력 해석\n- 질문: 지구는 평평하다?\n\n### 결과\n- 답변: 아니다.\n - 지역적으로만 그렇다. 그리고 그것도 특정 지역, 예를 들어 미국 중서부에서만 해당된다.\n\n자세한 정보는 [Wolfram|Alpha 웹사이트](https://www6b3.wolframalpha.com/input?i=Is+the+Earth+flat%3F)에서 확인할 수 있다.\n\n이처럼 Wolfram Alpha는 다양한 주장이나 정보에 대한 팩트체크를 할 수 있는 유용한 도구이다.\n\nWolfram|Alpha 웹사이트\n\n\n\n\n라면을 많이 먹으면 건강에 정말 안좋은가?\n\nWolfram Alpha에서는 “라면을 많이 먹으면 건강에 정말 안 좋은가?”에 대한 구체적인 답변을 제공하지 못했다. 그러나 일반적으로 라면은 높은 나트륨, 지방, 인공 첨가물 등을 포함하고 있어, 과도하게 섭취할 경우 다음과 같은 건강 문제가 발생할 수 있다:\n\n고혈압: 라면에는 나트륨이 많이 들어있다. 나트륨 과다 섭취는 혈압을 높일 수 있다.\n비만: 라면은 칼로리가 높고, 지방도 많이 들어있다. 지속적으로 라면을 많이 먹으면 체중 증가의 위험이 있다.\n소화기 문제: 라면은 소화가 잘 되지 않는 식품 중 하나이다. 과도한 섭취는 소화기에 부담을 줄 수 있다.\n영양 불균형: 라면은 영양소가 적고, 나트륨이나 지방 등의 불필요한 성분이 많다. 이로 인해 영양 불균형이 발생할 수 있다.\n심혈관 질환: 고혈압, 높은 콜레스테롤 등은 심혈관 질환의 위험을 높인다.\n\n라면을 즐길 때는 이러한 점들을 고려하여 적당한 양과 빈도로 섭취하는 것이 중요하다.\n\n\n\n\n\n\n\n2023 수능 수학문제 풀이풀이 과정\n\n\n2023학년도 대학수학능력시험 수학영역 문제를 풀어보자.\n\n\n\n\n\\(\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\\) 값은?\n\n식 \\[\n\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n\\] 의 값은 \\(4\\)입니다.\n정확한 형태로는 \\(2^{(2 - \\sqrt{2}) (2 + \\sqrt{2})}\\)로 표현됩니다.\n자세한 정보는 Wolfram|Alpha 웹사이트에서 확인할 수 있다.\n\n\n\n과정도 설명해줘.\n\n\n\n물론이다. 주어진 식은 \\[\n\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n\\]이다. 이 식을 단계별로 분해해보자.\n\n단계 1: 분모를 간단하게 만들기 먼저, 분모인 \\(2^{\\sqrt{2}}\\)를 간단하게 만들기 위해 분자와 분모에 \\(2^{\\sqrt{2}}\\)를 곱한다. 이렇게 하면:\n\n\\[\n\\left(\\frac{4 \\times 2^{\\sqrt{2}}}{2^{\\sqrt{2}} \\times 2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n\\]\n\n단계 2: 분모를 합치기 이제 분모의 \\(2^{\\sqrt{2}} \\times 2^{\\sqrt{2}}\\)를 합쳐 \\(2^{2\\sqrt{2}}\\)로 만든다.\n\n\\[\n\\left(\\frac{4 \\times 2^{\\sqrt{2}}}{2^{2\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n\\]\n\n\n단계 3: 분자를 합치기 분자의 \\(4 \\times 2^{\\sqrt{2}}\\)를 합쳐 \\(2^2 \\times 2^{\\sqrt{2}} = 2^{2+\\sqrt{2}}\\)로 만든다.\n\n\\[\n\\left(\\frac{2^{2+\\sqrt{2}}}{2^{2\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n\\]\n\n단계 4: 지수 법칙 적용 지수 법칙에 따라 분자와 분모의 지수를 빼준다.\n\n\\[\n\\left(2^{(2+\\sqrt{2}) - 2\\sqrt{2}}\\right)^{2+\\sqrt{2}} = \\left(2^{(2 - \\sqrt{2})(2 + \\sqrt{2})}\\right)^{2+\\sqrt{2}}\n\\]\n\n단계 5: 최종 계산 마지막으로, \\((2 - \\sqrt{2})(2 + \\sqrt{2})\\)를 계산하면 \\(4 - 2 = 2\\)가 된다. 따라서 최종 결과는 \\(2^2 = 4\\)이다.\n\n이렇게 해서 \\[\n\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}} = 4\n\\]임을 확인할 수 있다.\n\n\n\n\n\n\n\nShareGPT: 팩트체크와 수학문제 풀이\n\n\n\n\n\nOpen AI Code Interpreter → Advanced Data Analysis → 챗GPT4\n\n\n\n\n\n생성AI 광명시 데이터 분석 사례\n\n\n\n\nTools -> Global Options -> Copilot -> Enable Github Copilot\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n소스코드\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 데이터 과학" + }, + { + "objectID": "slideshow/202404-sch/chatgpt-data-science.html#포스터-제작", + "href": "slideshow/202404-sch/chatgpt-data-science.html#포스터-제작", + "title": "생성형 AI 데이터 사이언스", + "section": "", + "text": "포스터 시안포스터 편집최종 포스터\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nShareGPT: Canva 사용사례" + }, + { + "objectID": "slideshow/202404-sch/chatgpt-data-science.html#api-프로그래밍", + "href": "slideshow/202404-sch/chatgpt-data-science.html#api-프로그래밍", + "title": "생성형 AI 데이터 사이언스", + "section": "", + "text": "준비 프롬프트코드제작 프롬프트롬코드수정작업결과\n\n\n\n웹사이트를 읽고 다음 지령을 준비해 https://apis.map.kakao.com/web/documentation/\n\nKakao 지도 API 문서는 다양한 지도 관련 기능을 제공한다. 이 문서는 지도 객체 생성, 이벤트, 메서드 등에 대한 상세한 정보를 담고 있다. 아래는 주요 내용의 요약이다.\n\n### 지도 객체 생성\n- `kakao.maps.Map(container, options)`: 지도를 생성한다.\n - `container`: 지도가 표시될 HTML element\n - `options`: 중심 좌표, 확대 수준, 지도 종류 등을 설정할 수 있다.\n\n### 주요 메서드\n- `setCenter(latlng)`: 지도의 중심 좌표를 설정한다.\n- `getCenter()`: 지도의 중심 좌표를 반환한다.\n- `setLevel(level, options)`: 지도의 확대 수준을 설정한다.\n- `getLevel()`: 지도의 확대 수준을 반환한다.\n- `setMapTypeId(mapTypeId)`: 지도의 타입을 설정한다.\n- `getMapTypeId()`: 지도의 타입을 반환한다.\n\n### 이벤트\n- `center_changed`: 중심 좌표가 변경되면 발생한다.\n- `zoom_start`: 확대 수준이 변경되기 직전에 발생한다.\n- `zoom_changed`: 확대 수준이 변경되면 발생한다.\n- `click`: 지도를 클릭하면 발생한다.\n- `dblclick`: 지도를 더블클릭하면 발생한다.\n\n이 외에도 다양한 메서드와 이벤트가 있어, 개발자가 원하는 기능을 쉽게 구현할 수 있다.\n\n\n\n주소를 넘기면 위경도 좌표를 반환하는 R 코드를 작성해줘.\n\nR에서 주소를 위경도 좌표로 변환하기 위해 httr 패키지를 사용하여 Kakao API를 호출한 예시 코드이다.\n# 필요한 패키지를 설치하고 불러온다\ninstall.packages(\"httr\")\ninstall.packages(\"jsonlite\")\nlibrary(httr)\nlibrary(jsonlite)\n\n# 주소를 위경도로 변환하는 함수를 정의한다\nget_latlng <- function(address) {\n # Kakao API 키를 설정한다 (본인의 API 키로 교체해야 한다)\n api_key <- \"YOUR_KAKAO_API_KEY\"\n \n # API URL과 파라미터를 설정한다\n url <- \"https://dapi.kakao.com/v2/local/search/address.json\"\n params <- list(query = address)\n headers <- add_headers(\"Authorization\" = paste(\"KakaoAK\", api_key))\n \n # API를 호출한다\n response <- GET(url, query = params, headers)\n \n # 응답을 확인하고 JSON을 파싱한다\n if (status_code(response) == 200) {\n content <- content(response, \"text\", encoding = \"UTF-8\")\n json_data <- fromJSON(content)\n \n # 위경도를 추출한다\n if (json_data$meta$total_count > 0) {\n lat <- json_data$documents[[1]]$y\n lng <- json_data$documents[[1]]$x\n return(list(lat = lat, lng = lng))\n } else {\n return(NULL)\n }\n } else {\n stop(\"API request failed\")\n }\n}\n\n# 함수를 테스트한다\naddress <- \"서울특별시 강남구 테헤란로 212\"\nresult <- get_latlng(address)\nprint(paste(\"Latitude:\", result$lat, \"Longitude:\", result$lng))\n\n\n\nlibrary(httr)\nlibrary(jsonlite)\n\n# 주소를 위경도로 변환하는 함수를 정의한다\nget_latlng <- function(address) {\n # Kakao API 키를 설정한다 (본인의 API 키로 교체해야 한다)\n api_key <- Sys.getenv(\"KAKAO_MAP_API_KEY\")\n \n # API URL과 파라미터를 설정한다\n url <- \"https://dapi.kakao.com/v2/local/search/address.json\"\n params <- list(query = address)\n headers <- add_headers(\"Authorization\" = paste(\"KakaoAK\", api_key))\n \n # API를 호출한다\n response <- GET(url, query = params, headers)\n \n # 응답을 확인하고 JSON을 파싱한다\n if (status_code(response) == 200) {\n content <- content(response, \"text\", encoding = \"UTF-8\")\n json_data <- fromJSON(content)\n \n # 위경도를 추출한다\n if (json_data$meta$total_count > 0) {\n lat <- json_data$documents[[1]]$y\n lng <- json_data$documents[[1]]$x\n return(list(lat = lat, lng = lng))\n } else {\n return(NULL)\n }\n } else {\n stop(\"API request failed\")\n }\n}\n\n# 가톨릭관동대학교 주소\naddress <- \"강원도 강릉시 범일로579번길 24\"\nresult <- get_latlng(address)\nprint(paste(\"Latitude:\", result$lat, \"Longitude:\", result$lng))\n\n\n\n\n\n\n\n\n\n\n\n\nShareGPT: API 사용사례, 플러그인(Plugin) - 칸바, 카카오맵, 울프람" + }, + { + "objectID": "slideshow/202404-sch/chatgpt-data-science.html#사실확인-wolfram", + "href": "slideshow/202404-sch/chatgpt-data-science.html#사실확인-wolfram", + "title": "생성형 AI 데이터 사이언스", + "section": "", + "text": "지구는 평평해?라면은 건강에 안 좋아?\n\n\n울프람 알파(Wolfram Alpha) 사용해서 사실관계를 확인해보자.\n\n“지구는 평평하다”라는 주장을 검증해줘.\n\n\"지구는 평평하다\"라는 주장에 대해 Wolfram Alpha를 이용해 검증해보았습니다.\n\n### 입력 해석\n- 질문: 지구는 평평하다?\n\n### 결과\n- 답변: 아니다.\n - 지역적으로만 그렇다. 그리고 그것도 특정 지역, 예를 들어 미국 중서부에서만 해당된다.\n\n자세한 정보는 [Wolfram|Alpha 웹사이트](https://www6b3.wolframalpha.com/input?i=Is+the+Earth+flat%3F)에서 확인할 수 있다.\n\n이처럼 Wolfram Alpha는 다양한 주장이나 정보에 대한 팩트체크를 할 수 있는 유용한 도구이다.\n\nWolfram|Alpha 웹사이트\n\n\n\n\n라면을 많이 먹으면 건강에 정말 안좋은가?\n\nWolfram Alpha에서는 “라면을 많이 먹으면 건강에 정말 안 좋은가?”에 대한 구체적인 답변을 제공하지 못했다. 그러나 일반적으로 라면은 높은 나트륨, 지방, 인공 첨가물 등을 포함하고 있어, 과도하게 섭취할 경우 다음과 같은 건강 문제가 발생할 수 있다:\n\n고혈압: 라면에는 나트륨이 많이 들어있다. 나트륨 과다 섭취는 혈압을 높일 수 있다.\n비만: 라면은 칼로리가 높고, 지방도 많이 들어있다. 지속적으로 라면을 많이 먹으면 체중 증가의 위험이 있다.\n소화기 문제: 라면은 소화가 잘 되지 않는 식품 중 하나이다. 과도한 섭취는 소화기에 부담을 줄 수 있다.\n영양 불균형: 라면은 영양소가 적고, 나트륨이나 지방 등의 불필요한 성분이 많다. 이로 인해 영양 불균형이 발생할 수 있다.\n심혈관 질환: 고혈압, 높은 콜레스테롤 등은 심혈관 질환의 위험을 높인다.\n\n라면을 즐길 때는 이러한 점들을 고려하여 적당한 양과 빈도로 섭취하는 것이 중요하다." + }, + { + "objectID": "slideshow/202404-sch/chatgpt-data-science.html#수능문제-풀이", + "href": "slideshow/202404-sch/chatgpt-data-science.html#수능문제-풀이", + "title": "생성형 AI 데이터 사이언스", + "section": "", + "text": "2023 수능 수학문제 풀이풀이 과정\n\n\n2023학년도 대학수학능력시험 수학영역 문제를 풀어보자.\n\n\n\n\n\\(\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\\) 값은?\n\n식 \\[\n\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n\\] 의 값은 \\(4\\)입니다.\n정확한 형태로는 \\(2^{(2 - \\sqrt{2}) (2 + \\sqrt{2})}\\)로 표현됩니다.\n자세한 정보는 Wolfram|Alpha 웹사이트에서 확인할 수 있다.\n\n\n\n과정도 설명해줘.\n\n\n\n물론이다. 주어진 식은 \\[\n\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n\\]이다. 이 식을 단계별로 분해해보자.\n\n단계 1: 분모를 간단하게 만들기 먼저, 분모인 \\(2^{\\sqrt{2}}\\)를 간단하게 만들기 위해 분자와 분모에 \\(2^{\\sqrt{2}}\\)를 곱한다. 이렇게 하면:\n\n\\[\n\\left(\\frac{4 \\times 2^{\\sqrt{2}}}{2^{\\sqrt{2}} \\times 2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n\\]\n\n단계 2: 분모를 합치기 이제 분모의 \\(2^{\\sqrt{2}} \\times 2^{\\sqrt{2}}\\)를 합쳐 \\(2^{2\\sqrt{2}}\\)로 만든다.\n\n\\[\n\\left(\\frac{4 \\times 2^{\\sqrt{2}}}{2^{2\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n\\]\n\n\n단계 3: 분자를 합치기 분자의 \\(4 \\times 2^{\\sqrt{2}}\\)를 합쳐 \\(2^2 \\times 2^{\\sqrt{2}} = 2^{2+\\sqrt{2}}\\)로 만든다.\n\n\\[\n\\left(\\frac{2^{2+\\sqrt{2}}}{2^{2\\sqrt{2}}}\\right)^{2+\\sqrt{2}}\n\\]\n\n단계 4: 지수 법칙 적용 지수 법칙에 따라 분자와 분모의 지수를 빼준다.\n\n\\[\n\\left(2^{(2+\\sqrt{2}) - 2\\sqrt{2}}\\right)^{2+\\sqrt{2}} = \\left(2^{(2 - \\sqrt{2})(2 + \\sqrt{2})}\\right)^{2+\\sqrt{2}}\n\\]\n\n단계 5: 최종 계산 마지막으로, \\((2 - \\sqrt{2})(2 + \\sqrt{2})\\)를 계산하면 \\(4 - 2 = 2\\)가 된다. 따라서 최종 결과는 \\(2^2 = 4\\)이다.\n\n이렇게 해서 \\[\n\\left(\\frac{4}{2^{\\sqrt{2}}}\\right)^{2+\\sqrt{2}} = 4\n\\]임을 확인할 수 있다.\n\n\n\n\n\n\n\nShareGPT: 팩트체크와 수학문제 풀이" + }, + { + "objectID": "slideshow/202404-sch/chatgpt-data-science.html#openai-advanced-data-analysis", + "href": "slideshow/202404-sch/chatgpt-data-science.html#openai-advanced-data-analysis", + "title": "생성형 AI 데이터 사이언스", + "section": "", + "text": "Open AI Code Interpreter → Advanced Data Analysis → 챗GPT4\n\n\n\n\n\n생성AI 광명시 데이터 분석 사례" + }, + { + "objectID": "slideshow/202404-sch/chatgpt-data-science.html#rstudio-copilot", + "href": 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separate(학습내용, into = c(\"주차\", \"학습내용\"), sep = \"\\\\.\") |> \n select(주차, 학습내용, 날짜) |> \n drop_na() |> \n mutate(날짜 = str_trim(날짜),\n 학습내용 = str_trim(학습내용)) |> \n mutate(날짜 = if_else(str_detect(날짜, \"^\\\\d\"), 날짜, NA_character_)) |> \n gt() |> \n gt_theme_pff() |> \n sub_missing(\n columns = 날짜,\n missing_text = \"-\"\n ) |> \n cols_align(\"center\") |> \n opt_table_font(\n font = list(\n google_font(name = \"Sunflower\"),\n \"NanumGothic\"\n )\n ) %>% \n tab_style(\n style = list(\n \"font-variant: small-caps;\"\n ),\n locations = cells_body(columns = everything())\n ) %>% \n tab_header(\n title =\n htmltools::tagList(\n htmltools::tags$div(\n style = htmltools::css(\n `text-align` = \"center\"\n ),\n htmltools::HTML(\n web_image(\"img/curriculum_yonsei.png\")\n )\n ),\n htmltools::tags$div(\n htmltools::tags$strong(\"연세대\"),\n \"데이터 사이언스 입문\"\n )\n )\n ) |> \n tab_options(\n table.width = \"65%\",\n heading.title.font.size = px(12)\n )\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n연세대 데이터 사이언스 입문\n\n\n\n주차\n학습내용\n날짜\n\n\n\n\n1\n데이터 과학 (Data Science)\n3월7일\n\n\n2\n프로그래밍 기초와 마크다운 보고서\n3월14일\n\n\n3\n데이터와 커뮤니티 찾기, 도움 청하기, 프로젝트 설정\n3월21일\n\n\n4\n데이터 수집\n-\n\n\n5\n자료구조(Data Structures)\n4월4일\n\n\n6\n데이터프레임과 SQL\n4월11일\n\n\n7\n버전제어, 협업, 그리고 저작권과 라이선스\n4월18일\n\n\n8\n중간고사\n4월25일\n\n\n9\n시각화 (Visualization)\n5월2일\n\n\n10\n정규표현식\n5월9일\n\n\n11\n데이터 과학 프로그래밍\n5월16일\n\n\n12\n데이터 과학 제품\n-\n\n\n13\n함수형 프로그래밍\n5월30일\n\n\n14\n데이터 과학 저작\n6월6일(현충일)\n\n\n15\nR 팩키지\n6월13일\n\n\n16\n기말고사\n6월20일\n\n\n\n\n\n\n\n\n\n연세대 “데이터 과학 입문”\n\n\n\n\nds2_url <- \"https://statkclee.github.io/yonsei2/\"\nds2_html <- read_html(ds2_url)\n\nds2_html |> \n html_elements('table') |> \n html_table() %>%\n .[[1]] |> \n slice(2:n()) |> \n separate(X2, into = c(\"학습내용\", \"날짜\"), sep = \"∽̱\") |> \n separate(학습내용, into = c(\"주차\", \"학습내용\"), sep = \"\\\\.\") |> \n select(주차, 학습내용, 날짜) |> \n drop_na() |> \n mutate(날짜 = str_trim(날짜),\n 학습내용 = str_trim(학습내용)) |> \n mutate(날짜 = if_else(str_detect(날짜, \"^\\\\d\"), 날짜, NA_character_)) |> \n gt() |> \n gt_theme_pff() |> \n sub_missing(\n columns = 날짜,\n missing_text = \"-\"\n ) |> \n cols_align(\"center\") |> \n opt_table_font(\n font = list(\n google_font(name = \"Sunflower\"),\n \"NanumGothic\"\n )\n ) %>% \n tab_style(\n style = list(\n \"font-variant: small-caps;\"\n ),\n locations = cells_body(columns = everything())\n ) %>% \n tab_header(\n title =\n htmltools::tagList(\n htmltools::tags$div(\n style = htmltools::css(\n `text-align` = \"center\"\n ),\n htmltools::HTML(\n web_image(\"img/curriculum_yonsei2.png\")\n )\n ),\n htmltools::tags$div(\n htmltools::tags$strong(\"연세대\"),\n \"데이터 (사이언스) 공학\"\n )\n )\n ) |> \n tab_options(\n table.width = \"65%\",\n heading.title.font.size = px(12)\n )\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n연세대 데이터 (사이언스) 공학\n\n\n\n주차\n학습내용\n날짜\n\n\n\n\n1\n데이터 사이언스 언어\n9월6일\n\n\n2\n[언어] R\n9월20일\n\n\n3\n[언어] 파이썬\n9월27일\n\n\n4\n[언어] API 프로그래밍\n10월4일\n\n\n5\n데이터 사이언스 자료구조\n10월11일\n\n\n6\n[자료구조] 시계열 데이터\n10월18일\n\n\n7\n중간고사\n10월25일\n\n\n8\n[자료구조] 지리 공간 데이터\n11월01일\n\n\n9\n[자료구조] NLP - 텍스트 마이닝(R)\n11월08일\n\n\n10\n[자료구조] NLP - 파이썬\n11월15일\n\n\n11\n빅데이터 시각화\n11월22일\n\n\n12\n기계학습 - 예측모형\n11월29일\n\n\n13\nDevOps - 도커, HPC, 스파크\n12월06일\n\n\n14\n제품화\n12월13일\n\n\n15\n기말고사\n12월20일\n\n\n\n\n\n\n\n\n\n연세대 “데이터 (사이언스) 공학”\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGS 칼텍스 시민 데이터 과학\n\n\n\n일정\n과정\n과정상세\n데이터셋\n\n\n\n\n1주차\nIntroduction\n\nDefining and solving problems- Quantifying performance indicators- Stats and Probability for Data Science\n\n-\n\n\n2주차\nData Science\n\nPython 기초 문법 복습 및 NumPy, Pandas 등 연습- Kaggle: MPG / Titanic EDA 개념과 Data science 전체흐름 습득- EDA, Feature engineering, Visualization, Correlating 등 preprocessing 작업- Model, predict and evaluation 의 전체 과정을 습득\n\n-\n\n\n3주차\nRegression and Prediction\n\nRegression에 대한 개념 및 이론 설명\n\nBreast Cancer, California housing prices, Bike-sharing\n\n\n3주차\nRegression and Prediction\n\nEDA 및 preprocessing- Linear Regression 중심, CART, SVM, Ensemble기법 적용- Training & Evaluation (MSE, MAE, RMSE, RMSLE, R-Squared, etc.)- Basic hyperparameter tuning 수행\n\n-\n\n\n4주차\nClassification and Hypothesis Testing\n\nClassification과 필요 개념 및 이론 설명\n\nTitanic, Iris, Penuins, San Francisco Crime\n\n\n4주차\nClassification and Hypothesis Testing\n\nLogistic Regression과 Decision Tree, SVC, Random Forest, XGBoost, LGBM 등 적용- Training & Evaluation / Basic hyperparameter tuning 수행\n\n-\n\n\n5주차\nCase Studies and Projects\n\n기존 수업 이론/실습 개념 바탕 Case Study 3종 (+Final Quiz)- Case Study 및 Final Quiz 해설- 개인 별 Project 인터뷰 및 Grouping / 공통과제 선정- Project 진행, 발표, Best Project 선정 및 공유\n\n-\n\n\n\n\n\n\n\n\n\nGS 칼텍스 데이터 사이언스\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGS 칼텍스 데이터 과학\n\n\n\n일정\n과목명\n학습상세\n데이터셋\n\n\n\n\n00주차\n환경설정\n학습 도구 설치 및 학습환경\n파머 펭귄, BitData\n\n\n01주차\n디지털 글쓰기(보고서)\n마크다운 / Quarto\nBitData\n\n\n02주차\n명령라인 데이터 분석\n자동화(Shell)\nSWC\n\n\n03주차\n버전 제어\n버전제어/협업(Git / GitHub / GitLab)\n\n\n\n04주차\n프로그래밍\nSQL\nData Carpentry, DVD 렌탈\n\n\n05주차\n프로그래밍\n시각화\ngapminder\n\n\n06주차\n프로그래밍\n파이썬\n\n\n\n07주차\n대쉬보드\nFlexdashboard / Shinydashboard\n\n\n\n08주차\n기계학습\ntidymodels / scikit-learn\n\n\n\n09주차\n데이터 과학 제품\nRESTful API\n\n\n\n10주차\n특수 데이터\n시계열(Time Series)\nKOSPI\n\n\n11주차\n특수 데이터\n공간정보(Geospatial) / 텍스트 분석\n미디어오늘\n\n\n12주차\n비정형 데이터\n텍스트와 이미지 (딥러닝)\n\n\n\n\n\n\n\n\n\n\nGS 칼텍스 데이터 사이언스\n\n\n\n\nlibrary(tidyverse)\nlibrary(readxl)\nlibrary(gt)\nlibrary(gtExtras)\nextrafont::loadfonts()\n\ncurriculum_raw <- read_excel(glue::glue(\"{here::here()}/data/건국대_커리큘럼.xlsx\"))\n\ncurriculum <- curriculum_raw %>% \n fill(everything(), .direction=\"down\") %>% \n mutate(날짜 = as.Date(날짜)) %>% \n mutate(주차 = glue::glue(\"{주차} 주차\"))\n\n## 첫번째\ncurriculum_first <- curriculum %>% \n filter(dplyr::row_number() <=10)\n\ncurriculum_first_gt <- curriculum_first %>% \n gt(groupname_col = c(\"주차\", \"날짜\")) %>% \n cols_align(align = \"center\") %>% \n opt_table_font(\n font = list(\n google_font(name = \"Sunflower\"),\n \"NanumGothic\"\n )\n ) %>% \n gt_theme_pff() %>% \n tab_style(\n style = list(\n \"font-variant: small-caps;\"\n ),\n locations = cells_body(columns = everything())\n ) %>% \n tab_header(\n title =\n htmltools::tagList(\n htmltools::tags$div(\n style = htmltools::css(\n `text-align` = \"center\"\n ),\n htmltools::HTML(\n web_image(\"img/konkuk/tools.png\")\n )\n ),\n htmltools::tags$div(\n htmltools::tags$strong(\"챗GPT 데이터 사이언스\"),\n \"건국대 전문교육과정\"\n )\n )\n ) \n\n## 두번째\ncurriculum_second <- curriculum %>% \n filter(dplyr::row_number() > 10)\n\ncurriculum_second_gt <- curriculum_second %>% \n gt(groupname_col = c(\"주차\", \"날짜\")) %>% \n cols_align(align = \"center\") %>% \n opt_table_font(\n font = list(\n google_font(name = \"Sunflower\"),\n \"NanumGothic\"\n )\n ) %>% \n gt_theme_pff() %>% \n tab_style(\n style = list(\n \"font-variant: small-caps;\"\n ),\n locations = cells_body(columns = everything())\n ) \n\n## 결합\n\nlisted_tables <- list(curriculum_first_gt, curriculum_second_gt)\n\ngt_two_column_layout(listed_tables)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 데이터 사이언스 건국대 전문교육과정\n\n\n\n강의주제\n강의내용\n수업방법\n\n\n\n\n1 주차 - 2023-07-01\n\n\n데이터 사이언스 언어\n챗GPT와 데이터 사이언스\n핸즈온 실습\n\n\n데이터 사이언스 언어\nTidyverse와 챗GPT\n핸즈온 실습\n\n\n데이터 사이언스 언어\n파이썬/R/SQL 그리고 API\n핸즈온 실습\n\n\n2 주차 - 2023-07-08\n\n\n소프트웨어 카펜트리 (기본기)\n자동화: 유닉스 쉘\n핸즈온 실습\n\n\n소프트웨어 카펜트리 (기본기)\n버전제어: Git / GitHub\n핸즈온 실습\n\n\n3 주차 - 2023-07-15\n\n\n소프트웨어 카펜트리 (기본기)\n파이썬 / R\n핸즈온 실습\n\n\n소프트웨어 카펜트리 (기본기)\n데이터베이스\n핸즈온 실습\n\n\n4 주차 - 2023-07-22\n\n\n데이터 과학 자료구조\n숫자/범주 --> 데이터프레임\n핸즈온 실습\n\n\n데이터 과학 자료구조\n날짜/공간정보/네트워크\n핸즈온 실습\n\n\n데이터 과학 자료구조\n텍스트/이미지/오디오\n핸즈온 실습\n\n\n\n\n\n\n\n\n\n\n\n강의주제\n강의내용\n수업방법\n\n\n\n\n5 주차 - 2023-07-29\n\n\n디지털 글쓰기, 대쉬보드, API 개발\n디지털 보고서(PDF, HTML, PPT)\n핸즈온 실습\n\n\n디지털 글쓰기, 대쉬보드, API 개발\nServerless 대쉬보드, R/Python 대쉬보드\n핸즈온 실습\n\n\n디지털 글쓰기, 대쉬보드, API 개발\n기계학습 모형 API 배포\n핸즈온 실습\n\n\n- 주차 - 2023-08-05\n\n\n휴가\n휴가\n-\n\n\n6 주차 - 2023-08-12\n\n\n챗GPT와 오픈소스\n(오픈소스) 거대언어모형(LLM)\n핸즈온 실습\n\n\n챗GPT와 오픈소스\nWrite/Draw/Coding/Reasoning\n핸즈온 실습\n\n\n챗GPT와 오픈소스\nAI 시대 데이터 사이언스 제품개발 방법\n핸즈온 실습\n\n\n7 주차 - 2023-08-19\n\n\nPrompt Engineering\n사용자 및 개발자를 위한 프롬프트 공학\n핸즈온 실습\n\n\nPrompt Engineering\nOpenAI API 기반 NLP 개발\n핸즈온 실습\n\n\nPrompt Engineering\nOPL(OpenAI, Pinecone, Langchain) 프레임워크\n핸즈온 실습\n\n\n8 주차 - 2023-08-26\n\n\n챗GPT AI 앱 프로젝트\n오픈소스 LLM 기반 AI 데이터 분석 제품\n핸즈온 실습\n\n\n챗GPT AI 앱 프로젝트\n데이터 사이언스 AutoGPT\n핸즈온 실습\n\n\n챗GPT AI 앱 프로젝트\n챗GPT AI 프로젝트 발표\n핸즈온 실습\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n데이터 사이언스 MCQ" + }, + { + "objectID": "slideshow/202404-sch/curriculum.html#교육과정", + "href": "slideshow/202404-sch/curriculum.html#교육과정", + "title": "통계 → 데이터 사이언스 → 챗GPT 인공지능", + "section": "", + "text": "종이와 연필통계 보고서DS 입문고급 DSGS 칼텍스GS 칼텍스챗GPT DS평가\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nlibrary(tidyverse)\nlibrary(rvest)\n\nlibrary(gt)\nlibrary(gtExtras)\n\nds_url <- \"https://statkclee.github.io/yonsei/\"\nds_html <- read_html(ds_url)\n\nds_html |> \n html_elements('table') |> \n html_table() %>%\n .[[1]] |> \n slice(2:n()) |> \n separate(X2, into = c(\"학습내용\", \"날짜\"), sep = \"-\") |> \n separate(학습내용, into = c(\"주차\", \"학습내용\"), sep = \"\\\\.\") |> \n select(주차, 학습내용, 날짜) |> \n drop_na() |> \n mutate(날짜 = str_trim(날짜),\n 학습내용 = str_trim(학습내용)) |> \n mutate(날짜 = if_else(str_detect(날짜, \"^\\\\d\"), 날짜, NA_character_)) |> \n gt() |> \n gt_theme_pff() |> \n sub_missing(\n columns = 날짜,\n missing_text = \"-\"\n ) |> \n cols_align(\"center\") |> \n opt_table_font(\n font = list(\n google_font(name = \"Sunflower\"),\n \"NanumGothic\"\n )\n ) %>% \n tab_style(\n style = list(\n \"font-variant: small-caps;\"\n ),\n locations = cells_body(columns = everything())\n ) %>% \n tab_header(\n title =\n htmltools::tagList(\n htmltools::tags$div(\n style = htmltools::css(\n `text-align` = \"center\"\n ),\n htmltools::HTML(\n web_image(\"img/curriculum_yonsei.png\")\n )\n ),\n htmltools::tags$div(\n htmltools::tags$strong(\"연세대\"),\n \"데이터 사이언스 입문\"\n )\n )\n ) |> \n tab_options(\n table.width = \"65%\",\n heading.title.font.size = px(12)\n )\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n연세대 데이터 사이언스 입문\n\n\n\n주차\n학습내용\n날짜\n\n\n\n\n1\n데이터 과학 (Data Science)\n3월7일\n\n\n2\n프로그래밍 기초와 마크다운 보고서\n3월14일\n\n\n3\n데이터와 커뮤니티 찾기, 도움 청하기, 프로젝트 설정\n3월21일\n\n\n4\n데이터 수집\n-\n\n\n5\n자료구조(Data Structures)\n4월4일\n\n\n6\n데이터프레임과 SQL\n4월11일\n\n\n7\n버전제어, 협업, 그리고 저작권과 라이선스\n4월18일\n\n\n8\n중간고사\n4월25일\n\n\n9\n시각화 (Visualization)\n5월2일\n\n\n10\n정규표현식\n5월9일\n\n\n11\n데이터 과학 프로그래밍\n5월16일\n\n\n12\n데이터 과학 제품\n-\n\n\n13\n함수형 프로그래밍\n5월30일\n\n\n14\n데이터 과학 저작\n6월6일(현충일)\n\n\n15\nR 팩키지\n6월13일\n\n\n16\n기말고사\n6월20일\n\n\n\n\n\n\n\n\n\n연세대 “데이터 과학 입문”\n\n\n\n\nds2_url <- \"https://statkclee.github.io/yonsei2/\"\nds2_html <- read_html(ds2_url)\n\nds2_html |> \n html_elements('table') |> \n html_table() %>%\n .[[1]] |> \n slice(2:n()) |> \n separate(X2, into = c(\"학습내용\", \"날짜\"), sep = \"∽̱\") |> \n separate(학습내용, into = c(\"주차\", \"학습내용\"), sep = \"\\\\.\") |> \n select(주차, 학습내용, 날짜) |> \n drop_na() |> \n mutate(날짜 = str_trim(날짜),\n 학습내용 = str_trim(학습내용)) |> \n mutate(날짜 = if_else(str_detect(날짜, \"^\\\\d\"), 날짜, NA_character_)) |> \n gt() |> \n gt_theme_pff() |> \n sub_missing(\n columns = 날짜,\n missing_text = \"-\"\n ) |> \n cols_align(\"center\") |> \n opt_table_font(\n font = list(\n google_font(name = \"Sunflower\"),\n \"NanumGothic\"\n )\n ) %>% \n tab_style(\n style = list(\n \"font-variant: small-caps;\"\n ),\n locations = cells_body(columns = everything())\n ) %>% \n tab_header(\n title =\n htmltools::tagList(\n htmltools::tags$div(\n style = htmltools::css(\n `text-align` = \"center\"\n ),\n htmltools::HTML(\n web_image(\"img/curriculum_yonsei2.png\")\n )\n ),\n htmltools::tags$div(\n htmltools::tags$strong(\"연세대\"),\n \"데이터 (사이언스) 공학\"\n )\n )\n ) |> \n tab_options(\n table.width = \"65%\",\n heading.title.font.size = px(12)\n )\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n연세대 데이터 (사이언스) 공학\n\n\n\n주차\n학습내용\n날짜\n\n\n\n\n1\n데이터 사이언스 언어\n9월6일\n\n\n2\n[언어] R\n9월20일\n\n\n3\n[언어] 파이썬\n9월27일\n\n\n4\n[언어] API 프로그래밍\n10월4일\n\n\n5\n데이터 사이언스 자료구조\n10월11일\n\n\n6\n[자료구조] 시계열 데이터\n10월18일\n\n\n7\n중간고사\n10월25일\n\n\n8\n[자료구조] 지리 공간 데이터\n11월01일\n\n\n9\n[자료구조] NLP - 텍스트 마이닝(R)\n11월08일\n\n\n10\n[자료구조] NLP - 파이썬\n11월15일\n\n\n11\n빅데이터 시각화\n11월22일\n\n\n12\n기계학습 - 예측모형\n11월29일\n\n\n13\nDevOps - 도커, HPC, 스파크\n12월06일\n\n\n14\n제품화\n12월13일\n\n\n15\n기말고사\n12월20일\n\n\n\n\n\n\n\n\n\n연세대 “데이터 (사이언스) 공학”\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGS 칼텍스 시민 데이터 과학\n\n\n\n일정\n과정\n과정상세\n데이터셋\n\n\n\n\n1주차\nIntroduction\n\nDefining and solving problems- Quantifying performance indicators- Stats and Probability for Data Science\n\n-\n\n\n2주차\nData Science\n\nPython 기초 문법 복습 및 NumPy, Pandas 등 연습- Kaggle: MPG / Titanic EDA 개념과 Data science 전체흐름 습득- EDA, Feature engineering, Visualization, Correlating 등 preprocessing 작업- Model, predict and evaluation 의 전체 과정을 습득\n\n-\n\n\n3주차\nRegression and Prediction\n\nRegression에 대한 개념 및 이론 설명\n\nBreast Cancer, California housing prices, Bike-sharing\n\n\n3주차\nRegression and Prediction\n\nEDA 및 preprocessing- Linear Regression 중심, CART, SVM, Ensemble기법 적용- Training & Evaluation (MSE, MAE, RMSE, RMSLE, R-Squared, etc.)- Basic hyperparameter tuning 수행\n\n-\n\n\n4주차\nClassification and Hypothesis Testing\n\nClassification과 필요 개념 및 이론 설명\n\nTitanic, Iris, Penuins, San Francisco Crime\n\n\n4주차\nClassification and Hypothesis Testing\n\nLogistic Regression과 Decision Tree, SVC, Random Forest, XGBoost, LGBM 등 적용- Training & Evaluation / Basic hyperparameter tuning 수행\n\n-\n\n\n5주차\nCase Studies and Projects\n\n기존 수업 이론/실습 개념 바탕 Case Study 3종 (+Final Quiz)- Case Study 및 Final Quiz 해설- 개인 별 Project 인터뷰 및 Grouping / 공통과제 선정- Project 진행, 발표, Best Project 선정 및 공유\n\n-\n\n\n\n\n\n\n\n\n\nGS 칼텍스 데이터 사이언스\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGS 칼텍스 데이터 과학\n\n\n\n일정\n과목명\n학습상세\n데이터셋\n\n\n\n\n00주차\n환경설정\n학습 도구 설치 및 학습환경\n파머 펭귄, BitData\n\n\n01주차\n디지털 글쓰기(보고서)\n마크다운 / Quarto\nBitData\n\n\n02주차\n명령라인 데이터 분석\n자동화(Shell)\nSWC\n\n\n03주차\n버전 제어\n버전제어/협업(Git / GitHub / GitLab)\n\n\n\n04주차\n프로그래밍\nSQL\nData Carpentry, DVD 렌탈\n\n\n05주차\n프로그래밍\n시각화\ngapminder\n\n\n06주차\n프로그래밍\n파이썬\n\n\n\n07주차\n대쉬보드\nFlexdashboard / Shinydashboard\n\n\n\n08주차\n기계학습\ntidymodels / scikit-learn\n\n\n\n09주차\n데이터 과학 제품\nRESTful API\n\n\n\n10주차\n특수 데이터\n시계열(Time Series)\nKOSPI\n\n\n11주차\n특수 데이터\n공간정보(Geospatial) / 텍스트 분석\n미디어오늘\n\n\n12주차\n비정형 데이터\n텍스트와 이미지 (딥러닝)\n\n\n\n\n\n\n\n\n\n\nGS 칼텍스 데이터 사이언스\n\n\n\n\nlibrary(tidyverse)\nlibrary(readxl)\nlibrary(gt)\nlibrary(gtExtras)\nextrafont::loadfonts()\n\ncurriculum_raw <- read_excel(glue::glue(\"{here::here()}/data/건국대_커리큘럼.xlsx\"))\n\ncurriculum <- curriculum_raw %>% \n fill(everything(), .direction=\"down\") %>% \n mutate(날짜 = as.Date(날짜)) %>% \n mutate(주차 = glue::glue(\"{주차} 주차\"))\n\n## 첫번째\ncurriculum_first <- curriculum %>% \n filter(dplyr::row_number() <=10)\n\ncurriculum_first_gt <- curriculum_first %>% \n gt(groupname_col = c(\"주차\", \"날짜\")) %>% \n cols_align(align = \"center\") %>% \n opt_table_font(\n font = list(\n google_font(name = \"Sunflower\"),\n \"NanumGothic\"\n )\n ) %>% \n gt_theme_pff() %>% \n tab_style(\n style = list(\n \"font-variant: small-caps;\"\n ),\n locations = cells_body(columns = everything())\n ) %>% \n tab_header(\n title =\n htmltools::tagList(\n htmltools::tags$div(\n style = htmltools::css(\n `text-align` = \"center\"\n ),\n htmltools::HTML(\n web_image(\"img/konkuk/tools.png\")\n )\n ),\n htmltools::tags$div(\n htmltools::tags$strong(\"챗GPT 데이터 사이언스\"),\n \"건국대 전문교육과정\"\n )\n )\n ) \n\n## 두번째\ncurriculum_second <- curriculum %>% \n filter(dplyr::row_number() > 10)\n\ncurriculum_second_gt <- curriculum_second %>% \n gt(groupname_col = c(\"주차\", \"날짜\")) %>% \n cols_align(align = \"center\") %>% \n opt_table_font(\n font = list(\n google_font(name = \"Sunflower\"),\n \"NanumGothic\"\n )\n ) %>% \n gt_theme_pff() %>% \n tab_style(\n style = list(\n \"font-variant: small-caps;\"\n ),\n locations = cells_body(columns = everything())\n ) \n\n## 결합\n\nlisted_tables <- list(curriculum_first_gt, curriculum_second_gt)\n\ngt_two_column_layout(listed_tables)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 데이터 사이언스 건국대 전문교육과정\n\n\n\n강의주제\n강의내용\n수업방법\n\n\n\n\n1 주차 - 2023-07-01\n\n\n데이터 사이언스 언어\n챗GPT와 데이터 사이언스\n핸즈온 실습\n\n\n데이터 사이언스 언어\nTidyverse와 챗GPT\n핸즈온 실습\n\n\n데이터 사이언스 언어\n파이썬/R/SQL 그리고 API\n핸즈온 실습\n\n\n2 주차 - 2023-07-08\n\n\n소프트웨어 카펜트리 (기본기)\n자동화: 유닉스 쉘\n핸즈온 실습\n\n\n소프트웨어 카펜트리 (기본기)\n버전제어: Git / GitHub\n핸즈온 실습\n\n\n3 주차 - 2023-07-15\n\n\n소프트웨어 카펜트리 (기본기)\n파이썬 / R\n핸즈온 실습\n\n\n소프트웨어 카펜트리 (기본기)\n데이터베이스\n핸즈온 실습\n\n\n4 주차 - 2023-07-22\n\n\n데이터 과학 자료구조\n숫자/범주 --> 데이터프레임\n핸즈온 실습\n\n\n데이터 과학 자료구조\n날짜/공간정보/네트워크\n핸즈온 실습\n\n\n데이터 과학 자료구조\n텍스트/이미지/오디오\n핸즈온 실습\n\n\n\n\n\n\n\n\n\n\n\n강의주제\n강의내용\n수업방법\n\n\n\n\n5 주차 - 2023-07-29\n\n\n디지털 글쓰기, 대쉬보드, API 개발\n디지털 보고서(PDF, HTML, PPT)\n핸즈온 실습\n\n\n디지털 글쓰기, 대쉬보드, API 개발\nServerless 대쉬보드, R/Python 대쉬보드\n핸즈온 실습\n\n\n디지털 글쓰기, 대쉬보드, API 개발\n기계학습 모형 API 배포\n핸즈온 실습\n\n\n- 주차 - 2023-08-05\n\n\n휴가\n휴가\n-\n\n\n6 주차 - 2023-08-12\n\n\n챗GPT와 오픈소스\n(오픈소스) 거대언어모형(LLM)\n핸즈온 실습\n\n\n챗GPT와 오픈소스\nWrite/Draw/Coding/Reasoning\n핸즈온 실습\n\n\n챗GPT와 오픈소스\nAI 시대 데이터 사이언스 제품개발 방법\n핸즈온 실습\n\n\n7 주차 - 2023-08-19\n\n\nPrompt Engineering\n사용자 및 개발자를 위한 프롬프트 공학\n핸즈온 실습\n\n\nPrompt Engineering\nOpenAI API 기반 NLP 개발\n핸즈온 실습\n\n\nPrompt Engineering\nOPL(OpenAI, Pinecone, Langchain) 프레임워크\n핸즈온 실습\n\n\n8 주차 - 2023-08-26\n\n\n챗GPT AI 앱 프로젝트\n오픈소스 LLM 기반 AI 데이터 분석 제품\n핸즈온 실습\n\n\n챗GPT AI 앱 프로젝트\n데이터 사이언스 AutoGPT\n핸즈온 실습\n\n\n챗GPT AI 앱 프로젝트\n챗GPT AI 프로젝트 발표\n핸즈온 실습\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n데이터 사이언스 MCQ" + }, + { + "objectID": "slideshow/202404-sch/office-interface.html", + "href": "slideshow/202404-sch/office-interface.html", + "title": "글쓰기 챗GPT 인터페이스 - 오피스 전쟁", + "section": "", + "text": "문자와 종이 역사\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n항목\nMS 오피스 1\n구글 오피스 2\n리브레 오피스 3\n한컴 오피스 4\n\n\n\n\n문서 작성 및 편집\n워드, 엑셀, 파워포인트 등의 데스크톱 및 웹 앱 제공\n구글 문서, 스프레드시트, 프레젠테이션 등의 웹 앱 제공\n라이터, 캘크, 임프레스 등의 데스크톱 앱 제공\n한글, 한셀, 한쇼 등의 데스크톱 및 모바일 앱 제공\n\n\n협업 및 공유\n원드라이브를 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움\n구글 드라이브를 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정하고 실시간으로 확인 가능\n드롭박스나 구글 드라이브와 연동하여 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움\n넷프리즘을 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움\n\n\n이메일 및 채팅\nOutlook을 통해 비즈니스 메일 제공. Teams을 통해 채팅 및 화상 회의 가능\nGmail을 통해 비즈니스 메일 제공. Chat과 Meet을 통해 채팅 및 화상 회의 가능\n이메일 및 채팅 기능 없음\n이메일 및 채팅 기능 없음\n\n\n가격\n1인당 월 5~20달러 다양한 요금제 제공. 데스크톱 앱 사용 가능한 요금제는 12.5달러부터 시작\n1인당 월 6~18달러까지 다양한 요금제 제공\n완전 무료\n개인용은 무료로 사용 가능하며 업무용은 별도로 견적 요청 필요\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 사용원인인터페이스챗GPT AS-IS챗GPT TO-BE\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n진화과정CLIGUIWUIMUILUI\n\n\n \n\n\n\n\n\nflowchart TB\n \n subgraph A[\"사용자 인터페이스\"]\n direction LR\n CLI[\"CLI<br>Command Line Interface<br> 1950~\"] --> GUI[\"GUI <br> Graphic User Inferface<br> 1970~\"]\n GUI --> WI[\"Web Interface <br> 1994~\"]\n WI --> MI[\"Mobile Interface <br> 2007~\"]\n MI --> NUI[\"LUI <br> Language User Interface <br> 2023~\"]\n\n end\n\n\nclass A nodeStyle\n\nclassDef nodeStyle fill:#fcfbfa,stroke:#000000,stroke-width:0.7px,font-weight:bold,font-size:14px;\n\n\n\n\n\n\n\n\n\n\n\nhttps://en.wikipedia.org/wiki/Computer_terminal\n\n\n\n\n\n\nhttps://kartsci.org/kocomu/computer-history/graphical-user-interface-history/\n\n\n\n \n\n\n\n\n\nhttps://www.huffpost.com/entry/uber-your-way-through-cit_b_1205446\n\n\n\n\n\nhttps://twitter.com/sama/status/1515764302904377344\n\n\n\n\n\n\n\n\n1:1 채팅클릭하여 완료자동완성명령 팔레트\n\n\n\n챗GPT (ChatGPT)\n\n\n\n\n\n\n\n\n\nOpenAI Playground\n\n\n\n\n\n\n\n\n\nGitHub Copilot\n\n\n\n\n\n\n\nhttps://towardsdatascience.com/some-experiments-using-github-copilot-with-python-90f8065fb72e\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nhttps://blog.replit.com/clui\nhttps://blog.replit.com/ai\n\n\n\n\n\n\n60년대로 거슬러 올라가는 명령 팔레트는 최근 기술 업계에서 다시 인기를 얻고 있으며 사용자 중심 소프트웨어의 필수적인 부분으로 간주됨.\nCLI는 개인용 컴퓨터가 대중화되기 전 초기 컴퓨팅을 지배했으며 사용자가 시스템을 작동하기 위해 특정 명령을 알아야 했다. 1984년 Apple의 매킨토시와 함께 도입된 그래픽 사용자 인터페이스(GUI)는 중요한 혁신으로 여겨졌지만, 소프트웨어의 복잡성이 증가함에 따라 GUI만으로는 탐색하기가 점점 더 어려워졌다.\n사용자가 항목 이름의 짧은 약어입력하여 앱과 파일을 검색할 수 있는 런처가 점점 더 보편화 되었다. 예를 들어 2001년에 출시된 Mac OS X용 LaunchBar, Quicksilver, 2005년에 출시된 Apple의 시스템 전체 Spotlight가 있다. 검색과 CLI의 결합은 Command Palette의 탄생으로 이어졌다.\n오늘날 우리가 알고 있는 현대식 명령 팔레트는 2011년 Sublime Text 2 베타 Joe Skinner가 처음 소개. 명령줄 사용에 익숙한 프로그래머들이 단축키를 모두 외울 필요 없이 더 빠르게 작업할 수 있어 중요한 발전이 됨." + }, + { + "objectID": "slideshow/202404-sch/office-interface.html#글쓰기-역사", + "href": "slideshow/202404-sch/office-interface.html#글쓰기-역사", + "title": "글쓰기 챗GPT 인터페이스 - 오피스 전쟁", + "section": "", + "text": "문자와 종이 역사" + }, + { + "objectID": "slideshow/202404-sch/office-interface.html#오피스-전쟁", + "href": "slideshow/202404-sch/office-interface.html#오피스-전쟁", + "title": "글쓰기 챗GPT 인터페이스 - 오피스 전쟁", + "section": "", + "text": "항목\nMS 오피스 1\n구글 오피스 2\n리브레 오피스 3\n한컴 오피스 4\n\n\n\n\n문서 작성 및 편집\n워드, 엑셀, 파워포인트 등의 데스크톱 및 웹 앱 제공\n구글 문서, 스프레드시트, 프레젠테이션 등의 웹 앱 제공\n라이터, 캘크, 임프레스 등의 데스크톱 앱 제공\n한글, 한셀, 한쇼 등의 데스크톱 및 모바일 앱 제공\n\n\n협업 및 공유\n원드라이브를 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움\n구글 드라이브를 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정하고 실시간으로 확인 가능\n드롭박스나 구글 드라이브와 연동하여 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움\n넷프리즘을 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움\n\n\n이메일 및 채팅\nOutlook을 통해 비즈니스 메일 제공. Teams을 통해 채팅 및 화상 회의 가능\nGmail을 통해 비즈니스 메일 제공. Chat과 Meet을 통해 채팅 및 화상 회의 가능\n이메일 및 채팅 기능 없음\n이메일 및 채팅 기능 없음\n\n\n가격\n1인당 월 5~20달러 다양한 요금제 제공. 데스크톱 앱 사용 가능한 요금제는 12.5달러부터 시작\n1인당 월 6~18달러까지 다양한 요금제 제공\n완전 무료\n개인용은 무료로 사용 가능하며 업무용은 별도로 견적 요청 필요" + }, + { + "objectID": "slideshow/202404-sch/office-interface.html#챗gpt-시대-사무실", + "href": "slideshow/202404-sch/office-interface.html#챗gpt-시대-사무실", + "title": "글쓰기 챗GPT 인터페이스 - 오피스 전쟁", + "section": "", + "text": "챗GPT 사용원인인터페이스챗GPT AS-IS챗GPT TO-BE" + }, + { + "objectID": "slideshow/202404-sch/office-interface.html#인터페이스-interface", + "href": "slideshow/202404-sch/office-interface.html#인터페이스-interface", + "title": "글쓰기 챗GPT 인터페이스 - 오피스 전쟁", + "section": "", + "text": "진화과정CLIGUIWUIMUILUI\n\n\n \n\n\n\n\n\nflowchart TB\n \n subgraph A[\"사용자 인터페이스\"]\n direction LR\n CLI[\"CLI<br>Command Line Interface<br> 1950~\"] --> GUI[\"GUI <br> Graphic User Inferface<br> 1970~\"]\n GUI --> WI[\"Web Interface <br> 1994~\"]\n WI --> MI[\"Mobile Interface <br> 2007~\"]\n MI --> NUI[\"LUI <br> Language User Interface <br> 2023~\"]\n\n end\n\n\nclass A nodeStyle\n\nclassDef nodeStyle fill:#fcfbfa,stroke:#000000,stroke-width:0.7px,font-weight:bold,font-size:14px;\n\n\n\n\n\n\n\n\n\n\n\nhttps://en.wikipedia.org/wiki/Computer_terminal\n\n\n\n\n\n\nhttps://kartsci.org/kocomu/computer-history/graphical-user-interface-history/\n\n\n\n \n\n\n\n\n\nhttps://www.huffpost.com/entry/uber-your-way-through-cit_b_1205446\n\n\n\n\n\nhttps://twitter.com/sama/status/1515764302904377344" + }, + { + "objectID": "slideshow/202404-sch/office-interface.html#챗gpt-nui", + "href": "slideshow/202404-sch/office-interface.html#챗gpt-nui", + "title": "글쓰기 챗GPT 인터페이스 - 오피스 전쟁", + "section": "", + "text": "1:1 채팅클릭하여 완료자동완성명령 팔레트\n\n\n\n챗GPT (ChatGPT)\n\n\n\n\n\n\n\n\n\nOpenAI Playground\n\n\n\n\n\n\n\n\n\nGitHub Copilot\n\n\n\n\n\n\n\nhttps://towardsdatascience.com/some-experiments-using-github-copilot-with-python-90f8065fb72e\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nhttps://blog.replit.com/clui\nhttps://blog.replit.com/ai\n\n\n\n\n\n\n60년대로 거슬러 올라가는 명령 팔레트는 최근 기술 업계에서 다시 인기를 얻고 있으며 사용자 중심 소프트웨어의 필수적인 부분으로 간주됨.\nCLI는 개인용 컴퓨터가 대중화되기 전 초기 컴퓨팅을 지배했으며 사용자가 시스템을 작동하기 위해 특정 명령을 알아야 했다. 1984년 Apple의 매킨토시와 함께 도입된 그래픽 사용자 인터페이스(GUI)는 중요한 혁신으로 여겨졌지만, 소프트웨어의 복잡성이 증가함에 따라 GUI만으로는 탐색하기가 점점 더 어려워졌다.\n사용자가 항목 이름의 짧은 약어입력하여 앱과 파일을 검색할 수 있는 런처가 점점 더 보편화 되었다. 예를 들어 2001년에 출시된 Mac OS X용 LaunchBar, Quicksilver, 2005년에 출시된 Apple의 시스템 전체 Spotlight가 있다. 검색과 CLI의 결합은 Command Palette의 탄생으로 이어졌다.\n오늘날 우리가 알고 있는 현대식 명령 팔레트는 2011년 Sublime Text 2 베타 Joe Skinner가 처음 소개. 명령줄 사용에 익숙한 프로그래머들이 단축키를 모두 외울 필요 없이 더 빠르게 작업할 수 있어 중요한 발전이 됨." + }, + { + "objectID": "slideshow/202404-sch/office-interface.html#footnotes", + "href": "slideshow/202404-sch/office-interface.html#footnotes", + "title": "글쓰기 챗GPT 인터페이스 - 오피스 전쟁", + "section": "각주", + "text": "각주\n\n\nhttps://www.microsoft.com/ko-kr/microsoft-365/business↩︎\nhttps://workspace.google.com/intl/ko_kr/↩︎\nhttps://www.libreoffice.org/↩︎\nhttps://www.hancom.com/product/productMain.do↩︎" + }, + { + "objectID": "slideshow/202404-sch/reference.html", + "href": "slideshow/202404-sch/reference.html", + "title": "참고문헌", + "section": "", + "text": "참고문헌" + }, + { + "objectID": "chatgpt_sch_202404.html#교육과정", + "href": "chatgpt_sch_202404.html#교육과정", + "title": "챗GPT 데이터 사이언스", + "section": "교육과정", + "text": "교육과정\n\n종이와 연필통계 보고서DS 입문고급 DSGS 칼텍스GS 칼텍스챗GPT DS평가\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n연세대 데이터 사이언스 입문\n\n\n\n주차\n학습내용\n날짜\n\n\n\n\n1\n데이터 과학 (Data Science)\n3월7일\n\n\n2\n프로그래밍 기초와 마크다운 보고서\n3월14일\n\n\n3\n데이터와 커뮤니티 찾기, 도움 청하기, 프로젝트 설정\n3월21일\n\n\n4\n데이터 수집\n-\n\n\n5\n자료구조(Data Structures)\n4월4일\n\n\n6\n데이터프레임과 SQL\n4월11일\n\n\n7\n버전제어, 협업, 그리고 저작권과 라이선스\n4월18일\n\n\n8\n중간고사\n4월25일\n\n\n9\n시각화 (Visualization)\n5월2일\n\n\n10\n정규표현식\n5월9일\n\n\n11\n데이터 과학 프로그래밍\n5월16일\n\n\n12\n데이터 과학 제품\n-\n\n\n13\n함수형 프로그래밍\n5월30일\n\n\n14\n데이터 과학 저작\n6월6일(현충일)\n\n\n15\nR 팩키지\n6월13일\n\n\n16\n기말고사\n6월20일\n\n\n\n\n\n\n\n\n\n연세대 “데이터 과학 입문”\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n연세대 데이터 (사이언스) 공학\n\n\n\n주차\n학습내용\n날짜\n\n\n\n\n1\n데이터 사이언스 언어\n9월6일\n\n\n2\n[언어] R\n9월20일\n\n\n3\n[언어] 파이썬\n9월27일\n\n\n4\n[언어] API 프로그래밍\n10월4일\n\n\n5\n데이터 사이언스 자료구조\n10월11일\n\n\n6\n[자료구조] 시계열 데이터\n10월18일\n\n\n7\n중간고사\n10월25일\n\n\n8\n[자료구조] 지리 공간 데이터\n11월01일\n\n\n9\n[자료구조] NLP - 텍스트 마이닝(R)\n11월08일\n\n\n10\n[자료구조] NLP - 파이썬\n11월15일\n\n\n11\n빅데이터 시각화\n11월22일\n\n\n12\n기계학습 - 예측모형\n11월29일\n\n\n13\nDevOps - 도커, HPC, 스파크\n12월06일\n\n\n14\n제품화\n12월13일\n\n\n15\n기말고사\n12월20일\n\n\n\n\n\n\n\n\n\n연세대 “데이터 (사이언스) 공학”\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGS 칼텍스 시민 데이터 과학\n\n\n\n일정\n과정\n과정상세\n데이터셋\n\n\n\n\n1주차\nIntroduction\n\nDefining and solving problems- Quantifying performance indicators- Stats and Probability for Data Science\n\n-\n\n\n2주차\nData Science\n\nPython 기초 문법 복습 및 NumPy, Pandas 등 연습- Kaggle: MPG / Titanic EDA 개념과 Data science 전체흐름 습득- EDA, Feature engineering, Visualization, Correlating 등 preprocessing 작업- Model, predict and evaluation 의 전체 과정을 습득\n\n-\n\n\n3주차\nRegression and Prediction\n\nRegression에 대한 개념 및 이론 설명\n\nBreast Cancer, California housing prices, Bike-sharing\n\n\n3주차\nRegression and Prediction\n\nEDA 및 preprocessing- Linear Regression 중심, CART, SVM, Ensemble기법 적용- Training & Evaluation (MSE, MAE, RMSE, RMSLE, R-Squared, etc.)- Basic hyperparameter tuning 수행\n\n-\n\n\n4주차\nClassification and Hypothesis Testing\n\nClassification과 필요 개념 및 이론 설명\n\nTitanic, Iris, Penuins, San Francisco Crime\n\n\n4주차\nClassification and Hypothesis Testing\n\nLogistic Regression과 Decision Tree, SVC, Random Forest, XGBoost, LGBM 등 적용- Training & Evaluation / Basic hyperparameter tuning 수행\n\n-\n\n\n5주차\nCase Studies and Projects\n\n기존 수업 이론/실습 개념 바탕 Case Study 3종 (+Final Quiz)- Case Study 및 Final Quiz 해설- 개인 별 Project 인터뷰 및 Grouping / 공통과제 선정- Project 진행, 발표, Best Project 선정 및 공유\n\n-\n\n\n\n\n\n\n\n\n\nGS 칼텍스 데이터 사이언스\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGS 칼텍스 데이터 과학\n\n\n\n일정\n과목명\n학습상세\n데이터셋\n\n\n\n\n00주차\n환경설정\n학습 도구 설치 및 학습환경\n파머 펭귄, BitData\n\n\n01주차\n디지털 글쓰기(보고서)\n마크다운 / Quarto\nBitData\n\n\n02주차\n명령라인 데이터 분석\n자동화(Shell)\nSWC\n\n\n03주차\n버전 제어\n버전제어/협업(Git / GitHub / GitLab)\n\n\n\n04주차\n프로그래밍\nSQL\nData Carpentry, DVD 렌탈\n\n\n05주차\n프로그래밍\n시각화\ngapminder\n\n\n06주차\n프로그래밍\n파이썬\n\n\n\n07주차\n대쉬보드\nFlexdashboard / Shinydashboard\n\n\n\n08주차\n기계학습\ntidymodels / scikit-learn\n\n\n\n09주차\n데이터 과학 제품\nRESTful API\n\n\n\n10주차\n특수 데이터\n시계열(Time Series)\nKOSPI\n\n\n11주차\n특수 데이터\n공간정보(Geospatial) / 텍스트 분석\n미디어오늘\n\n\n12주차\n비정형 데이터\n텍스트와 이미지 (딥러닝)\n\n\n\n\n\n\n\n\n\n\nGS 칼텍스 데이터 사이언스\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n챗GPT 데이터 사이언스 건국대 전문교육과정\n\n\n\n강의주제\n강의내용\n수업방법\n\n\n\n\n1 주차 - 2023-07-01\n\n\n데이터 사이언스 언어\n챗GPT와 데이터 사이언스\n핸즈온 실습\n\n\n데이터 사이언스 언어\nTidyverse와 챗GPT\n핸즈온 실습\n\n\n데이터 사이언스 언어\n파이썬/R/SQL 그리고 API\n핸즈온 실습\n\n\n2 주차 - 2023-07-08\n\n\n소프트웨어 카펜트리 (기본기)\n자동화: 유닉스 쉘\n핸즈온 실습\n\n\n소프트웨어 카펜트리 (기본기)\n버전제어: Git / GitHub\n핸즈온 실습\n\n\n3 주차 - 2023-07-15\n\n\n소프트웨어 카펜트리 (기본기)\n파이썬 / R\n핸즈온 실습\n\n\n소프트웨어 카펜트리 (기본기)\n데이터베이스\n핸즈온 실습\n\n\n4 주차 - 2023-07-22\n\n\n데이터 과학 자료구조\n숫자/범주 --> 데이터프레임\n핸즈온 실습\n\n\n데이터 과학 자료구조\n날짜/공간정보/네트워크\n핸즈온 실습\n\n\n데이터 과학 자료구조\n텍스트/이미지/오디오\n핸즈온 실습\n\n\n\n\n\n\n\n\n\n\n\n강의주제\n강의내용\n수업방법\n\n\n\n\n5 주차 - 2023-07-29\n\n\n디지털 글쓰기, 대쉬보드, API 개발\n디지털 보고서(PDF, HTML, PPT)\n핸즈온 실습\n\n\n디지털 글쓰기, 대쉬보드, API 개발\nServerless 대쉬보드, R/Python 대쉬보드\n핸즈온 실습\n\n\n디지털 글쓰기, 대쉬보드, API 개발\n기계학습 모형 API 배포\n핸즈온 실습\n\n\n- 주차 - 2023-08-05\n\n\n휴가\n휴가\n-\n\n\n6 주차 - 2023-08-12\n\n\n챗GPT와 오픈소스\n(오픈소스) 거대언어모형(LLM)\n핸즈온 실습\n\n\n챗GPT와 오픈소스\nWrite/Draw/Coding/Reasoning\n핸즈온 실습\n\n\n챗GPT와 오픈소스\nAI 시대 데이터 사이언스 제품개발 방법\n핸즈온 실습\n\n\n7 주차 - 2023-08-19\n\n\nPrompt Engineering\n사용자 및 개발자를 위한 프롬프트 공학\n핸즈온 실습\n\n\nPrompt Engineering\nOpenAI API 기반 NLP 개발\n핸즈온 실습\n\n\nPrompt Engineering\nOPL(OpenAI, Pinecone, Langchain) 프레임워크\n핸즈온 실습\n\n\n8 주차 - 2023-08-26\n\n\n챗GPT AI 앱 프로젝트\n오픈소스 LLM 기반 AI 데이터 분석 제품\n핸즈온 실습\n\n\n챗GPT AI 앱 프로젝트\n데이터 사이언스 AutoGPT\n핸즈온 실습\n\n\n챗GPT AI 앱 프로젝트\n챗GPT AI 프로젝트 발표\n핸즈온 실습\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n데이터 사이언스 MCQ" + }, + { + "objectID": "chatgpt_sch_202404.html#구글-검색의-종말", + "href": "chatgpt_sch_202404.html#구글-검색의-종말", + "title": "챗GPT 데이터 사이언스", + "section": "구글 검색의 종말(?)", + "text": "구글 검색의 종말(?)" + }, + { + "objectID": "chatgpt_sch_202404.html#글쓰기-역사", + "href": "chatgpt_sch_202404.html#글쓰기-역사", + "title": "챗GPT 데이터 사이언스", + "section": "글쓰기 역사", + "text": "글쓰기 역사\n\n\n\n\n\n문자와 종이 역사" + }, + { + "objectID": "chatgpt_sch_202404.html#오피스-전쟁", + "href": "chatgpt_sch_202404.html#오피스-전쟁", + "title": "챗GPT 데이터 사이언스", + "section": "오피스 전쟁", + "text": "오피스 전쟁\n\n\n\n\n\n\n\n\n\n\n항목\nMS 오피스 1\n구글 오피스 2\n리브레 오피스 3\n한컴 오피스 4\n\n\n\n\n문서 작성 및 편집\n워드, 엑셀, 파워포인트 등의 데스크톱 및 웹 앱 제공\n구글 문서, 스프레드시트, 프레젠테이션 등의 웹 앱 제공\n라이터, 캘크, 임프레스 등의 데스크톱 앱 제공\n한글, 한셀, 한쇼 등의 데스크톱 및 모바일 앱 제공\n\n\n협업 및 공유\n원드라이브를 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움\n구글 드라이브를 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정하고 실시간으로 확인 가능\n드롭박스나 구글 드라이브와 연동하여 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움\n넷프리즘을 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움\n\n\n이메일 및 채팅\nOutlook을 통해 비즈니스 메일 제공. Teams을 통해 채팅 및 화상 회의 가능\nGmail을 통해 비즈니스 메일 제공. Chat과 Meet을 통해 채팅 및 화상 회의 가능\n이메일 및 채팅 기능 없음\n이메일 및 채팅 기능 없음\n\n\n가격\n1인당 월 5~20달러 다양한 요금제 제공. 데스크톱 앱 사용 가능한 요금제는 12.5달러부터 시작\n1인당 월 6~18달러까지 다양한 요금제 제공\n완전 무료\n개인용은 무료로 사용 가능하며 업무용은 별도로 견적 요청 필요\n\n\n\nhttps://www.microsoft.com/ko-kr/microsoft-365/businesshttps://workspace.google.com/intl/ko_kr/https://www.libreoffice.org/https://www.hancom.com/product/productMain.do" + }, + { + "objectID": "chatgpt_sch_202404.html#오피스-전쟁-패러다임-충돌", + "href": "chatgpt_sch_202404.html#오피스-전쟁-패러다임-충돌", + "title": "챗GPT 데이터 사이언스", + "section": "오피스 전쟁: 패러다임 충돌", + "text": "오피스 전쟁: 패러다임 충돌" + }, + { + "objectID": "chatgpt_sch_202404.html#챗gpt-시대-사무실", + "href": "chatgpt_sch_202404.html#챗gpt-시대-사무실", + "title": "챗GPT 데이터 사이언스", + "section": "챗GPT 시대 사무실", + "text": "챗GPT 시대 사무실\n\n챗GPT 사용원인인터페이스챗GPT AS-IS챗GPT TO-BE" + }, + { + "objectID": "chatgpt_sch_202404.html#챗gpt가-이룬-대통합", + "href": "chatgpt_sch_202404.html#챗gpt가-이룬-대통합", + "title": "챗GPT 데이터 사이언스", + "section": "챗GPT가 이룬 대통합", + "text": "챗GPT가 이룬 대통합" + }, + { + "objectID": "chatgpt_sch_202404.html#인터페이스-interface", + "href": "chatgpt_sch_202404.html#인터페이스-interface", + "title": "챗GPT 데이터 사이언스", + "section": "인터페이스 (Interface)", + "text": "인터페이스 (Interface)\n\n진화과정CLIGUIWUIMUILUI\n\n\n \n\n\n\n\n\nflowchart TB\n \n subgraph A[\"사용자 인터페이스\"]\n direction LR\n CLI[\"CLI<br>Command Line Interface<br> 1950~\"] --> GUI[\"GUI <br> Graphic User Inferface<br> 1970~\"]\n GUI --> WI[\"Web Interface <br> 1994~\"]\n WI --> MI[\"Mobile Interface <br> 2007~\"]\n MI --> NUI[\"LUI <br> Language User Interface <br> 2023~\"]\n\n end\n\n\nclass A nodeStyle\n\nclassDef nodeStyle fill:#fcfbfa,stroke:#000000,stroke-width:0.7px,font-weight:bold,font-size:14px;\n\n\n\n\n\n\n\n\n\n\n\nhttps://en.wikipedia.org/wiki/Computer_terminal\n\n\n\n\n\n\nhttps://kartsci.org/kocomu/computer-history/graphical-user-interface-history/\n\n\n\n \n\n\n\n\n\nhttps://www.huffpost.com/entry/uber-your-way-through-cit_b_1205446\n\n\n\n\n\nhttps://twitter.com/sama/status/1515764302904377344" + }, + { + "objectID": "chatgpt_sch_202404.html#챗gpt-nui", + "href": "chatgpt_sch_202404.html#챗gpt-nui", + "title": "챗GPT 데이터 사이언스", + "section": "챗GPT NUI", + "text": "챗GPT NUI\n\n1:1 채팅클릭하여 완료자동완성명령 팔레트\n\n\n\n챗GPT (ChatGPT)\n\n\n\n\n\n\n\n\n\nOpenAI Playground\n\n\n\n\n\n\n\n\n\nGitHub Copilot\n\n\n\n\n\n\n\nhttps://towardsdatascience.com/some-experiments-using-github-copilot-with-python-90f8065fb72e\n\n\n\n\n\n사례\n\n\n\n\n\n\n\n\n\n\nhttps://blog.replit.com/clui\nhttps://blog.replit.com/ai\n\n\nCommand Palette 역사\n\n60년대로 거슬러 올라가는 명령 팔레트는 최근 기술 업계에서 다시 인기를 얻고 있으며 사용자 중심 소프트웨어의 필수적인 부분으로 간주됨.\nCLI는 개인용 컴퓨터가 대중화되기 전 초기 컴퓨팅을 지배했으며 사용자가 시스템을 작동하기 위해 특정 명령을 알아야 했다. 1984년 Apple의 매킨토시와 함께 도입된 그래픽 사용자 인터페이스(GUI)는 중요한 혁신으로 여겨졌지만, 소프트웨어의 복잡성이 증가함에 따라 GUI만으로는 탐색하기가 점점 더 어려워졌다.\n사용자가 항목 이름의 짧은 약어입력하여 앱과 파일을 검색할 수 있는 런처가 점점 더 보편화 되었다. 예를 들어 2001년에 출시된 Mac OS X용 LaunchBar, Quicksilver, 2005년에 출시된 Apple의 시스템 전체 Spotlight가 있다. 검색과 CLI의 결합은 Command Palette의 탄생으로 이어졌다.\n오늘날 우리가 알고 있는 현대식 명령 팔레트는 2011년 Sublime Text 2 베타 Joe Skinner가 처음 소개. 명령줄 사용에 익숙한 프로그래머들이 단축키를 모두 외울 필요 없이 더 빠르게 작업할 수 있어 중요한 발전이 됨." } ] \ No newline at end of file diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 580cbf3..936e8ef 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -1296,4 +1296,40 @@ https://r2bit.com/bitSlide/slideshow/202404-meetup/dashboard.html 2024-04-18T03:15:19.760Z + + https://r2bit.com/bitSlide/chatgpt_sch_202404.html + 2024-04-23T07:35:45.691Z + + + https://r2bit.com/bitSlide/slideshow/202404-sch/seoul-r-meeup.html + 2024-04-18T02:21:32.241Z + + + https://r2bit.com/bitSlide/slideshow/202404-sch/quarto-digital-writing.html + 2024-04-23T06:52:42.363Z + + + https://r2bit.com/bitSlide/slideshow/202404-sch/dashboard.html + 2024-04-18T03:15:19.760Z + + + https://r2bit.com/bitSlide/slideshow/202404-sch/chatgpt-data-science.html + 2024-04-23T07:25:25.860Z + + + https://r2bit.com/bitSlide/slideshow/202404-sch/book.html + 2024-04-23T07:33:56.033Z + + + https://r2bit.com/bitSlide/slideshow/202404-sch/curriculum.html + 2024-04-23T06:24:38.041Z + + + https://r2bit.com/bitSlide/slideshow/202404-sch/office-interface.html + 2024-01-08T13:03:31.989Z + + + https://r2bit.com/bitSlide/slideshow/202404-sch/reference.html + 2023-04-06T11:45:34.598Z + diff --git a/docs/slideshow/202404-sch/book.html b/docs/slideshow/202404-sch/book.html new file mode 100644 index 0000000..9593477 --- /dev/null +++ b/docs/slideshow/202404-sch/book.html @@ -0,0 +1,770 @@ + + + + + + + + + +발표자료 | 한국 R 사용자회 + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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    챗GPT 도서출판

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    +

    챗GPT 데이터 과학 도서

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    📘 교보 POD 종이책: https://bit.ly/4cVDxxL

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    📗 교보 전자책: https://bit.ly/4dccQ8n

    +

    🌐 웹사이트: https://bit.ly/4909pOB

    +

    💻 소스코드: https://bit.ly/3wXugES (이광춘 2024b)

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    📘 교보 POD 종이책: https://bit.ly/4a8v1JS

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    📗 교보 전자책: https://bit.ly/4auasHU

    +

    🌐 웹사이트: https://bit.ly/48V8u1T

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    💻 소스코드: https://bit.ly/48RqMB9 (이광춘 2024c)

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    📘 교보 POD 종이책: https://bit.ly/3wElKuk

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    📗 교보 전자책: https://bit.ly/3IkWE6f

    +

    🌐 웹사이트: https://bit.ly/3P7PVjX

    +

    💻 소스코드: https://bit.ly/3InK46m (이광춘 2024d)

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    📘 교보 POD 종이책: https://bit.ly/3OJmMeT

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    📗 교보 전자책: https://bit.ly/48fujZD

    +

    🌐 웹사이트: https://bit.ly/48gYn7d

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    💻 소스코드: https://bit.ly/3wrgeuP (이광춘 2024a)

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    📘 교보 POD 종이책: https://bit.ly/434h5y9

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    📗 교보 전자책: https://bit.ly/KzUdOU

    +

    🌐 웹사이트: https://bit.ly/3I4FpWS

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    💻 소스코드: https://bit.ly/3wrgeuP (이광춘·신종화 2023)

    +

    📰 뉴스기사: 챗GPT가 던진 새 가능성, 데이터 과학 분야 통해 미리 살펴보기

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    준비중

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    📘 교보 POD 종이책: 준비중

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    📗 교보 전자책: 준비중

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    🌐 웹사이트: https://r2bit.com/gpt-stat/

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    💻 소스코드: https://github.com/bit2r/gpt-stat/

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    참고문헌

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    +이광춘. 2024a. 챗GPT SQL. 한국 R 사용자회. +
    +
    +———. 2024b. 챗GPT 데이터 과학. 한국 R 사용자회. +
    +
    +———. 2024c. 챗GPT 코딩. 한국 R 사용자회. +
    +
    +———. 2024d. 챗GPT 쿼토. 한국 R 사용자회. +
    +
    +이광춘·신종화. 2023. 챗GPT 유닉스 쉘: 데이터 과학 자동화 도구. 한국 R 사용자회. +
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    + + + + + + \ No newline at end of file diff --git a/docs/slideshow/202404-sch/chatgpt-data-science.html b/docs/slideshow/202404-sch/chatgpt-data-science.html new file mode 100644 index 0000000..ceba769 --- /dev/null +++ b/docs/slideshow/202404-sch/chatgpt-data-science.html @@ -0,0 +1,956 @@ + + + + + + + + + +발표자료 | 한국 R 사용자회 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    생성형 AI
    데이터 사이언스

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    포스터 제작

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    API 프로그래밍

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    웹사이트를 읽고 다음 지령을 준비해 https://apis.map.kakao.com/web/documentation/

    +
    +
    Kakao 지도 API 문서는 다양한 지도 관련 기능을 제공한다. 이 문서는 지도 객체 생성, 이벤트, 메서드 등에 대한 상세한 정보를 담고 있다. 아래는 주요 내용의 요약이다.
    +
    +### 지도 객체 생성
    +- `kakao.maps.Map(container, options)`: 지도를 생성한다.
    +  - `container`: 지도가 표시될 HTML element
    +  - `options`: 중심 좌표, 확대 수준, 지도 종류 등을 설정할 수 있다.
    +
    +### 주요 메서드
    +- `setCenter(latlng)`: 지도의 중심 좌표를 설정한다.
    +- `getCenter()`: 지도의 중심 좌표를 반환한다.
    +- `setLevel(level, options)`: 지도의 확대 수준을 설정한다.
    +- `getLevel()`: 지도의 확대 수준을 반환한다.
    +- `setMapTypeId(mapTypeId)`: 지도의 타입을 설정한다.
    +- `getMapTypeId()`: 지도의 타입을 반환한다.
    +
    +### 이벤트
    +- `center_changed`: 중심 좌표가 변경되면 발생한다.
    +- `zoom_start`: 확대 수준이 변경되기 직전에 발생한다.
    +- `zoom_changed`: 확대 수준이 변경되면 발생한다.
    +- `click`: 지도를 클릭하면 발생한다.
    +- `dblclick`: 지도를 더블클릭하면 발생한다.
    +
    +이 외에도 다양한 메서드와 이벤트가 있어, 개발자가 원하는 기능을 쉽게 구현할 수 있다.
    +
    +
    +
    +

    주소를 넘기면 위경도 좌표를 반환하는 R 코드를 작성해줘.

    +
    +

    R에서 주소를 위경도 좌표로 변환하기 위해 httr 패키지를 사용하여 Kakao API를 호출한 예시 코드이다.

    +
    # 필요한 패키지를 설치하고 불러온다
    +install.packages("httr")
    +install.packages("jsonlite")
    +library(httr)
    +library(jsonlite)
    +
    +# 주소를 위경도로 변환하는 함수를 정의한다
    +get_latlng <- function(address) {
    +  # Kakao API 키를 설정한다 (본인의 API 키로 교체해야 한다)
    +  api_key <- "YOUR_KAKAO_API_KEY"
    +  
    +  # API URL과 파라미터를 설정한다
    +  url <- "https://dapi.kakao.com/v2/local/search/address.json"
    +  params <- list(query = address)
    +  headers <- add_headers("Authorization" = paste("KakaoAK", api_key))
    +  
    +  # API를 호출한다
    +  response <- GET(url, query = params, headers)
    +  
    +  # 응답을 확인하고 JSON을 파싱한다
    +  if (status_code(response) == 200) {
    +    content <- content(response, "text", encoding = "UTF-8")
    +    json_data <- fromJSON(content)
    +    
    +    # 위경도를 추출한다
    +    if (json_data$meta$total_count > 0) {
    +      lat <- json_data$documents[[1]]$y
    +      lng <- json_data$documents[[1]]$x
    +      return(list(lat = lat, lng = lng))
    +    } else {
    +      return(NULL)
    +    }
    +  } else {
    +    stop("API request failed")
    +  }
    +}
    +
    +# 함수를 테스트한다
    +address <- "서울특별시 강남구 테헤란로 212"
    +result <- get_latlng(address)
    +print(paste("Latitude:", result$lat, "Longitude:", result$lng))
    +
    +
    +
    +
    library(httr)
    +library(jsonlite)
    +
    +# 주소를 위경도로 변환하는 함수를 정의한다
    +get_latlng <- function(address) {
    +  # Kakao API 키를 설정한다 (본인의 API 키로 교체해야 한다)
    +  api_key <- Sys.getenv("KAKAO_MAP_API_KEY")
    +  
    +  # API URL과 파라미터를 설정한다
    +  url <- "https://dapi.kakao.com/v2/local/search/address.json"
    +  params <- list(query = address)
    +  headers <- add_headers("Authorization" = paste("KakaoAK", api_key))
    +  
    +  # API를 호출한다
    +  response <- GET(url, query = params, headers)
    +  
    +  # 응답을 확인하고 JSON을 파싱한다
    +  if (status_code(response) == 200) {
    +    content <- content(response, "text", encoding = "UTF-8")
    +    json_data <- fromJSON(content)
    +    
    +    # 위경도를 추출한다
    +    if (json_data$meta$total_count > 0) {
    +      lat <- json_data$documents[[1]]$y
    +      lng <- json_data$documents[[1]]$x
    +      return(list(lat = lat, lng = lng))
    +    } else {
    +      return(NULL)
    +    }
    +  } else {
    +    stop("API request failed")
    +  }
    +}
    +
    +# 가톨릭관동대학교 주소
    +address <- "강원도 강릉시 범일로579번길 24"
    +result <- get_latlng(address)
    +print(paste("Latitude:", result$lat, "Longitude:", result$lng))
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    + +
    +
    +

    사실확인 (Wolfram)

    +
    + +
    +
    +

    울프람 알파(Wolfram Alpha) 사용해서 사실관계를 확인해보자.

    +
    +

    “지구는 평평하다”라는 주장을 검증해줘.

    +
    +
    "지구는 평평하다"라는 주장에 대해 Wolfram Alpha를 이용해 검증해보았습니다.
    +
    +### 입력 해석
    +- 질문: 지구는 평평하다?
    +
    +### 결과
    +- 답변: 아니다.
    +  - 지역적으로만 그렇다. 그리고 그것도 특정 지역, 예를 들어 미국 중서부에서만 해당된다.
    +
    +자세한 정보는 [Wolfram|Alpha 웹사이트](https://www6b3.wolframalpha.com/input?i=Is+the+Earth+flat%3F)에서 확인할 수 있다.
    +
    +이처럼 Wolfram Alpha는 다양한 주장이나 정보에 대한 팩트체크를 할 수 있는 유용한 도구이다.
    + +
    +
    +
    +

    라면을 많이 먹으면 건강에 정말 안좋은가?

    +
    +

    Wolfram Alpha에서는 “라면을 많이 먹으면 건강에 정말 안 좋은가?”에 대한 구체적인 답변을 제공하지 못했다. 그러나 일반적으로 라면은 높은 나트륨, 지방, 인공 첨가물 등을 포함하고 있어, 과도하게 섭취할 경우 다음과 같은 건강 문제가 발생할 수 있다:

    +
      +
    1. 고혈압: 라면에는 나트륨이 많이 들어있다. 나트륨 과다 섭취는 혈압을 높일 수 있다.

    2. +
    3. 비만: 라면은 칼로리가 높고, 지방도 많이 들어있다. 지속적으로 라면을 많이 먹으면 체중 증가의 위험이 있다.

    4. +
    5. 소화기 문제: 라면은 소화가 잘 되지 않는 식품 중 하나이다. 과도한 섭취는 소화기에 부담을 줄 수 있다.

    6. +
    7. 영양 불균형: 라면은 영양소가 적고, 나트륨이나 지방 등의 불필요한 성분이 많다. 이로 인해 영양 불균형이 발생할 수 있다.

    8. +
    9. 심혈관 질환: 고혈압, 높은 콜레스테롤 등은 심혈관 질환의 위험을 높인다.

    10. +
    +

    라면을 즐길 때는 이러한 점들을 고려하여 적당한 양과 빈도로 섭취하는 것이 중요하다.

    +
    +
    +
    +
    +
    +

    수능문제 풀이

    +
    + +
    +
    +

    2023학년도 대학수학능력시험 수학영역 문제를 풀어보자.

    +

    +
    +
    +
    +

    \(\left(\frac{4}{2^{\sqrt{2}}}\right)^{2+\sqrt{2}}\) 값은?

    +
    +

    \[ +\left(\frac{4}{2^{\sqrt{2}}}\right)^{2+\sqrt{2}} +\] 의 값은 \(4\)입니다.

    +

    정확한 형태로는 \(2^{(2 - \sqrt{2}) (2 + \sqrt{2})}\)로 표현됩니다.

    +

    자세한 정보는 Wolfram|Alpha 웹사이트에서 확인할 수 있다.

    +
    +
    +
    +

    과정도 설명해줘.

    +
    +
    +
    +

    물론이다. 주어진 식은 \[ +\left(\frac{4}{2^{\sqrt{2}}}\right)^{2+\sqrt{2}} +\]이다. 이 식을 단계별로 분해해보자.

    +
      +
    • 단계 1: 분모를 간단하게 만들기
      먼저, 분모인 \(2^{\sqrt{2}}\)를 간단하게 만들기 위해 분자와 분모에 \(2^{\sqrt{2}}\)를 곱한다. 이렇게 하면:
    • +
    +

    \[ +\left(\frac{4 \times 2^{\sqrt{2}}}{2^{\sqrt{2}} \times 2^{\sqrt{2}}}\right)^{2+\sqrt{2}} +\]

    +
      +
    • 단계 2: 분모를 합치기
      이제 분모의 \(2^{\sqrt{2}} \times 2^{\sqrt{2}}\)를 합쳐 \(2^{2\sqrt{2}}\)로 만든다.
    • +
    +

    \[ +\left(\frac{4 \times 2^{\sqrt{2}}}{2^{2\sqrt{2}}}\right)^{2+\sqrt{2}} +\]

    +
    +
      +
    • 단계 3: 분자를 합치기
      분자의 \(4 \times 2^{\sqrt{2}}\)를 합쳐 \(2^2 \times 2^{\sqrt{2}} = 2^{2+\sqrt{2}}\)로 만든다.
    • +
    +

    \[ +\left(\frac{2^{2+\sqrt{2}}}{2^{2\sqrt{2}}}\right)^{2+\sqrt{2}} +\]

    +
      +
    • 단계 4: 지수 법칙 적용
      지수 법칙에 따라 분자와 분모의 지수를 빼준다.
    • +
    +

    \[ +\left(2^{(2+\sqrt{2}) - 2\sqrt{2}}\right)^{2+\sqrt{2}} = \left(2^{(2 - \sqrt{2})(2 + \sqrt{2})}\right)^{2+\sqrt{2}} +\]

    +
      +
    • 단계 5: 최종 계산
      마지막으로, \((2 - \sqrt{2})(2 + \sqrt{2})\)를 계산하면 \(4 - 2 = 2\)가 된다. 따라서 최종 결과는 \(2^2 = 4\)이다.
    • +
    +

    이렇게 해서 \[ +\left(\frac{4}{2^{\sqrt{2}}}\right)^{2+\sqrt{2}} = 4 +\]임을 확인할 수 있다.

    +
    +
    +
    +
    +
    + +
    +
    +

    OpenAI Advanced Data Analysis

    +


    +
    +

    Open AI Code Interpreter → Advanced Data Analysis → 챗GPT4

    +
    +


    +

    + +
    +
    +

    RStudio Copilot

    +
    +

    Tools -> Global Options -> Copilot -> Enable Github Copilot

    +
    +

    +
    +
    +

    웹앱(Shiny App) 개발 사례

    +

    +
    +
    +

    광명시 보좌관

    +
    +
    +

    +
    +
    + +
    +
    +

    RTutor & PandasAI

    +
    +
    +

    +
    +
    + + + +
    +
    + +
    + +
    + + + + + + \ No newline at end of file diff --git a/docs/slideshow/202404-sch/curriculum.html b/docs/slideshow/202404-sch/curriculum.html new file mode 100644 index 0000000..4dd19c1 --- /dev/null +++ b/docs/slideshow/202404-sch/curriculum.html @@ -0,0 +1,4116 @@ + + + + + + + + + +발표자료 | 한국 R 사용자회 + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + + + +
    + + + + +
    +

    통계 → 데이터 사이언스 → 챗GPT 인공지능

    +
    +

    교육과정

    +
    + +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    +

    +
    +
    +
    +
    +
    +
    library(tidyverse)
    +library(rvest)
    +
    +library(gt)
    +library(gtExtras)
    +
    +ds_url <- "https://statkclee.github.io/yonsei/"
    +ds_html <- read_html(ds_url)
    +
    +ds_html |> 
    +  html_elements('table') |> 
    +  html_table() %>%
    +  .[[1]] |> 
    +  slice(2:n()) |> 
    +  separate(X2, into = c("학습내용", "날짜"), sep = "-") |> 
    +  separate(학습내용, into = c("주차", "학습내용"), sep = "\\.") |> 
    +  select(주차, 학습내용, 날짜) |> 
    +  drop_na() |> 
    +  mutate(날짜 = str_trim(날짜),
    +         학습내용 = str_trim(학습내용))  |> 
    +  mutate(날짜 = if_else(str_detect(날짜, "^\\d"), 날짜, NA_character_))  |> 
    +  gt() |> 
    +    gt_theme_pff() |> 
    +    sub_missing(
    +      columns = 날짜,
    +    missing_text = "-"
    +    )  |> 
    +    cols_align("center") |> 
    +    opt_table_font(
    +      font = list(
    +        google_font(name = "Sunflower"),
    +        "NanumGothic"
    +      )
    +    ) %>% 
    +    tab_style(
    +      style = list(
    +        "font-variant: small-caps;"
    +      ),
    +      locations = cells_body(columns = everything())
    +    ) %>% 
    +    tab_header(
    +      title =
    +        htmltools::tagList(
    +          htmltools::tags$div(
    +            style = htmltools::css(
    +              `text-align` = "center"
    +            ),
    +            htmltools::HTML(
    +              web_image("img/curriculum_yonsei.png")
    +            )
    +          ),
    +          htmltools::tags$div(
    +            htmltools::tags$strong("연세대"),
    +            "데이터 사이언스 입문"
    +          )
    +        )
    +    ) |> 
    +  tab_options(
    +    table.width = "65%",
    +    heading.title.font.size = px(12)
    +  )
    +
    +
    +
    + + + +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    +
    +연세대 데이터 사이언스 입문 +
    주차학습내용날짜
    1데이터 과학 (Data Science)3월7일
    2프로그래밍 기초와 마크다운 보고서3월14일
    3데이터와 커뮤니티 찾기, 도움 청하기, 프로젝트 설정3월21일
    4데이터 수집-
    5자료구조(Data Structures)4월4일
    6데이터프레임과 SQL4월11일
    7버전제어, 협업, 그리고 저작권과 라이선스4월18일
    8중간고사4월25일
    9시각화 (Visualization)5월2일
    10정규표현식5월9일
    11데이터 과학 프로그래밍5월16일
    12데이터 과학 제품-
    13함수형 프로그래밍5월30일
    14데이터 과학 저작6월6일(현충일)
    15R 팩키지6월13일
    16기말고사6월20일
    + +
    +
    +
    +
    + +
    +
    +
    +
    ds2_url <- "https://statkclee.github.io/yonsei2/"
    +ds2_html <- read_html(ds2_url)
    +
    +ds2_html |> 
    +  html_elements('table') |> 
    +  html_table() %>%
    +  .[[1]] |> 
    +  slice(2:n()) |> 
    +  separate(X2, into = c("학습내용", "날짜"), sep = "&race;") |> 
    +  separate(학습내용, into = c("주차", "학습내용"), sep = "\\.") |> 
    +  select(주차, 학습내용, 날짜) |> 
    +  drop_na() |> 
    +  mutate(날짜 = str_trim(날짜),
    +         학습내용 = str_trim(학습내용))  |> 
    +  mutate(날짜 = if_else(str_detect(날짜, "^\\d"), 날짜, NA_character_))  |> 
    +  gt() |> 
    +    gt_theme_pff() |> 
    +    sub_missing(
    +      columns = 날짜,
    +    missing_text = "-"
    +    )  |> 
    +    cols_align("center") |> 
    +    opt_table_font(
    +      font = list(
    +        google_font(name = "Sunflower"),
    +        "NanumGothic"
    +      )
    +    ) %>% 
    +    tab_style(
    +      style = list(
    +        "font-variant: small-caps;"
    +      ),
    +      locations = cells_body(columns = everything())
    +    ) %>% 
    +    tab_header(
    +      title =
    +        htmltools::tagList(
    +          htmltools::tags$div(
    +            style = htmltools::css(
    +              `text-align` = "center"
    +            ),
    +            htmltools::HTML(
    +              web_image("img/curriculum_yonsei2.png")
    +            )
    +          ),
    +          htmltools::tags$div(
    +            htmltools::tags$strong("연세대"),
    +            "데이터 (사이언스) 공학"
    +          )
    +        )
    +    ) |> 
    +  tab_options(
    +    table.width = "65%",
    +    heading.title.font.size = px(12)
    +  )
    +
    +
    +
    + + + +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    +
    +연세대 데이터 (사이언스) 공학 +
    주차학습내용날짜
    1데이터 사이언스 언어9월6일
    2[언어] R9월20일
    3[언어] 파이썬9월27일
    4[언어] API 프로그래밍10월4일
    5데이터 사이언스 자료구조10월11일
    6[자료구조] 시계열 데이터10월18일
    7중간고사10월25일
    8[자료구조] 지리 공간 데이터11월01일
    9[자료구조] NLP - 텍스트 마이닝(R)11월08일
    10[자료구조] NLP - 파이썬11월15일
    11빅데이터 시각화11월22일
    12기계학습 - 예측모형11월29일
    13DevOps - 도커, HPC, 스파크12월06일
    14제품화12월13일
    15기말고사12월20일
    + +
    +
    +
    +
    + +
    +
    +
    +
    +
    +
    + + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    +
    +GS 칼텍스 시민 데이터 과학 +
    일정과정과정상세데이터셋

    1주차

    Introduction

      +
    • Defining and solving problems
      - Quantifying performance indicators
      - Stats and Probability for Data Science
    • +
    -

    2주차

    Data Science

      +
    • Python 기초 문법 복습 및 NumPy, Pandas 등 연습
      - Kaggle: MPG / Titanic EDA 개념과 Data science 전체흐름 습득
      - EDA, Feature engineering, Visualization, Correlating 등 preprocessing 작업
      - Model, predict and evaluation 의 전체 과정을 습득
    • +
    -

    3주차

    Regression and Prediction

      +
    • Regression에 대한 개념 및 이론 설명
    • +

    Breast Cancer, California housing prices, Bike-sharing

    3주차

    Regression and Prediction

      +
    • EDA 및 preprocessing
      - Linear Regression 중심, CART, SVM, Ensemble기법 적용
      - Training & Evaluation (MSE, MAE, RMSE, RMSLE, R-Squared, etc.)
      - Basic hyperparameter tuning 수행
    • +
    -

    4주차

    Classification and Hypothesis Testing

      +
    • Classification과 필요 개념 및 이론 설명
    • +

    Titanic, Iris, Penuins, San Francisco Crime

    4주차

    Classification and Hypothesis Testing

      +
    • Logistic Regression과 Decision Tree, SVC, Random Forest, XGBoost, LGBM 등 적용
      - Training & Evaluation / Basic hyperparameter tuning 수행
    • +
    -

    5주차

    Case Studies and Projects

      +
    • 기존 수업 이론/실습 개념 바탕 Case Study 3종 (+Final Quiz)
      - Case Study 및 Final Quiz 해설
      - 개인 별 Project 인터뷰 및 Grouping / 공통과제 선정
      - Project 진행, 발표, Best Project 선정 및 공유
    • +
    -
    + +
    +
    +
    +
    + +
    +
    +
    +
    +
    +
    + + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    +
    +GS 칼텍스 데이터 과학 +
    일정과목명학습상세데이터셋
    00주차환경설정학습 도구 설치 및 학습환경파머 펭귄, BitData
    01주차디지털 글쓰기(보고서)마크다운 / QuartoBitData
    02주차명령라인 데이터 분석자동화(Shell)SWC
    03주차버전 제어버전제어/협업(Git / GitHub / GitLab)
    04주차프로그래밍SQLData Carpentry, DVD 렌탈
    05주차프로그래밍시각화gapminder
    06주차프로그래밍파이썬
    07주차대쉬보드Flexdashboard / Shinydashboard
    08주차기계학습tidymodels / scikit-learn
    09주차데이터 과학 제품RESTful API
    10주차특수 데이터시계열(Time Series)KOSPI
    11주차특수 데이터공간정보(Geospatial) / 텍스트 분석미디어오늘
    12주차비정형 데이터텍스트와 이미지 (딥러닝)
    + +
    +
    +
    +
    + +
    +
    +
    +
    library(tidyverse)
    +library(readxl)
    +library(gt)
    +library(gtExtras)
    +extrafont::loadfonts()
    +
    +curriculum_raw <- read_excel(glue::glue("{here::here()}/data/건국대_커리큘럼.xlsx"))
    +
    +curriculum <- curriculum_raw %>% 
    +  fill(everything(), .direction="down") %>% 
    +  mutate(날짜 = as.Date(날짜)) %>% 
    +  mutate(주차 = glue::glue("{주차} 주차"))
    +
    +## 첫번째
    +curriculum_first <- curriculum %>% 
    +  filter(dplyr::row_number() <=10)
    +
    +curriculum_first_gt <- curriculum_first %>%   
    +  gt(groupname_col = c("주차", "날짜")) %>% 
    +    cols_align(align = "center") %>% 
    +  opt_table_font(
    +    font = list(
    +      google_font(name = "Sunflower"),
    +      "NanumGothic"
    +    )
    +  ) %>% 
    +  gt_theme_pff() %>% 
    +  tab_style(
    +    style = list(
    +      "font-variant: small-caps;"
    +    ),
    +    locations = cells_body(columns = everything())
    +  ) %>% 
    +  tab_header(
    +    title =
    +      htmltools::tagList(
    +        htmltools::tags$div(
    +          style = htmltools::css(
    +            `text-align` = "center"
    +          ),
    +          htmltools::HTML(
    +            web_image("img/konkuk/tools.png")
    +          )
    +        ),
    +        htmltools::tags$div(
    +          htmltools::tags$strong("챗GPT 데이터 사이언스"),
    +          "건국대 전문교육과정"
    +        )
    +      )
    +  ) 
    +
    +## 두번째
    +curriculum_second <- curriculum %>% 
    +  filter(dplyr::row_number() > 10)
    +
    +curriculum_second_gt <- curriculum_second %>%   
    +  gt(groupname_col = c("주차", "날짜")) %>% 
    +    cols_align(align = "center") %>% 
    +  opt_table_font(
    +    font = list(
    +      google_font(name = "Sunflower"),
    +      "NanumGothic"
    +    )
    +  ) %>% 
    +  gt_theme_pff() %>% 
    +  tab_style(
    +    style = list(
    +      "font-variant: small-caps;"
    +    ),
    +    locations = cells_body(columns = everything())
    +  ) 
    +
    +## 결합
    +
    +listed_tables <- list(curriculum_first_gt, curriculum_second_gt)
    +
    +gt_two_column_layout(listed_tables)
    +
    +
    +
    +
    +
    + + + +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    +
    +챗GPT 데이터 사이언스 건국대 전문교육과정 +
    강의주제강의내용수업방법
    1 주차 - 2023-07-01
    데이터 사이언스 언어챗GPT와 데이터 사이언스핸즈온 실습
    데이터 사이언스 언어Tidyverse와 챗GPT핸즈온 실습
    데이터 사이언스 언어파이썬/R/SQL 그리고 API핸즈온 실습
    2 주차 - 2023-07-08
    소프트웨어 카펜트리 (기본기)자동화: 유닉스 쉘핸즈온 실습
    소프트웨어 카펜트리 (기본기)버전제어: Git / GitHub핸즈온 실습
    3 주차 - 2023-07-15
    소프트웨어 카펜트리 (기본기)파이썬 / R핸즈온 실습
    소프트웨어 카펜트리 (기본기)데이터베이스핸즈온 실습
    4 주차 - 2023-07-22
    데이터 과학 자료구조숫자/범주 --> 데이터프레임핸즈온 실습
    데이터 과학 자료구조날짜/공간정보/네트워크핸즈온 실습
    데이터 과학 자료구조텍스트/이미지/오디오핸즈온 실습
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    강의주제강의내용수업방법
    5 주차 - 2023-07-29
    디지털 글쓰기, 대쉬보드, API 개발디지털 보고서(PDF, HTML, PPT)핸즈온 실습
    디지털 글쓰기, 대쉬보드, API 개발Serverless 대쉬보드, R/Python 대쉬보드핸즈온 실습
    디지털 글쓰기, 대쉬보드, API 개발기계학습 모형 API 배포핸즈온 실습
    - 주차 - 2023-08-05
    휴가휴가-
    6 주차 - 2023-08-12
    챗GPT와 오픈소스(오픈소스) 거대언어모형(LLM)핸즈온 실습
    챗GPT와 오픈소스Write/Draw/Coding/Reasoning핸즈온 실습
    챗GPT와 오픈소스AI 시대 데이터 사이언스 제품개발 방법핸즈온 실습
    7 주차 - 2023-08-19
    Prompt Engineering사용자 및 개발자를 위한 프롬프트 공학핸즈온 실습
    Prompt EngineeringOpenAI API 기반 NLP 개발핸즈온 실습
    Prompt EngineeringOPL(OpenAI, Pinecone, Langchain) 프레임워크핸즈온 실습
    8 주차 - 2023-08-26
    챗GPT AI 앱 프로젝트오픈소스 LLM 기반 AI 데이터 분석 제품핸즈온 실습
    챗GPT AI 앱 프로젝트데이터 사이언스 AutoGPT핸즈온 실습
    챗GPT AI 앱 프로젝트챗GPT AI 프로젝트 발표핸즈온 실습
    +
    +
    + +
    +
    +
    +
    +
    +
    +
    + +
    + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/slideshow/202404-sch/dashboard.html b/docs/slideshow/202404-sch/dashboard.html new file mode 100644 index 0000000..9d345ad --- /dev/null +++ b/docs/slideshow/202404-sch/dashboard.html @@ -0,0 +1,641 @@ + + + + + + + + + +발표자료 | 한국 R 사용자회 + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + + + +
    + + + + +
    +

    대쉬보드

    +
    +

    코로나19 대쉬보드

    +



    +
    +
    +

    쿼토 코로나19 제작과정 (Kulkarni 2019)

    +
    쿼토 코로나19 제작과정 (Kulkarni 2019)
    +
    +
    + +
    +
    +

    flexdashboard

    +
    +
    +

    +
    +
    + +
    +
    +

    쿼토 대시보드

    +
    +
    +

    +
    +
    + + + +
    + +
    + +
    +
    + +

    참고문헌

    +
    +Kulkarni, Sagar. 2019. “Building an HR Dashboard in R using Flexdashboard”. Towards Data Science, 9월. https://towardsdatascience.com/building-an-hr-dashboard-in-r-using-flexdashboard-76d14ed3f32. +
    +
    + +
    + + + + + + \ No newline at end of file diff --git a/docs/slideshow/202404-sch/office-interface.html b/docs/slideshow/202404-sch/office-interface.html new file mode 100644 index 0000000..00b4fd6 --- /dev/null +++ b/docs/slideshow/202404-sch/office-interface.html @@ -0,0 +1,820 @@ + + + + + + + + + +발표자료 | 한국 R 사용자회 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + + + +
    + + + + +
    +

    글쓰기
    챗GPT 인터페이스 - 오피스 전쟁

    +
    +

    구글 검색의 종말(?)

    +
    +
    +

    +
    +
    +
    +
    +

    글쓰기 역사

    +


    +

    + +
    +
    +

    오피스 전쟁

    + +++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    항목MS 오피스 1구글 오피스 2리브레 오피스 3한컴 오피스 4
    문서 작성 및 편집워드, 엑셀, 파워포인트 등의 데스크톱 및 웹 앱 제공구글 문서, 스프레드시트, 프레젠테이션 등의 웹 앱 제공라이터, 캘크, 임프레스 등의 데스크톱 앱 제공한글, 한셀, 한쇼 등의 데스크톱 및 모바일 앱 제공
    협업 및 공유원드라이브를 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움구글 드라이브를 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정하고 실시간으로 확인 가능드롭박스나 구글 드라이브와 연동하여 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움넷프리즘을 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움
    이메일 및 채팅Outlook을 통해 비즈니스 메일 제공. Teams을 통해 채팅 및 화상 회의 가능Gmail을 통해 비즈니스 메일 제공. Chat과 Meet을 통해 채팅 및 화상 회의 가능이메일 및 채팅 기능 없음이메일 및 채팅 기능 없음
    가격1인당 월 5~20달러 다양한 요금제 제공. 데스크톱 앱 사용 가능한 요금제는 12.5달러부터 시작1인당 월 6~18달러까지 다양한 요금제 제공완전 무료개인용은 무료로 사용 가능하며 업무용은 별도로 견적 요청 필요
    +
    +
    +

    오피스 전쟁: 패러다임 충돌

    +

    +
    +
    +

    챗GPT 시대 사무실

    +
    +
    +
    +

    챗GPT가 이룬 대통합

    +

    +
    +
    +

    인터페이스 (Interface)

    +
    + +
    +
    +




    +
    +
    +
    +

    +
    +
    flowchart TB
    +    
    +    subgraph A["사용자 인터페이스"]
    +    direction LR
    +        CLI["CLI<br>Command Line Interface<br> 1950~"]     --> GUI["GUI <br> Graphic User Inferface<br> 1970~"]
    +        GUI               --> WI["Web Interface <br> 1994~"]
    +        WI --> MI["Mobile Interface <br> 2007~"]
    +        MI --> NUI["LUI <br> Language User Interface <br> 2023~"]
    +
    +    end
    +
    +
    +class A nodeStyle
    +
    +classDef nodeStyle fill:#fcfbfa,stroke:#000000,stroke-width:0.7px,font-weight:bold,font-size:14px;
    +
    +
    +

    +
    +
    +
    +
    + + +
    +



    +

    +
    + + +
    +
    +
    +
    +

    챗GPT NUI

    +
    + +
    +
    +
      +
    • 챗GPT (ChatGPT)
    • +
    +
    +
    +

    +
    +
    +
    +
    +
      +
    • OpenAI Playground
    • +
    +
    +
    +

    +
    +
    +
    + +
    +
    +
    +
    +

    사례

    +
    +

    +
    +

    +
    +
    +
    +
    +
    + +
    +
    +
    +

    Command Palette 역사

    +
      +
    • 60년대로 거슬러 올라가는 명령 팔레트는 최근 기술 업계에서 다시 인기를 얻고 있으며 사용자 중심 소프트웨어의 필수적인 부분으로 간주됨.
    • +
    • CLI는 개인용 컴퓨터가 대중화되기 전 초기 컴퓨팅을 지배했으며 사용자가 시스템을 작동하기 위해 특정 명령을 알아야 했다. 1984년 Apple의 매킨토시와 함께 도입된 그래픽 사용자 인터페이스(GUI)는 중요한 혁신으로 여겨졌지만, 소프트웨어의 복잡성이 증가함에 따라 GUI만으로는 탐색하기가 점점 더 어려워졌다.
    • +
    • 사용자가 항목 이름의 짧은 약어입력하여 앱과 파일을 검색할 수 있는 런처가 점점 더 보편화 되었다. 예를 들어 2001년에 출시된 Mac OS X용 LaunchBar, Quicksilver, 2005년에 출시된 Apple의 시스템 전체 Spotlight가 있다. 검색과 CLI의 결합은 Command Palette의 탄생으로 이어졌다.
    • +
    • 오늘날 우리가 알고 있는 현대식 명령 팔레트는 2011년 Sublime Text 2 베타 Joe Skinner가 처음 소개. 명령줄 사용에 익숙한 프로그래머들이 단축키를 모두 외울 필요 없이 더 빠르게 작업할 수 있어 중요한 발전이 됨.
    • +
    +
    +
    +
    +
    +
    +
    + + +
    +
    + + +

    각주

    + +
      +
    1. https://www.microsoft.com/ko-kr/microsoft-365/business↩︎

    2. +
    3. https://workspace.google.com/intl/ko_kr/↩︎

    4. +
    5. https://www.libreoffice.org/↩︎

    6. +
    7. https://www.hancom.com/product/productMain.do↩︎

    8. +
    +
    + +
    + + + + + + + \ No newline at end of file diff --git a/docs/slideshow/202404-sch/quarto-digital-writing.html b/docs/slideshow/202404-sch/quarto-digital-writing.html new file mode 100644 index 0000000..10c8285 --- /dev/null +++ b/docs/slideshow/202404-sch/quarto-digital-writing.html @@ -0,0 +1,1380 @@ + + + + + + + + + +발표자료 | 한국 R 사용자회 + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + + + +
    + + + + +
    +

    도구(문서)의 인간
    디지털 저작

    +


    +

    +
    +

    데이터 과학 과거와 현재

    +



    +
    +
    +
    +

    과거

    +


    +

    +
    +
    +
    +

    현재

    +


    +

    +
    +
    +
    +
    +
    +

    글쓰기 진화

    +
    + +
    +
    +

    +
    +
    +

    +
    +
    +

    + + + + + + + + + +
            <!-- sourcecode --> package1 -->
    +        <!-- package1 --> product1 -->
    +        <!-- product1 --> distribute1 -->
    +        <!-- distribute1 --> service1 -->
    + + + + + + + + + +

    package2 –> product2 –> distribute2 –> service2 –>

    + + + + + + + + + + + + + +
    +
    +
    +
    +
    +

    데이터 과학 → 디지털 글쓰기

    +
    + +
    +
    +

    +
    +
    +

    +
    +
    +

    +
    +
    +
    +
    library(tidyverse)
    +library(rvest)
    +library(gt)
    +library(gtExtras)
    +
    +history_html <- read_html('https://jjallaire.quarto.pub/notebook-publishing/#/history') 
    +
    +history_url <- history_html |> 
    +  html_element('table') |> 
    +  html_elements('a') |> 
    +  html_attr("href")
    +
    +history_tbl <- history_html |> 
    +  html_element('table') |> 
    +  html_table() |> 
    +  set_names(c("연도", "도구", "개발자"))  |> 
    +  mutate(url = history_url) |> 
    +  mutate(link = str_glue("<a href={url}>{도구}</a>")) |> 
    +  select(연도, link, 개발자) |> 
    +  add_row(연도 = 2020, link = "<a href=https://quarto.org>Quarto</a>",
    +          개발자 = "J.J. Allaire")
    +
    +history_tbl |>
    +  write_csv("../../data/history_tbl.csv")
    +
    +
    +
    library(tidyverse)
    +library(rvest)
    +library(gt)
    +library(gtExtras)
    +
    +history_tbl <- 
    +  read_csv(glue::glue("{here::here()}/data/history_tbl.csv"))
    +
    +history_tbl |> 
    +  mutate(link = map(link, gt::html)) |> 
    +  select(연도, 도구=link, 개발자) |> 
    +  gt() |> 
    +    # gt_theme_hangul() |> 
    +    cols_align("center")
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    연도도구개발자
    1978TeXDonald Knuth
    1984Literate ProgrammingDonald Knuth
    1988Mathematica NotebooksStephen Wolfram
    2001IPythonFernando Perez
    2003Emacs org-modeCarsten Dominik
    2004MarkdownJohn Gruber
    2005Sage NotebookWilliam Stein
    2006PandocJohn MacFarlane
    2009GitHub Flavored MarkdownTom Preston-Werner
    2011iPython NotebookFernando Perez
    2012knitrYihui Xie
    2014Project JupyterFernando Perez
    2020QuartoJ.J. Allaire
    + +
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    +
    +
    +
    +
    +
    +
    +

    디지털 글쓰기 - 쿼토

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    + +
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    +
    +
    +

    +
    +
    + +
    +
    +


    +

    +
    +
    +



    +

    + + + + + + +

    주피터 –> 마크다운 –> 팬독 –> pdf –> html –> docx –> knitr –> 마크다운 –>

    +
    +
    +
    +
    +
    +표 1: R 마크다운과 쿼토 비교 +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    기능R 마크다운쿼토(Quarto)
    파일형식html_document / pdf_document / word_documenthtml / pdf / docx
    비머(Beamer)beamer_presentationbeamer
    파워포인트(PPT)powerpoint_presentationpptx
    웹 슬라이드xaringan / ioslides / revealjsrevealjs
    고급 레이아웃tufte / distillQuarto Article Layout
    상호 참조html_document2 / pdf_document2 / word_document2Quarto Crossrefs
    웹사이트/블로그blogdown / distillQuarto Websites / Quarto Blogs
    bookdownQuarto Books
    인터랙티브 문서Shiny DocumentsQuarto Interactive Documents
    페이지 HTMLpagedown출시 예정
    학술 논문rticlesQuarto Journal Articles
    대쉬보드flexdashboardQuarto Dashboards
    인터랙티브 자습서learnr계획 없음
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    + + + + + + \ No newline at end of file diff --git a/docs/slideshow/202404-sch/reference.html b/docs/slideshow/202404-sch/reference.html new file mode 100644 index 0000000..d8b201e --- /dev/null +++ b/docs/slideshow/202404-sch/reference.html @@ -0,0 +1,560 @@ + + + + + + + + + +발표자료 | 한국 R 사용자회 + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + + + +
    + + + + +
    +

    참고문헌

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    + + + + + \ No newline at end of file diff --git a/docs/slideshow/202404-sch/seoul-r-meeup.html b/docs/slideshow/202404-sch/seoul-r-meeup.html new file mode 100644 index 0000000..444174d --- /dev/null +++ b/docs/slideshow/202404-sch/seoul-r-meeup.html @@ -0,0 +1,659 @@ + + + + + + + + + +발표자료 | 한국 R 사용자회 + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + + + +
    + + + + + +
    +

    +
    +

    2023년 챗GPT 데이터 사이언스



    +
    +
    +

    서울 R 미트업 (2023년)

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    +
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    +

    참여와 연대

    +
      +
    • 서울R미트업 meetup.com: link +
        +
      • 23년 4월 서울 R 미트업: link
      • +
      • 23년 5월 서울 R 미트업: link
      • +
      • 23년 6월 서울 R 미트업: link
      • +
      • 23년 7월 서울 R 미트업: link
      • +
      • 23년 8월 서울 R 미트업: link
      • +
      • 23년 9월 서울 R 미트업: link
      • +
    • +
    • 세계 R 미트업 현황 (Global R Meetup Dashboard): link
    • +
    • 한국 R 사용자회 (Korea R User Group): link
    • +
    • 한국 R 컨퍼런스 (Korea R Conference): link
    • +
    • 유튜브 채널 (Youtube Channel): link
    • +
    • 페이스북 그룹 (Facebook Group): link
    • +
    +
    +
    +
    + +
    +
    +
    +

    +
    +

    2024년 인공지능 데이터 사이언스



    +
    +
    +

    서울 R 미트업

    +
    +
    +
    +

    +
    +

    +
    +
    +
    +
    +

    참여와 연대

    +
      +
    • 서울R미트업 meetup.com: link +
        +
      • 23년 4월 서울 R 미트업: link
      • +
      • 23년 5월 서울 R 미트업: link
      • +
      • 23년 6월 서울 R 미트업: link
      • +
      • 23년 7월 서울 R 미트업: link
      • +
      • 23년 8월 서울 R 미트업: link
      • +
      • 23년 9월 서울 R 미트업: link
      • +
    • +
    • Resources for General Help with R - Regional R communities link
    • +
    • 세계 R 미트업 현황 (Global R Meetup Dashboard): link
    • +
    • 한국 R 사용자회 (Korea R User Group): link
    • +
    • 한국 R 컨퍼런스 (Korea R Conference): link
    • +
    • 유튜브 채널 (Youtube Channel): link
    • +
    • 페이스북 그룹 (Facebook Group): link
    • +
    +
    +
    +
    + + +
    +
    + +
    + +
    + + + + + + \ No newline at end of file diff --git a/img/quarto_workflow_mermaid.png b/img/quarto_workflow_mermaid.png new file mode 100644 index 0000000000000000000000000000000000000000..1a2ed162de594a8c31087278aca68c711228f90c GIT binary patch literal 33733 zcmeFZc{J30+&4ZbM3%BuLN3V`F3A$j7*s--EXjUF_AS|Wl_iAAR@Ra1Teh)N3Pp_U z`^03KER!W;EHl5)pzFTx-*cbmcb-3f&pFRIk8`drX1>ek`~JMw*ZcMUMk2J;m>AA5 zKp+q%^_$mpArRUy2!wiuo)&zd-^YAu@>rUw!E3qia@@!GxsOylUg9}IrA2p7 ze@Qr+`dIDzf3ES|HezqLhRfc*Ps_f`-hTTyEeBn4Z3x3PHMhlDB>l0!-t;QN&vCP! zV>D7GVYut;ViU*f+H zn8UjNfE?Kfkt#H7r(LRpHx(bYy#84rt{sivvL7{MI0m*B{N$hNR?R9>^44nRLZP4G z`o;cY?}IV#e}!bAKR5BG)!+#9*WZ3vNgVcZR^TX-FCqu>O?fr9H( zWgToaoP+g${Ai)lEk5A*{ynUh#~e#z#$7Wxc9tq#P50^?*P3Q+2Y<@A4aQW?eCXh` zUXN?UGP!|Uw(VX=w>!5q$*uS=topbRJC~Z%z0RJJ9QO`Ld++Ipr(m^< z!S_5TNU3#h^MT~2wiy<@B9~6d{{vsT;*~5vT`d{+k$FOBsMMr}SbdzgO4W3&Io^^C zy)lbsU7e^%bi;Yd*(zZ&S~IgJzP^G%gMvDAtXe~lN$qV*&iZ%7X&eYg*ey_Y55p3C zMW~0o>-~n5K_^G2@`bTz=#yD+;CYD#65Z;k>#jwy&poXmokiWP^`Zver|z&oy$R!B 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zTRzf9z5F8QNExq4>HneH7@(0p-hue~1Z#v$U_dy5{?gw3Lhx3xK>cLQ_-CYyvW`*9 zb1Afa{y(IQua7@rL7|$JOl(xFMq16eFG)X0w&S1V!b@=^GWMu=GK!S(n!kYm7naU` Uj;8s=BLDyZ07*qoM6N<$g0#cz=l}o! literal 0 HcmV?d00001 diff --git a/metadata.yml b/metadata.yml index a56e143..4ce7f9f 100644 --- a/metadata.yml +++ b/metadata.yml @@ -1,5 +1,23 @@ # Internal slide decks +- title: "챗GPT 데이터 사이언스" + subtitle: "데이터 사이언스 패러다임 전환" + author: "이광춘" + date: 2024-04-29 + image: img/sch_logo.jpg + path: chatgpt_sch_202404.qmd + categories: + - "챗GPT" + - "인공지능" + - "R마크다운" + - "서울 R 미트업" + - "패러다임 전환" + - "데이터 과학" + - "한국 R 사용자회" + - "쿼토(Quarto)" + - "R/Tidyverse" + - "LLM" + - title: "차세대 대쉬보드" subtitle: "flexdashboard → 쿼토 대시보드" author: "이광춘" diff --git a/slideshow/20230324-kwangmyung/index.ipynb b/slideshow/20230324-kwangmyung/index.ipynb index c236660..61796b0 100644 --- a/slideshow/20230324-kwangmyung/index.ipynb +++ b/slideshow/20230324-kwangmyung/index.ipynb @@ -26,7 +26,7 @@ " chunk_output_type: console\n", "---" ], - "id": "d118df53" + "id": "141b17c3" }, { "cell_type": "markdown", @@ -379,7 +379,7 @@ "\n", "### 크롤링: 코드" ], - "id": "e0449942" + "id": "68ee5e94" }, { "cell_type": "code", @@ -444,7 +444,7 @@ "# Print a confirmation message\n", "print('samsung.csv saved successfully.')" ], - "id": "9880209f", + "id": "7ac64a85", "execution_count": null, "outputs": [] }, @@ -598,7 +598,7 @@ "\n", "### `Sympy` 동원" ], - "id": "4f13266f" + "id": "04d24c34" }, { "cell_type": "code", @@ -610,7 +610,7 @@ "Diophantus_puzzle = x/6 + x/12 + x/7 + 5 + x/2 + 4 - x\n", "solve(Diophantus_puzzle, x, dict = True)" ], - "id": "26daded5", + "id": "07f374d8", "execution_count": null, "outputs": [] }, @@ -708,7 +708,7 @@ "\n", ":::\n" ], - "id": "1ce6a1b7" + "id": "e215f30f" } ], "metadata": { diff --git a/slideshow/202304-gpt/index.ipynb b/slideshow/202304-gpt/index.ipynb index 1f398b6..c4587cf 100644 --- a/slideshow/202304-gpt/index.ipynb +++ b/slideshow/202304-gpt/index.ipynb @@ -26,7 +26,7 @@ " chunk_output_type: console\n", "---" ], - "id": "439b0d32" + "id": "5c07d544" }, { "cell_type": "markdown", @@ -327,7 +327,7 @@ "\n", "### `Sympy` 동원" ], - "id": "57c0adcf" + "id": "fae004d3" }, { "cell_type": "code", @@ -339,7 +339,7 @@ "Diophantus_puzzle = x/6 + x/12 + x/7 + 5 + x/2 + 4 - x\n", "solve(Diophantus_puzzle, x, dict = True)" ], - "id": "a85446e0", + "id": "f5531281", "execution_count": null, "outputs": [] }, @@ -562,7 +562,7 @@ "::: {#refs}\n", ":::\n" ], - "id": "5560cf70" + "id": "4c3c53cb" } ], "metadata": { diff --git a/slideshow/202404-sch/book.qmd b/slideshow/202404-sch/book.qmd new file mode 100644 index 0000000..e26fe71 --- /dev/null +++ b/slideshow/202404-sch/book.qmd @@ -0,0 +1,109 @@ +# 챗GPT 도서출판 + +## 챗GPT 데이터 과학 도서 {.smaller} + +::: panel-tabset +### 챗GPT 시리즈 + +![](img/book/chatGPT_series3.png){fig-align="center" width="552"} + +### 데이터 과학 + +::: {#chatGPT-ds layout-ncol="2"} +![](img/book/cover_ds.jpg){fig-align="center" width="250"} + +![](img/book/ds_logo.png){fig-align="center" width="300"} +::: + +📘 교보 POD 종이책: + +📗 교보 전자책: + +🌐 웹사이트: + +💻 소스코드: [@lee2024ds] + +### 코딩 + +::: {#chatGPT-coding layout-ncol="2"} +![](img/book/cover_coding.jpg){fig-align="center" width="250"} + +![](img/book/coding_logo.png){fig-align="center" width="300"} +::: + +📘 교보 POD 종이책: + +📗 교보 전자책: + +🌐 웹사이트: + +💻 소스코드: [@lee2024coding] + +### 쿼토 + +::: {#chatGPT-sql layout-ncol="2"} +![](img/book/cover_quarto.jpg){fig-align="center" width="250"} + +![](img/book/quarto_logo){fig-align="center" width="300"} +::: + +📘 교보 POD 종이책: + +📗 교보 전자책: + +🌐 웹사이트: + +💻 소스코드: [@lee2024quarto] + +### SQL + +::: {layout-ncol="2"} +![](img/book/sql_cover.jpg){fig-align="center" width="250"} + +![](img/book/sql_logo.png){fig-align="center" width="300"} +::: + +📘 교보 POD 종이책: + +📗 교보 전자책: + +🌐 웹사이트: + +💻 소스코드: [@lee2024sql] + +### 유닉스 쉘 + +::: {#chatGPT-shell layout-ncol="2"} +![](img/book/cover_unix.jpg){fig-align="center" width="250"} + +![](img/book/hex_chatGPT_unix_shell_02.png){fig-align="center" width="300"} +::: + +📘 교보 POD 종이책: + +📗 교보 전자책: [https://bit.ly/KzUdOU](https://bit.ly/3KzUdOU) + +🌐 웹사이트: + +💻 소스코드: [https://bit.ly/3wrgeuP](https://bit.ly/49p0I1f) [@lee2023shell] + +📰 뉴스기사: [챗GPT가 던진 새 가능성, 데이터 과학 분야 통해 미리 살펴보기](https://omn.kr/25f0i) + + +### 확률통계 + +::: {#chatGPT-ds layout-ncol="2"} + +준비중 + +::: + +📘 교보 POD 종이책: 준비중 + +📗 교보 전자책: 준비중 + +🌐 웹사이트: + +💻 소스코드: + +::: diff --git a/slideshow/202404-sch/chatgpt-data-science.qmd b/slideshow/202404-sch/chatgpt-data-science.qmd new file mode 100644 index 0000000..b8a5268 --- /dev/null +++ b/slideshow/202404-sch/chatgpt-data-science.qmd @@ -0,0 +1,341 @@ +--- +editor: + markdown: + wrap: 72 +--- + +# 생성형 AI
      데이터 사이언스 + +## 포스터 제작 + +::: panel-tabset +### 포스터 시안 + +![](img/plugin_canva.jpg){fig-align="center" width="359"} + +### 포스터 편집 + +![](img/plugin_canva_edit.jpg){fig-align="center" width="580"} + +### 최종 포스터 + +![](img/plugin_canva_poster.jpg){fig-align="center" width="353"} +::: + +- [ShareGPT: Canva + 사용사례](https://chat.openai.com/share/9689351d-6f4b-475c-96e4-618e46aca82b) + +## API 프로그래밍 {.smaller} + +::: panel-tabset +### 준비 프롬프트 + +> 웹사이트를 읽고 다음 지령을 준비해 +> https://apis.map.kakao.com/web/documentation/ + +``` markdown +Kakao 지도 API 문서는 다양한 지도 관련 기능을 제공한다. 이 문서는 지도 객체 생성, 이벤트, 메서드 등에 대한 상세한 정보를 담고 있다. 아래는 주요 내용의 요약이다. + +### 지도 객체 생성 +- `kakao.maps.Map(container, options)`: 지도를 생성한다. + - `container`: 지도가 표시될 HTML element + - `options`: 중심 좌표, 확대 수준, 지도 종류 등을 설정할 수 있다. + +### 주요 메서드 +- `setCenter(latlng)`: 지도의 중심 좌표를 설정한다. +- `getCenter()`: 지도의 중심 좌표를 반환한다. +- `setLevel(level, options)`: 지도의 확대 수준을 설정한다. +- `getLevel()`: 지도의 확대 수준을 반환한다. +- `setMapTypeId(mapTypeId)`: 지도의 타입을 설정한다. +- `getMapTypeId()`: 지도의 타입을 반환한다. + +### 이벤트 +- `center_changed`: 중심 좌표가 변경되면 발생한다. +- `zoom_start`: 확대 수준이 변경되기 직전에 발생한다. +- `zoom_changed`: 확대 수준이 변경되면 발생한다. +- `click`: 지도를 클릭하면 발생한다. +- `dblclick`: 지도를 더블클릭하면 발생한다. + +이 외에도 다양한 메서드와 이벤트가 있어, 개발자가 원하는 기능을 쉽게 구현할 수 있다. +``` + +### 코드제작 프롬프트롬 + +> 주소를 넘기면 위경도 좌표를 반환하는 R 코드를 작성해줘. + +R에서 주소를 위경도 좌표로 변환하기 위해 `httr` 패키지를 사용하여 Kakao +API를 호출한 예시 코드이다. + +``` r +# 필요한 패키지를 설치하고 불러온다 +install.packages("httr") +install.packages("jsonlite") +library(httr) +library(jsonlite) + +# 주소를 위경도로 변환하는 함수를 정의한다 +get_latlng <- function(address) { + # Kakao API 키를 설정한다 (본인의 API 키로 교체해야 한다) + api_key <- "YOUR_KAKAO_API_KEY" + + # API URL과 파라미터를 설정한다 + url <- "https://dapi.kakao.com/v2/local/search/address.json" + params <- list(query = address) + headers <- add_headers("Authorization" = paste("KakaoAK", api_key)) + + # API를 호출한다 + response <- GET(url, query = params, headers) + + # 응답을 확인하고 JSON을 파싱한다 + if (status_code(response) == 200) { + content <- content(response, "text", encoding = "UTF-8") + json_data <- fromJSON(content) + + # 위경도를 추출한다 + if (json_data$meta$total_count > 0) { + lat <- json_data$documents[[1]]$y + lng <- json_data$documents[[1]]$x + return(list(lat = lat, lng = lng)) + } else { + return(NULL) + } + } else { + stop("API request failed") + } +} + +# 함수를 테스트한다 +address <- "서울특별시 강남구 테헤란로 212" +result <- get_latlng(address) +print(paste("Latitude:", result$lat, "Longitude:", result$lng)) +``` + +### 코드수정 + +```{r} +#| code-line-numbers: "10" +#| eval: false +#| echo: true + +library(httr) +library(jsonlite) + +# 주소를 위경도로 변환하는 함수를 정의한다 +get_latlng <- function(address) { + # Kakao API 키를 설정한다 (본인의 API 키로 교체해야 한다) + api_key <- Sys.getenv("KAKAO_MAP_API_KEY") + + # API URL과 파라미터를 설정한다 + url <- "https://dapi.kakao.com/v2/local/search/address.json" + params <- list(query = address) + headers <- add_headers("Authorization" = paste("KakaoAK", api_key)) + + # API를 호출한다 + response <- GET(url, query = params, headers) + + # 응답을 확인하고 JSON을 파싱한다 + if (status_code(response) == 200) { + content <- content(response, "text", encoding = "UTF-8") + json_data <- fromJSON(content) + + # 위경도를 추출한다 + if (json_data$meta$total_count > 0) { + lat <- json_data$documents[[1]]$y + lng <- json_data$documents[[1]]$x + return(list(lat = lat, lng = lng)) + } else { + return(NULL) + } + } else { + stop("API request failed") + } +} + +# 가톨릭관동대학교 주소 +address <- "강원도 강릉시 범일로579번길 24" +result <- get_latlng(address) +print(paste("Latitude:", result$lat, "Longitude:", result$lng)) +``` + +### 작업결과 + +![](img/plugin_map.png){fig-align="center" width="700"} +::: + +- [ShareGPT: API + 사용사례](https://chat.openai.com/share/dcfee23a-0853-454b-9d8c-f76fc4474cb6), + [플러그인(Plugin) - 칸바, 카카오맵, + 울프람](https://r2bit.com/chatGPT/plugin.html) + +## 사실확인 (Wolfram) {.smaller} + +::: panel-tabset +### 지구는 평평해? + +울프람 알파(Wolfram Alpha) 사용해서 사실관계를 확인해보자. + +> "지구는 평평하다"라는 주장을 검증해줘. + +``` +"지구는 평평하다"라는 주장에 대해 Wolfram Alpha를 이용해 검증해보았습니다. + +### 입력 해석 +- 질문: 지구는 평평하다? + +### 결과 +- 답변: 아니다. + - 지역적으로만 그렇다. 그리고 그것도 특정 지역, 예를 들어 미국 중서부에서만 해당된다. + +자세한 정보는 [Wolfram|Alpha 웹사이트](https://www6b3.wolframalpha.com/input?i=Is+the+Earth+flat%3F)에서 확인할 수 있다. + +이처럼 Wolfram Alpha는 다양한 주장이나 정보에 대한 팩트체크를 할 수 있는 유용한 도구이다. +``` + +- [Wolfram\|Alpha + 웹사이트](https://www6b3.wolframalpha.com/input?i=Is+the+Earth+flat%3F) + +### 라면은 건강에 안 좋아? + +> 라면을 많이 먹으면 건강에 정말 안좋은가? + +Wolfram Alpha에서는 "라면을 많이 먹으면 건강에 정말 안 좋은가?"에 대한 +구체적인 답변을 제공하지 못했다. 그러나 일반적으로 라면은 높은 나트륨, +지방, 인공 첨가물 등을 포함하고 있어, 과도하게 섭취할 경우 다음과 같은 +건강 문제가 발생할 수 있다: + +1. **고혈압**: 라면에는 나트륨이 많이 들어있다. 나트륨 과다 섭취는 + 혈압을 높일 수 있다. + +2. **비만**: 라면은 칼로리가 높고, 지방도 많이 들어있다. 지속적으로 + 라면을 많이 먹으면 체중 증가의 위험이 있다. + +3. **소화기 문제**: 라면은 소화가 잘 되지 않는 식품 중 하나이다. 과도한 + 섭취는 소화기에 부담을 줄 수 있다. + +4. **영양 불균형**: 라면은 영양소가 적고, 나트륨이나 지방 등의 불필요한 + 성분이 많다. 이로 인해 영양 불균형이 발생할 수 있다. + +5. **심혈관 질환**: 고혈압, 높은 콜레스테롤 등은 심혈관 질환의 위험을 + 높인다. + +라면을 즐길 때는 이러한 점들을 고려하여 적당한 양과 빈도로 섭취하는 것이 +중요하다. +::: + +## 수능문제 풀이 {.smaller} + +::: panel-tabset +### 2023 수능 수학 + +2023학년도 대학수학능력시험 수학영역 문제를 풀어보자. + +![](img/plugin_wolfram.jpg) + +### 문제 풀이 + +> $\left(\frac{4}{2^{\sqrt{2}}}\right)^{2+\sqrt{2}}$ 값은? + +식 $$ +\left(\frac{4}{2^{\sqrt{2}}}\right)^{2+\sqrt{2}} +$$ 의 값은 $4$입니다. + +정확한 형태로는 $2^{(2 - \sqrt{2}) (2 + \sqrt{2})}$로 표현됩니다. + +자세한 정보는 [Wolfram\|Alpha +웹사이트](https://www6b3.wolframalpha.com/input?i=%284+%2F+%282%5E%28sqrt%282%29%29%29%29%5E%282+%2B+sqrt%282%29%29)에서 +확인할 수 있다. + +### 풀이 과정 + +> 과정도 설명해줘. + +::: columns +::: column +물론이다. 주어진 식은 $$ +\left(\frac{4}{2^{\sqrt{2}}}\right)^{2+\sqrt{2}} +$$이다. 이 식을 단계별로 분해해보자. + +- 단계 1: 분모를 간단하게 만들기
      먼저, 분모인 $2^{\sqrt{2}}$를 + 간단하게 만들기 위해 분자와 분모에 $2^{\sqrt{2}}$를 곱한다. 이렇게 + 하면: + +$$ +\left(\frac{4 \times 2^{\sqrt{2}}}{2^{\sqrt{2}} \times 2^{\sqrt{2}}}\right)^{2+\sqrt{2}} +$$ + +- 단계 2: 분모를 합치기
      이제 분모의 + $2^{\sqrt{2}} \times 2^{\sqrt{2}}$를 합쳐 $2^{2\sqrt{2}}$로 만든다. + +$$ +\left(\frac{4 \times 2^{\sqrt{2}}}{2^{2\sqrt{2}}}\right)^{2+\sqrt{2}} +$$ +::: + +::: column +- 단계 3: 분자를 합치기
      분자의 $4 \times 2^{\sqrt{2}}$를 합쳐 + $2^2 \times 2^{\sqrt{2}} = 2^{2+\sqrt{2}}$로 만든다. + +$$ +\left(\frac{2^{2+\sqrt{2}}}{2^{2\sqrt{2}}}\right)^{2+\sqrt{2}} +$$ + +- 단계 4: 지수 법칙 적용
      지수 법칙에 따라 분자와 분모의 지수를 + 빼준다. + +$$ +\left(2^{(2+\sqrt{2}) - 2\sqrt{2}}\right)^{2+\sqrt{2}} = \left(2^{(2 - \sqrt{2})(2 + \sqrt{2})}\right)^{2+\sqrt{2}} +$$ + +- 단계 5: 최종 계산
      마지막으로, $(2 - \sqrt{2})(2 + \sqrt{2})$를 + 계산하면 $4 - 2 = 2$가 된다. 따라서 최종 결과는 $2^2 = 4$이다. + +이렇게 해서 $$ +\left(\frac{4}{2^{\sqrt{2}}}\right)^{2+\sqrt{2}} = 4 +$$임을 확인할 수 있다. +::: +::: +::: + +::: aside +[[ShareGPT: 팩트체크와 수학문제 +풀이](https://chat.openai.com/share/e3e57271-9956-4348-abe9-4a87bc56a984)]{.aside} +::: + +## OpenAI Advanced Data Analysis {.smaller} + +
      + +> Open AI Code Interpreter → Advanced Data Analysis → 챗GPT4 + +
      + +![](img/km_code_interpreter.png) + +::: aside +[생성AI 광명시 데이터 분석 +사례](https://r2bit.com/map_challenge/km_BitGPT.html) +::: + +## RStudio Copilot + +> `Tools -> Global Options -> Copilot -> Enable Github Copilot` + +![](img/rstudio_copilot.jpg) + +## 웹앱(Shiny App) 개발 사례 + +![](img/shiny_app_ci.gif) + +## 광명시 보좌관 + +![](img/km_ai.gif){fig-align="center" width="728"} + +- [소스코드](https://github.com/bit2r/BitStat2) + +## RTutor & PandasAI + +![](img/rtutor_pandasai.png){fig-align="center" width="450"} + +::: aside +- [챗GPT 데이터 과학](https://r2bit.com/gpt-ds/) +::: diff --git a/slideshow/202404-sch/curriculum.qmd b/slideshow/202404-sch/curriculum.qmd new file mode 100644 index 0000000..72181be --- /dev/null +++ b/slideshow/202404-sch/curriculum.qmd @@ -0,0 +1,366 @@ +# 통계 → 데이터 사이언스 → 챗GPT 인공지능 + +## 교육과정 {.smaller} + +::: panel-tabset +### 종이와 연필 + +![](img/cku_statistics.jpg){fig-align="center" width="413"} + +### 통계 보고서 + +![](img/cku_sas_word.png){fig-align="center" width="715"} + +### DS 입문 + +```{r} +library(tidyverse) +library(rvest) + +library(gt) +library(gtExtras) + +ds_url <- "https://statkclee.github.io/yonsei/" +ds_html <- read_html(ds_url) + +ds_html |> + html_elements('table') |> + html_table() %>% + .[[1]] |> + slice(2:n()) |> + separate(X2, into = c("학습내용", "날짜"), sep = "-") |> + separate(학습내용, into = c("주차", "학습내용"), sep = "\\.") |> + select(주차, 학습내용, 날짜) |> + drop_na() |> + mutate(날짜 = str_trim(날짜), + 학습내용 = str_trim(학습내용)) |> + mutate(날짜 = if_else(str_detect(날짜, "^\\d"), 날짜, NA_character_)) |> + gt() |> + gt_theme_pff() |> + sub_missing( + columns = 날짜, + missing_text = "-" + ) |> + cols_align("center") |> + opt_table_font( + font = list( + google_font(name = "Sunflower"), + "NanumGothic" + ) + ) %>% + tab_style( + style = list( + "font-variant: small-caps;" + ), + locations = cells_body(columns = everything()) + ) %>% + tab_header( + title = + htmltools::tagList( + htmltools::tags$div( + style = htmltools::css( + `text-align` = "center" + ), + htmltools::HTML( + web_image("img/curriculum_yonsei.png") + ) + ), + htmltools::tags$div( + htmltools::tags$strong("연세대"), + "데이터 사이언스 입문" + ) + ) + ) |> + tab_options( + table.width = "65%", + heading.title.font.size = px(12) + ) + +``` + +- [연세대 "데이터 과학 입문"](https://statkclee.github.io/yonsei/) + +### 고급 DS + +```{r} +ds2_url <- "https://statkclee.github.io/yonsei2/" +ds2_html <- read_html(ds2_url) + +ds2_html |> + html_elements('table') |> + html_table() %>% + .[[1]] |> + slice(2:n()) |> + separate(X2, into = c("학습내용", "날짜"), sep = "∽̱") |> + separate(학습내용, into = c("주차", "학습내용"), sep = "\\.") |> + select(주차, 학습내용, 날짜) |> + drop_na() |> + mutate(날짜 = str_trim(날짜), + 학습내용 = str_trim(학습내용)) |> + mutate(날짜 = if_else(str_detect(날짜, "^\\d"), 날짜, NA_character_)) |> + gt() |> + gt_theme_pff() |> + sub_missing( + columns = 날짜, + missing_text = "-" + ) |> + cols_align("center") |> + opt_table_font( + font = list( + google_font(name = "Sunflower"), + "NanumGothic" + ) + ) %>% + tab_style( + style = list( + "font-variant: small-caps;" + ), + locations = cells_body(columns = everything()) + ) %>% + tab_header( + title = + htmltools::tagList( + htmltools::tags$div( + style = htmltools::css( + `text-align` = "center" + ), + htmltools::HTML( + web_image("img/curriculum_yonsei2.png") + ) + ), + htmltools::tags$div( + htmltools::tags$strong("연세대"), + "데이터 (사이언스) 공학" + ) + ) + ) |> + tab_options( + table.width = "65%", + heading.title.font.size = px(12) + ) +``` + +- [연세대 "데이터 (사이언스) 공학"](https://statkclee.github.io/yonsei2/) + +### GS 칼텍스 + +```{r} +#| echo: false +library(readxl) +library(tidyverse) + +citizen_courses <- read_excel(glue::glue("{here::here()}/data/curriculum_2023.xlsx"), sheet = "level_01") + +citizen_courses %>% + fill(c(일정, 과정)) %>% + mutate(과정 = str_remove(과정, "^\\d+\\.\\s+")) %>% + group_by(일정, 과정, 데이터셋) %>% + summarize(과정상세 = str_c(과정상세, collapse = "
      ")) |> + ungroup() |> + relocate(데이터셋, .after = 과정상세) |> + ## 표 작성 + gt() %>% + gt_theme_pff() %>% + tab_style( + style = list( + "font-variant: small-caps;" + ), + locations = cells_body(columns = everything()) + ) |> + sub_missing(columns = everything(), + missing_text = "-") |> + fmt_markdown(columns = TRUE) |> + tab_options( + table.width = "100%", + heading.title.font.size = px(12) + ) |> + cols_align("center") |> + opt_table_font( + font = list( + google_font(name = "Sunflower"), + "NanumGothic" + ) + ) %>% + tab_header( + title = + htmltools::tagList( + htmltools::tags$div( + style = htmltools::css( + `text-align` = "center" + ), + htmltools::HTML( + web_image("img/curriculum_gs_caltex.png") + ) + ), + htmltools::tags$div( + htmltools::tags$strong("GS 칼텍스"), + "시민 데이터 과학" + ) + ) + ) + +``` + +- [GS 칼텍스 데이터 사이언스](https://r2bit.com/curriculum/) + + +### GS 칼텍스 + +```{r} +#| echo: false +courses <- tribble(~"과목명", ~"학습상세", ~"일정", ~"데이터셋", + "환경설정", "학습 도구 설치 및 학습환경", "00주차", "파머 펭귄, BitData", + "디지털 글쓰기(보고서)", "마크다운 / Quarto", "01주차", "BitData", + "명령라인 데이터 분석", "자동화(Shell)", "02주차", "SWC", + "버전 제어", "버전제어/협업(Git / GitHub / GitLab)", "03주차", "", + "프로그래밍", "SQL", "04주차", "Data Carpentry, DVD 렌탈", + "프로그래밍", "시각화", "05주차", "gapminder", + "프로그래밍", "파이썬", "06주차", "", + "대쉬보드", "Flexdashboard / Shinydashboard", "07주차", "", + "기계학습", "tidymodels / scikit-learn", "08주차", "", + "데이터 과학 제품", "RESTful API", "09주차", "", + "특수 데이터", "시계열(Time Series)", "10주차", "KOSPI", + "특수 데이터", "공간정보(Geospatial) / 텍스트 분석", "11주차", "미디어오늘", + "비정형 데이터", "텍스트와 이미지 (딥러닝)", "12주차", "") + +courses %>% + select(일정, everything()) %>% + gt() %>% + gt_theme_pff() %>% + tab_options( + table.width = "100%", + heading.title.font.size = px(17) + ) |> + tab_style( + style = list( + "font-variant: small-caps;" + ), + locations = cells_body(columns = everything()) + ) |> + opt_table_font( + font = list( + google_font(name = "Sunflower"), + "NanumGothic" + ) + ) %>% + cols_align( + align = "center", + columns = everything() + ) |> + tab_header( + title = + htmltools::tagList( + htmltools::tags$div( + style = htmltools::css( + `text-align` = "center" + ), + htmltools::HTML( + web_image("img/curriculum_gs_caltex.png") + ) + ), + htmltools::tags$div( + htmltools::tags$strong("GS 칼텍스"), + "데이터 과학" + ) + ) + ) + +``` + +- [GS 칼텍스 데이터 사이언스](https://r2bit.com/curriculum/) + + + +### 챗GPT DS {.smaller} + +```{r} +library(tidyverse) +library(readxl) +library(gt) +library(gtExtras) +extrafont::loadfonts() + +curriculum_raw <- read_excel(glue::glue("{here::here()}/data/건국대_커리큘럼.xlsx")) + +curriculum <- curriculum_raw %>% + fill(everything(), .direction="down") %>% + mutate(날짜 = as.Date(날짜)) %>% + mutate(주차 = glue::glue("{주차} 주차")) + +## 첫번째 +curriculum_first <- curriculum %>% + filter(dplyr::row_number() <=10) + +curriculum_first_gt <- curriculum_first %>% + gt(groupname_col = c("주차", "날짜")) %>% + cols_align(align = "center") %>% + opt_table_font( + font = list( + google_font(name = "Sunflower"), + "NanumGothic" + ) + ) %>% + gt_theme_pff() %>% + tab_style( + style = list( + "font-variant: small-caps;" + ), + locations = cells_body(columns = everything()) + ) %>% + tab_header( + title = + htmltools::tagList( + htmltools::tags$div( + style = htmltools::css( + `text-align` = "center" + ), + htmltools::HTML( + web_image("img/konkuk/tools.png") + ) + ), + htmltools::tags$div( + htmltools::tags$strong("챗GPT 데이터 사이언스"), + "건국대 전문교육과정" + ) + ) + ) + +## 두번째 +curriculum_second <- curriculum %>% + filter(dplyr::row_number() > 10) + +curriculum_second_gt <- curriculum_second %>% + gt(groupname_col = c("주차", "날짜")) %>% + cols_align(align = "center") %>% + opt_table_font( + font = list( + google_font(name = "Sunflower"), + "NanumGothic" + ) + ) %>% + gt_theme_pff() %>% + tab_style( + style = list( + "font-variant: small-caps;" + ), + locations = cells_body(columns = everything()) + ) + +## 결합 + +listed_tables <- list(curriculum_first_gt, curriculum_second_gt) + +gt_two_column_layout(listed_tables) +``` + +### 평가 + +
      + +![](img/education_evaluation.jpg) + +- [데이터 사이언스 MCQ](https://r2bit.com/curriculum/mcq_stat.html) + +::: + + diff --git a/slideshow/202404-sch/dashboard.qmd b/slideshow/202404-sch/dashboard.qmd new file mode 100644 index 0000000..a7fd25a --- /dev/null +++ b/slideshow/202404-sch/dashboard.qmd @@ -0,0 +1,41 @@ + + +# 대쉬보드 + +## 코로나19 대쉬보드 + +

      + +![쿼토 코로나19 제작과정 [@kulkarni2019building]](img/dashboard_workflow.jpg) + +::: aside + +- 대시보드: +- 소스코드: +- 제작방법: + +::: + +## flexdashboard + +![](img/dashboard_flexdashboard.png){fig-align="center" width="788"} + +::: aside + +- 대시보드: +- 소스코드: + +::: + +## 쿼토 대시보드 + +![](img/dashboard_quarto.png){fig-align="center" width="675"} + +::: aside + +- 대시보드: +- 소스코드: 공개준비중 + +::: + + diff --git a/slideshow/202404-sch/office-interface.qmd b/slideshow/202404-sch/office-interface.qmd new file mode 100644 index 0000000..f497157 --- /dev/null +++ b/slideshow/202404-sch/office-interface.qmd @@ -0,0 +1,176 @@ +# **글쓰기**
      챗GPT 인터페이스 - 오피스 전쟁 + +## 구글 검색의 종말(?) + +![](img/chatGPT-search.jpg){fig-align="center" width="700"} + +## 글쓰기 역사 + +
      + +![](img/writing_history.jpg) + +::: aside +- [문자와 종이 역사](https://r2bit.com/quarto/writing_history.html) +::: + +## 오피스 전쟁 {.smaller} + +| 항목 | MS 오피스 [^1] | 구글 오피스 [^2] | 리브레 오피스 [^3] | 한컴 오피스 [^4] | +|:-------------:|:-------------:|:-------------:|:-------------:|:-------------:| +| 문서 작성 및 편집 | 워드, 엑셀, 파워포인트 등의 데스크톱 및 웹 앱 제공 | 구글 문서, 스프레드시트, 프레젠테이션 등의 웹 앱 제공 | 라이터, 캘크, 임프레스 등의 데스크톱 앱 제공 | 한글, 한셀, 한쇼 등의 데스크톱 및 모바일 앱 제공 | +| 협업 및 공유 | 원드라이브를 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움 | 구글 드라이브를 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정하고 실시간으로 확인 가능 | 드롭박스나 구글 드라이브와 연동하여 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움 | 넷프리즘을 통해 문서를 클라우드에 저장하고 공유 가능. 여러 사람이 동시에 문서를 수정할 수 있으나 실시간으로 보기 어려움 | +| 이메일 및 채팅 | Outlook을 통해 비즈니스 메일 제공. Teams을 통해 채팅 및 화상 회의 가능 | Gmail을 통해 비즈니스 메일 제공. Chat과 Meet을 통해 채팅 및 화상 회의 가능 | 이메일 및 채팅 기능 없음 | 이메일 및 채팅 기능 없음 | +| 가격 | 1인당 월 5\~20달러 다양한 요금제 제공. 데스크톱 앱 사용 가능한 요금제는 12.5달러부터 시작 | 1인당 월 6\~18달러까지 다양한 요금제 제공 | 완전 무료 | 개인용은 무료로 사용 가능하며 업무용은 별도로 견적 요청 필요 | + +[^1]: https://www.microsoft.com/ko-kr/microsoft-365/business + +[^2]: https://workspace.google.com/intl/ko_kr/ + +[^3]: https://www.libreoffice.org/ + +[^4]: https://www.hancom.com/product/productMain.do + +## 오피스 전쟁: 패러다임 충돌 + +![](img/writing-evolution.jpg) + +## 챗GPT 시대 사무실 + +::: panel-tabset +### 챗GPT 사용원인 + +![](img/chatGPT_why.jpg){width="600"} + +### 인터페이스 + +![](img/chatGPT_interface.jpg){width="500"} + +### 챗GPT AS-IS + +![](img/chatGPT-AS-IS.jpg) + +### 챗GPT TO-BE + +![](img/chatGPT-TO-BE.jpg) +::: + +## 챗GPT가 이룬 대통합 + +![](img/chatGPT-Text-to-X.jpg) + +## 인터페이스 (Interface) {.smaller} + +::: panel-tabset +### 진화과정 + +


      + +```{mermaid} +flowchart TB + + subgraph A["사용자 인터페이스"] + direction LR + CLI["CLI
      Command Line Interface
      1950~"] --> GUI["GUI
      Graphic User Inferface
      1970~"] + GUI --> WI["Web Interface
      1994~"] + WI --> MI["Mobile Interface
      2007~"] + MI --> NUI["LUI
      Language User Interface
      2023~"] + + end + + +class A nodeStyle + +classDef nodeStyle fill:#fcfbfa,stroke:#000000,stroke-width:0.7px,font-weight:bold,font-size:14px; +``` + +### CLI + +
      + +![](img/ui_cli.jpg) + +- + +### GUI + +
      + +![](img/ui_gui.jpg) + +- + +### WUI + +

      + +![](img/ui_web.jpg) + +### MUI + +![](img/ui_mi.jpg) + +- + +### LUI + +![](img/sam_altman_LUI.jpg) + +- +::: + +## 챗GPT NUI {.smaller} + +::: panel-tabset +### 1:1 채팅 + +- 챗GPT (ChatGPT) + +![](img/chatgpt_writing.gif){fig-align="center" width="634"} + +### 클릭하여 완료 + +- OpenAI Playground + +![](img/ui_playground.jpg){fig-align="center" width="645"} + +### 자동완성 + +- GitHub Copilot + +![](img/ui_copilot.gif){fig-align="center"} + +- + +### 명령 팔레트 + +::: columns +::: column +#### 사례 + +::: r-stack +![](img/command-tree.gif) + +::: fragment +![](img/generate_code.gif) +::: + +::: fragment +{{< video https://blog.replit.com/images/ai/aimode_future.mp4 width="500" height="400" >}} +::: +::: + +- +- +::: + +::: column +#### Command Palette 역사 + +- 60년대로 거슬러 올라가는 명령 팔레트는 최근 기술 업계에서 다시 인기를 얻고 있으며 사용자 중심 소프트웨어의 필수적인 부분으로 간주됨. +- CLI는 개인용 컴퓨터가 대중화되기 전 초기 컴퓨팅을 지배했으며 사용자가 시스템을 작동하기 위해 특정 명령을 알아야 했다. 1984년 Apple의 매킨토시와 함께 도입된 그래픽 사용자 인터페이스(GUI)는 중요한 혁신으로 여겨졌지만, 소프트웨어의 복잡성이 증가함에 따라 GUI만으로는 탐색하기가 점점 더 어려워졌다. +- 사용자가 항목 이름의 짧은 약어입력하여 앱과 파일을 검색할 수 있는 런처가 점점 더 보편화 되었다. 예를 들어 2001년에 출시된 Mac OS X용 LaunchBar, Quicksilver, 2005년에 출시된 Apple의 시스템 전체 Spotlight가 있다. 검색과 CLI의 결합은 **Command Palette**의 탄생으로 이어졌다. +- 오늘날 우리가 알고 있는 현대식 명령 팔레트는 2011년 Sublime Text 2 베타 Joe Skinner가 처음 소개. 명령줄 사용에 익숙한 프로그래머들이 단축키를 모두 외울 필요 없이 더 빠르게 작업할 수 있어 중요한 발전이 됨. +::: +::: +::: diff --git a/slideshow/202404-sch/quarto-digital-writing.qmd b/slideshow/202404-sch/quarto-digital-writing.qmd new file mode 100644 index 0000000..8fe914d --- /dev/null +++ b/slideshow/202404-sch/quarto-digital-writing.qmd @@ -0,0 +1,216 @@ +# 도구(문서)의 인간
      디지털 저작 + +
      + +![](img/homo-faber.jpg) + +## 데이터 과학 과거와 현재 {.smaller} + +

      + +::: columns +::: column +### 과거 + +
      + +![](slideshow/202212-youtube/fig/jobshop.gif) +::: + +::: column +### 현재 + +
      + +![](slideshow/202212-youtube/fig/quarto_to_go.gif) +::: +::: + +## 글쓰기 진화 {.smaller} + +::: panel-tabset +### 글쓰기 역사 + +![](img/writing_history.jpg) + +### 생산성과 자동화를 넘어 + +![](img/writing-evolution.jpg) + +### 개발과 저작 + +![](img/mermaid_docu_sw.jpg) + + + + + + + + + + + + package1 --> + product1 --> + distribute1 --> + service1 --> + + + + + + + + + + + package2 --> + product2 --> + distribute2 --> + service2 --> + + + + + + + + + + + + + + + + +::: + +## 데이터 과학 → 디지털 글쓰기 {.smaller} + +::: panel-tabset +### 데이터 과학 + +![](img/horst-eco-r4ds.png) + +### 문서 구성요소 + +![](img/data_science_document.jpg) + +### 문서 복잡성 + +![](img/markdown-writing.png){width="880"} + +### 문서 도구 개발 역사 + +```{r} +#| eval: false +library(tidyverse) +library(rvest) +library(gt) +library(gtExtras) + +history_html <- read_html('https://jjallaire.quarto.pub/notebook-publishing/#/history') + +history_url <- history_html |> + html_element('table') |> + html_elements('a') |> + html_attr("href") + +history_tbl <- history_html |> + html_element('table') |> + html_table() |> + set_names(c("연도", "도구", "개발자")) |> + mutate(url = history_url) |> + mutate(link = str_glue("
      {도구}")) |> + select(연도, link, 개발자) |> + add_row(연도 = 2020, link = "Quarto", + 개발자 = "J.J. Allaire") + +history_tbl |> + write_csv("../../data/history_tbl.csv") +``` + +```{r} +library(tidyverse) +library(rvest) +library(gt) +library(gtExtras) + +history_tbl <- + read_csv(glue::glue("{here::here()}/data/history_tbl.csv")) + +history_tbl |> + mutate(link = map(link, gt::html)) |> + select(연도, 도구=link, 개발자) |> + gt() |> + # gt_theme_hangul() |> + cols_align("center") +``` +::: + + +## 디지털 글쓰기 - 쿼토 {.smaller} + +::: panel-tabset +### 삼권분립 + +![](img/three-pillar.png){fig-align="center" width="615"} + +::: aside +[챗GPT 디지털 글쓰기](https://r2bit.com/gpt-writing/) +::: + + +### 쿼토 소개 + +
      + +![](img/horst_quarto_schematic.png) + +### 쿼토 작업흐름 + +
      +
      + +![](img/quarto_workflow_mermaid.png) + + + + + + + + 주피터 --> + 마크다운 --> + 팬독 --> + pdf --> + html --> + docx --> + knitr --> + 마크다운 --> + + +### 쿼토 + +| 기능 | R 마크다운 | 쿼토(Quarto) | +|:-----------------:|:-----------------------------|:-----------------------| +| 파일형식 | [html_document](https://pkgs.rstudio.com/rmarkdown/reference/html_document.html) / [pdf_document](https://pkgs.rstudio.com/rmarkdown/reference/pdf_document.html) / [word_document](https://pkgs.rstudio.com/rmarkdown/reference/word_document.html) | [html](https://quarto.org/docs/output-formats/html-basics.html) / [pdf](https://quarto.org/docs/output-formats/pdf-basics.html) / [docx](https://quarto.org/docs/output-formats/ms-word.html) | +| 비머(Beamer) | [beamer_presentation](https://pkgs.rstudio.com/rmarkdown/reference/beamer_presentation.html) | [beamer](https://quarto.org/docs/presentations/beamer.html) | +| 파워포인트(PPT) | [powerpoint_presentation](https://pkgs.rstudio.com/rmarkdown/reference/powerpoint_presentation.html) | [pptx](https://quarto.org/docs/presentations/powerpoint.html) | +| 웹 슬라이드 | [xaringan](https://bookdown.org/yihui/rmarkdown/xaringan.html) / [ioslides](https://bookdown.org/yihui/rmarkdown/ioslides-presentation.html) / [revealjs](https://bookdown.org/yihui/rmarkdown/revealjs.html) | [revealjs](https://quarto.org/docs/presentations/revealjs/) | +| 고급 레이아웃 | [tufte](https://bookdown.org/yihui/rmarkdown/tufte-handouts.html) / [distill](https://rstudio.github.io/distill/figures.html) | [Quarto Article Layout](https://quarto.org/docs/authoring/article-layout.html) | +| 상호 참조 | [html_document2](https://bookdown.org/yihui/bookdown/a-single-document.html) / [pdf_document2](https://bookdown.org/yihui/bookdown/a-single-document.html) / [word_document2](https://bookdown.org/yihui/bookdown/a-single-document.html) | [Quarto Crossrefs](https://quarto.org/docs/authoring/cross-references.html) | +| 웹사이트/블로그 | [blogdown](https://pkgs.rstudio.com/blogdown/) / [distill](https://pkgs.rstudio.com/distill/) | [Quarto Websites](https://quarto.org/docs/websites/) / [Quarto Blogs](https://quarto.org/docs/websites/website-blog.html) | +| 책 | [bookdown](https://pkgs.rstudio.com/bookdown/) | [Quarto Books](https://quarto.org/docs/books/) | +| 인터랙티브 문서 | [Shiny Documents](https://bookdown.org/yihui/rmarkdown/shiny-documents.html) | [Quarto Interactive Documents](https://quarto.org/docs/interactive/shiny/) | +| 페이지 HTML | [pagedown](https://github.com/rstudio/pagedown) | 출시 예정 | +| 학술 논문 | [rticles](https://pkgs.rstudio.com/rticles/) | [Quarto Journal Articles](https://quarto.org/docs/journals/) | +| 대쉬보드 | [flexdashboard](https://pkgs.rstudio.com/flexdashboard/) | [Quarto Dashboards](https://quarto.org/docs/dashboards/) | +| 인터랙티브 자습서 | [learnr](https://pkgs.rstudio.com/learnr/) | 계획 없음 | + +: R 마크다운과 쿼토 비교 {#tbl-quarto-rmarkdown} + +::: + diff --git a/slideshow/202404-sch/reference.qmd b/slideshow/202404-sch/reference.qmd new file mode 100644 index 0000000..95053d9 --- /dev/null +++ b/slideshow/202404-sch/reference.qmd @@ -0,0 +1,8 @@ +# 참고문헌 + +::: {#refs} +::: + + + + \ No newline at end of file diff --git a/slideshow/202404-sch/seoul-r-meeup.qmd b/slideshow/202404-sch/seoul-r-meeup.qmd new file mode 100644 index 0000000..1c2d1d6 --- /dev/null +++ b/slideshow/202404-sch/seoul-r-meeup.qmd @@ -0,0 +1,111 @@ + + +# {background-image="img/meetup_logo.jpg"} + +::: r-fit-text + +2023년 챗GPT 데이터 사이언스 +



      +::: + +## 서울 R 미트업 (2023년) {.smaller} + +::: columns +::: column +::: r-stack + +![](img/seoul-r-meetup-september.jpg){.fragment width="320"} + +::: fragment +![](img/seoul-r-meetup-august.jpg){.fragment width="320"} +::: + + +::: fragment +![](img/seoul-r-meetup-july.jpg){.fragment width="320"} +::: + +::: fragment +![](img/seoulr-r-meetup-june.jpg){.fragment width="320"} +::: + +::: fragment +![](img/seoul-r-meetup-202305.jpg){.fragment width="320"} +::: + +::: fragment +![](img/seoul-r-meetup-AsiaR.jpg){.fragment width="320"} +::: + +::: fragment +![](img/seoul-r-meetup-202304.jpg){.fragment width="320"} +::: + + +::: +::: + +::: column +## 참여와 연대 + +- **서울R미트업 meetup.com**: [link](https://www.meetup.com/seoul-r-meetup) + - **23년 4월 서울 R 미트업**: [link](https://www.meetup.com/seoul-r-meetup/events/292232899/) + - **23년 5월 서울 R 미트업**: [link](https://www.meetup.com/seoul-r-meetup/events/292966674/) + - **23년 6월 서울 R 미트업**: [link](https://www.meetup.com/seoul-r-meetup/events/293485362/) + - **23년 7월 서울 R 미트업**: [link](https://www.meetup.com/seoul-r-meetup/events/293929603/) + - **23년 8월 서울 R 미트업**: [link](https://www.meetup.com/seoul-r-meetup/events/294651161/) + - **23년 9월 서울 R 미트업**: [link](https://www.meetup.com/seoul-r-meetup/events/295338359/) +- 세계 R 미트업 현황 (Global R Meetup Dashboard): [link](https://r-community.org/usergroups/) +- 한국 R 사용자회 (Korea R User Group): [link](https://r2bit.com/) +- 한국 R 컨퍼런스 (Korea R Conference): [link](https://use-r.kr/) +- 유튜브 채널 (Youtube Channel): [link](https://www.youtube.com/channel/UCW-epmIvjBEhhVXw_F0Nqbw) +- 페이스북 그룹 (Facebook Group): [link](https://www.facebook.com/groups/tidyverse) +::: +::: + + + +# {background-image="img/meetup_logo.jpg"} + +::: r-fit-text + +2024년 인공지능 데이터 사이언스 +



      +::: + +## 서울 R 미트업 {.smaller} + +::: columns +::: column +::: r-stack + +![](img/meetup/20240430_poster.png){.fragment width="320"} + +::: fragment +![](img/meetup/20240430_poster.png){.fragment width="320"} +::: + + + +::: +::: + +::: column +## 참여와 연대 + +- **서울R미트업 meetup.com**: [link](https://www.meetup.com/seoul-r-meetup) + - **23년 4월 서울 R 미트업**: [link](https://www.meetup.com/seoul-r-meetup/events/300281000/) + - **23년 5월 서울 R 미트업**: [link]() + - **23년 6월 서울 R 미트업**: [link]() + - **23년 7월 서울 R 미트업**: [link]() + - **23년 8월 서울 R 미트업**: [link]() + - **23년 9월 서울 R 미트업**: [link]() +- Resources for General Help with R - Regional R communities [link](https://contributor.r-project.org/r-help) +- 세계 R 미트업 현황 (Global R Meetup Dashboard): [link](https://r-community.org/usergroups/) +- 한국 R 사용자회 (Korea R User Group): [link](https://r2bit.com/) +- 한국 R 컨퍼런스 (Korea R Conference): [link](https://use-r.kr/) +- 유튜브 채널 (Youtube Channel): [link](https://www.youtube.com/channel/UCW-epmIvjBEhhVXw_F0Nqbw) +- 페이스북 그룹 (Facebook Group): [link](https://www.facebook.com/groups/tidyverse) +::: +::: +

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