From b8ae407ea8786e56e303ac3380a48a1ae0b600c9 Mon Sep 17 00:00:00 2001 From: Daniel R Date: Sat, 3 Aug 2024 15:23:23 -0400 Subject: [PATCH 1/5] 04_project_showcase added in. --- .../markdown_slides/04_project_showcase | 172 ++++++++++++++++++ 1 file changed, 172 insertions(+) create mode 100644 instructional_team/markdown_slides/04_project_showcase diff --git a/instructional_team/markdown_slides/04_project_showcase b/instructional_team/markdown_slides/04_project_showcase new file mode 100644 index 00000000..16a15ada --- /dev/null +++ b/instructional_team/markdown_slides/04_project_showcase @@ -0,0 +1,172 @@ +--- +marp: true +theme: dsi-certificates-theme +_class: invert +paginate: true +--- + +# Introduction to Data Science Portfolios + +``` +$ echo "Data Sciences Institute" +``` + + + + + + +--- + +# Welcome to Team Project Part 2 + +- Team Project Part 2 is designed to address specific issues we observed in Part 1 of our certificates program. In this module, we'll guide you through improvements in your project work, better utilization of GitHub, and sharpening your presentation skills—all aimed at enhancing your employability in the field of data science. + + + +--- + + +# What is a Good Portfolio? + +A good portfolio showcases your best work and highlights skills that are highly valued by employers in data science and machine learning. It demonstrates your ability to handle real-world tasks, making you an attractive candidate for employment. + +**Personalize Your Portfolio:** +- **Remove generic content:** For instance, delete the default README we provided. It's designed to describe the module, not your unique project. +- **Highlight your unique contributions and skills:** This personalization shows employers that you're not just completing assignments but are engaged and innovating on your projects. + +--- + + +# Types of Projects to Include in Your Portfolio + +Including a diverse range of projects in your portfolio can significantly enhance your appeal to potential employers. Consider including these key project types to demonstrate your breadth and depth in data science: + +1. **Data Cleaning Project:** Show your ability to prepare data for analysis. +2. **Data Storytelling and Visualization Project:** Highlight your skills in interpreting and presenting data in compelling ways. +3. **ML Modeling:** Demonstrate your proficiency in building and tuning models. +4. **Group Project:** Showcase your teamwork and collaboration skills. (already doing this! 🥳) + +For more, read "[How to Create a Project Portfolio for Data Science Job Applications](https://www.dataquest.io/blog/career-guide-data-science-projects-portfolio/)" + + +--- + +# Selecting Projects That Showcase Your Skills + +Choosing the right projects for your portfolio is crucial. Each project should: + +- **Solve Real Problems**: Use actual datasets to address genuine issues in your field. +- **Demonstrate Industry Relevance**: Select projects that are pertinent to your specific area, such as marketing analytics for marketers or predictive maintenance for engineers. +- **Provide Actionable Insights**: Focus on projects that deliver clear, practical outcomes that demonstrate your ability to impact real-world scenarios. + +These criteria ensure that your projects not only highlight your technical skills but also your understanding of and adaptability to industry-specific challenges, making you a valuable candidate to potential employers. + +--- + +# Showcase Projects That Use Diverse Data Types + +Enhance your projects by effectively using a mix of structured, unstructured, and time series data: + +- **Structured Data**: Employ database data for clear, quantifiable insights. +- **Unstructured Data**: Add depth with text, images, or videos. +- **Time Series Data**: Utilize data in sequential order for trend analysis and forecasting. + +Select data types strategically to align with your project's objectives. Ensure each type contributes to a clear and coherent narrative, avoiding unnecessary complexity to maintain focus and utility. + +--- + +# Demonstrating Technical and Non-Technical Skills + +- **Demonstrate Coding Proficiency**: Include projects that showcase your ability to write clean, efficient, and well-documented code. +- **Showcase Communication Skills**: Feature projects where you’ve effectively communicated complex data-driven insights to non-technical stakeholders. + +Your ability to document code clearly and communicate effectively can drastically reduce onboarding times for new team members and help non-technical stakeholders make informed decisions, thereby enhancing overall project success and team efficiency. + +--- + + +# Crafting a Comprehensive Main README File + +- **Purpose & Overview:** Introduce the project with essential details and a concise description. +- **Goals & Objectives:** Articulate what the project aims to achieve. +- **Techniques & Technologies:** Highlight the tools and methods used. +- **Key Findings & Instructions:** Summarize outcomes and provide setup instructions. +- **Visuals & Credits:** Enhance with visuals; acknowledge contributors. + + + +--- + + +# Better Documentation - README + +While **you can have multiple README files** in your repository, adhere to the rule of maintaining just one README file per folder to keep each section clear and organized. This structure not only aids in navigation but also showcases your ability to manage and present complex information effectively, increasing your appeal to potential employers. + + +--- + +# Better Documentation - Comments + +**Effective commenting enhances code readability and maintainability, crucial for collaborative environments.** + +A lot of participants had code that wasn't properly commented. The idea here is that if someone with no tech background reads your code, they should get the essence of what you're doing and how the code flows. This practice not only aids in understanding but also facilitates smoother transitions and updates within team projects. + +📰 [Best practices for writing code comments](https://stackoverflow.blog/2021/12/23/best-practices-for-writing-code-comments/) + +--- +## Example + +❌ +``` +function calculateTotal(price, quantity) { + return price * quantity; +} + +let totalPrice = calculateTotal(25, 5); +console.log(totalPrice); // Output: 125 +``` + +✅ +``` +// Calculates the total cost by multiplying the price per item with the quantity +function calculateTotal(price, quantity) { + return price * quantity; +} + +// Example usage: Calculates the total price for 5 items at $25 each by multiplying the price +// per item ($25) with the quantity (5), and stores the result in the totalPrice variable. +let totalPrice = calculateTotal(25, 5); +console.log(totalPrice); // Output: 125 +``` + +--- + + +# Better Documentation - STAR Method + +- The STAR method is typically used for answering interview questions, applying this structure to your project overviews can be highly effective. +- By organizing your projects using the Situation, Task, Action, Result format, your portfolio essentially speaks for you, conducting a virtual interview with potential employers even in your absence. +- This approach ensures that employers can clearly understand the value and impact of your work, even without direct interaction. + +--- + +# Effective GitHub Repository Organization + +- Ensure your GitHub repository is neatly organized; avoid unused or empty folders and ensure each folder has a clear purpose. +- Use READMEs in key folders (like `src`, `iac`, `backend`, `frontend`, etc.) to detail their contents and purpose, aiding clarity for complex sections. +- Avoid READMEs in simple folders (like `images`, `docs`, etc.), unless there's specific information that needs to be explained. + +--- + +# Effective Presentation of Findings + +- Focus on crafting your project presentations to be clear and to the point. Utilize storytelling techniques to make your data findings compelling and employ visualizations to simplify and clarify complex information. + +- Your ability to communicate insights effectively is **crucial** — well-presented findings can make your work stand out to potential employers and demonstrate your capability to aid decision-makers like managers or CEOs in understanding critical data quickly. This approach not only showcases your skills but significantly enhances your employability. + +--- + +# Questions? \ No newline at end of file From 9c9828390f5ee1ad823a795a0a418fc03149cb1b Mon Sep 17 00:00:00 2001 From: Daniel R Date: Mon, 5 Aug 2024 15:11:37 -0400 Subject: [PATCH 2/5] Making changes to slide 4 and adding slide 5 --- .../markdown_slides/04_project_showcase | 6 +- .../05_project_planning_and_impact | 158 ++++++++++++++++++ 2 files changed, 160 insertions(+), 4 deletions(-) create mode 100644 instructional_team/markdown_slides/05_project_planning_and_impact diff --git a/instructional_team/markdown_slides/04_project_showcase b/instructional_team/markdown_slides/04_project_showcase index 16a15ada..a3dbb2da 100644 --- a/instructional_team/markdown_slides/04_project_showcase +++ b/instructional_team/markdown_slides/04_project_showcase @@ -18,11 +18,9 @@ $ echo "Data Sciences Institute" --- -# Welcome to Team Project Part 2 - -- Team Project Part 2 is designed to address specific issues we observed in Part 1 of our certificates program. In this module, we'll guide you through improvements in your project work, better utilization of GitHub, and sharpening your presentation skills—all aimed at enhancing your employability in the field of data science. - +# Module Overview +The content in this module is a response to the observations made during Team Project 1. There is no specific structure to the slides, as they present a collection of important points based on the feedback and common issues identified. --- diff --git a/instructional_team/markdown_slides/05_project_planning_and_impact b/instructional_team/markdown_slides/05_project_planning_and_impact new file mode 100644 index 00000000..577eaa0c --- /dev/null +++ b/instructional_team/markdown_slides/05_project_planning_and_impact @@ -0,0 +1,158 @@ +--- +marp: true +theme: dsi-certificates-theme +_class: invert +paginate: true +--- + +# Effective Project Planning and Time Management + +``` +$ echo "Data Sciences Institute" +``` + + + + + +--- + +# Module Overview + +This is a continuation of our reaction to what we observed in Team Project 1. The slides present key points and recommendations rather than following a specific order. Each slide addresses different aspects that need improvement, and they do not necessarily build on each other. + +--- + +# Strategic Planning for Short and Long-Term Goals + +Setting realistic time frames for your project stages is crucial. Use S.M.A.R.T goals to structure your planning effectively: + +- **Specific**: Define clear details about what needs to be achieved, who's involved, and where it will occur. +- **Measurable**: Establish concrete criteria for measuring progress and success. +- **Achievable**: Ensure the goals are realistic, considering available resources and constraints. +- **Relevant**: Align goals with broader business objectives to ensure they are meaningful. +- **Time-bound**: Set definitive deadlines to maintain focus and urgency. + + + +--- + +### Example: Customer Retention Model + +**Scenario**: Develop a predictive model to improve customer retention for a subscription-based service. + +- **Specific**: Machine learning model to predict customer churn using six months of user activity and demographic data. +- **Measurable**: Target 80% accuracy on a validation set. +- **Achievable**: Use Python’s Scikit-Learn and a marketing department dataset. +- **Relevant**: Aligns with the company’s goal of reducing churn and improving retention. +- **Time-bound**: Initial model version in three months, with testing and deployment in the next quarter. + +--- + +### Example: Dataset Preparation + +**Scenario**: Prepare a dataset for urgent customer feedback analysis. + +- **Specific**: Clean feedback data from the latest product launch, removing duplicates and handling missing values. +- **Measurable**: No missing values or duplicates, verified by data quality checks. +- **Achievable**: Use Python’s Pandas library for data manipulation. +- **Relevant**: Crucial for immediate analysis to inform the marketing team’s next steps. +- **Time-bound**: Complete in two days to start analysis on schedule. +--- + +# Leveraging Project Management Methodologies + +Explore methodologies like Scrum, Kanban, Waterfall, and Agile on your own. For now, let's focus on an essential concept: **spikes**. + +**Spikes** are short, time-boxed research or exploration tasks designed to answer specific questions or solve particular problems before starting the main project work. + +### Benefits of Spikes: + +- **Clarify Requirements**: Quickly gather necessary information. +- **Solve Problems Early**: Address technical uncertainties before main development. +- **Prevent Delays**: Avoid excessive brainstorming that can stall progress. + + + +--- + + +# Demonstrating the Business Impact of Your Projects + +Understanding and articulating the business value of your data science and machine learning projects is crucial for two main reasons: + +### Benefits: + +- **Educational**: Apply new technologies and methodologies from both data science and machine learning. +- **Employability**: Showcase your ability to solve real-world problems, making you attractive to potential employers. + +Highlight your skills through projects by using relevant industry examples and metrics, such as operational improvements, technical innovations, or strategic insights. + + + +--- + +# Examples Across Sectors (Part 1) + +### Healthcare Example +"Improved patient outcomes by developing a predictive model using Python and scikit-learn. Data was preprocessed using pandas to handle missing values and standardize features. Logistic regression was selected through cross-validation due to its interpretability and performance." + +### Retail Example +"Optimized inventory management with SQL and Tableau. Data was cleaned and aggregated using SQL queries to identify sales patterns. Visualizations in Tableau helped to forecast demand, leading to cost savings." + +*⏭️ Continued on next slide...* + +--- + +# Examples Across Sectors (Part 2) + +### Marketing Example +Increased customer engagement by performing customer segmentation using k-means clustering with Python. Feature engineering was conducted to create meaningful segments based on purchase history and demographics. The optimal number of clusters was determined using the elbow method, which helps identify the point where adding more clusters does not significantly improve the model. + +--- + +# How to Effectively Communicate Business Impact + +Showcase your project’s business value by applying specialized skills: + +- **📊 Data Science Stream**: Apply the techniques learned in your sampling and visualization modules to deliver impactful data insights. +- **⚙️ Machine Learning Stream**: Demonstrate model efficiency and algorithm optimization with detailed performance graphs and clear pipeline visualizations. + +Ensure your presentations translate complex data into clear, actionable business insights. + + + +--- + +# Making Complex Concepts Accessible + +Communicating complex data science concepts clearly is essential for audiences without technical backgrounds, **including recruiters and HR teams.** + +Strategies to Enhance Understandability: +- **Use Analogies and Metaphors**: Bridge understanding gaps with relatable comparisons. +- **Incremental Explanation**: Simplify concepts step-by-step. +- **Visual Aids**: Employ diagrams and infographics for clarity. + +Clear communication ensures your projects are comprehensible to all, enhancing your professional appeal and broadening project impact. + + + +--- + +# Next Steps Post-Certification + +As you transition from the certificate program, continue to build and showcase your skills. + +### Strategies for Continuous Growth: + +- **Active Portfolio**: Aim for 5-6 substantial projects to demonstrate a broad range of skills. Focus on quality over quantity. +- **Explore and Learn**: Continue exploring new areas within data science and machine learning to keep your knowledge current and dynamic. +- **Peer Collaboration**: Participate in group projects with fellow graduates to enhance your skills and widen your professional network. + +Actively applying for jobs should proceed in tandem with these activities. + + + +--- + +# Questions and Answers \ No newline at end of file From 26632fb1cb2489c92429893d909429fbb8480b14 Mon Sep 17 00:00:00 2001 From: Daniel R Date: Mon, 5 Aug 2024 15:16:04 -0400 Subject: [PATCH 3/5] generating slides. --- instructional_team/generate_slides.sh | 2 +- ...roject_showcase => 04_project_showcase.md} | 0 ...pact => 05_project_planning_and_impact.md} | 0 slides/01_team_project_overview.pdf | Bin 6106203 -> 6113965 bytes slides/02_teamwork_and_presenting.pdf | Bin 435499 -> 443540 bytes slides/03a_case_study_intro.pdf | Bin 83973 -> 88138 bytes slides/04_project_showcase.pdf | Bin 0 -> 153047 bytes slides/05_project_planning_and_impact.pdf | Bin 0 -> 159679 bytes 8 files changed, 1 insertion(+), 1 deletion(-) rename instructional_team/markdown_slides/{04_project_showcase => 04_project_showcase.md} (100%) rename instructional_team/markdown_slides/{05_project_planning_and_impact => 05_project_planning_and_impact.md} (100%) create mode 100644 slides/04_project_showcase.pdf create mode 100644 slides/05_project_planning_and_impact.pdf diff --git a/instructional_team/generate_slides.sh b/instructional_team/generate_slides.sh index 0a52c6db..528ae844 100755 --- a/instructional_team/generate_slides.sh +++ b/instructional_team/generate_slides.sh @@ -2,7 +2,7 @@ # CONFIGURATION folder_md="./markdown_slides" -folder_output="../slides/" # This will be used for both PDF and HTML +folder_output="../slides" # This will be used for both PDF and HTML # Clear the screen for the splash screen clear diff --git a/instructional_team/markdown_slides/04_project_showcase b/instructional_team/markdown_slides/04_project_showcase.md similarity index 100% rename from instructional_team/markdown_slides/04_project_showcase rename to instructional_team/markdown_slides/04_project_showcase.md diff --git a/instructional_team/markdown_slides/05_project_planning_and_impact b/instructional_team/markdown_slides/05_project_planning_and_impact.md similarity index 100% rename from instructional_team/markdown_slides/05_project_planning_and_impact rename to instructional_team/markdown_slides/05_project_planning_and_impact.md diff --git a/slides/01_team_project_overview.pdf b/slides/01_team_project_overview.pdf index 7be8ddef26b92f1b6185c460f81aed1fcc429f2a..5bf123e82914214c33f797cac731fac3c74fb96c 100644 GIT binary patch delta 177440 zcmZ6yV{|566D}OvnAo;$+qP$7TX$^RwmES!aWb)O+jpFtdB5}hdCresyH>B>yVma7 zRb6$})f=miV6~}FU<0F1^tq1Cc|aITuM2e9Bvmtv{DN`(UWL8n~cYm_d9N&OtZ5zep; 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zHM2j7BXXXJblIf2>=S_t5{gi}Euh%&#vH24&gbCLn-sanJ1x&*I(7Zu{Hs0FG#|Ho zLjtAvBIybu(E*kWXZgKMBoADxfrM$tpIR`PYONiY6%|L7DQ$W(JAZ)=1f=V7Jf2ZQ ha2SODO)m2Av~c(Ib^n`0_=Q9T|30;0VNrOk_#b9n!*u`v literal 0 HcmV?d00001 From ac432b1931b4b0132bea669d2646b0120daae8f6 Mon Sep 17 00:00:00 2001 From: Daniel R Date: Tue, 6 Aug 2024 14:16:59 -0400 Subject: [PATCH 4/5] updating terminology --- .../markdown_slides/05_project_planning_and_impact.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/instructional_team/markdown_slides/05_project_planning_and_impact.md b/instructional_team/markdown_slides/05_project_planning_and_impact.md index 577eaa0c..69b7d343 100644 --- a/instructional_team/markdown_slides/05_project_planning_and_impact.md +++ b/instructional_team/markdown_slides/05_project_planning_and_impact.md @@ -147,11 +147,11 @@ As you transition from the certificate program, continue to build and showcase y - **Active Portfolio**: Aim for 5-6 substantial projects to demonstrate a broad range of skills. Focus on quality over quantity. - **Explore and Learn**: Continue exploring new areas within data science and machine learning to keep your knowledge current and dynamic. -- **Peer Collaboration**: Participate in group projects with fellow graduates to enhance your skills and widen your professional network. +- **Peer Collaboration**: Participate in group projects with fellow participants to enhance your skills and widen your professional network. Actively applying for jobs should proceed in tandem with these activities. - + --- From 53d87e16f59ba547030f76947d120725c8e12184 Mon Sep 17 00:00:00 2001 From: Daniel R Date: Tue, 6 Aug 2024 16:44:32 -0400 Subject: [PATCH 5/5] Update to the content addressing comments. --- .../markdown_slides/04_project_showcase.md | 30 +++++++++++---- .../05_project_planning_and_impact.md | 37 +++++++++++++++++-- 2 files changed, 57 insertions(+), 10 deletions(-) diff --git a/instructional_team/markdown_slides/04_project_showcase.md b/instructional_team/markdown_slides/04_project_showcase.md index a3dbb2da..624e07ff 100644 --- a/instructional_team/markdown_slides/04_project_showcase.md +++ b/instructional_team/markdown_slides/04_project_showcase.md @@ -38,7 +38,7 @@ A good portfolio showcases your best work and highlights skills that are highly # Types of Projects to Include in Your Portfolio -Including a diverse range of projects in your portfolio can significantly enhance your appeal to potential employers. Consider including these key project types to demonstrate your breadth and depth in data science: +Including a diverse range of projects in your portfolio can significantly enhance your appeal to potential employers. Consider including a variety of project types to demonstrate the breadth and depth of your data science skills: 1. **Data Cleaning Project:** Show your ability to prepare data for analysis. 2. **Data Storytelling and Visualization Project:** Highlight your skills in interpreting and presenting data in compelling ways. @@ -54,7 +54,7 @@ For more, read "[How to Create a Project Portfolio for Data Science Job Applicat Choosing the right projects for your portfolio is crucial. Each project should: -- **Solve Real Problems**: Use actual datasets to address genuine issues in your field. +- **Solve Real Problems**: Use actual datasets to address genuine issues in your field. (*examples in the next slide*) - **Demonstrate Industry Relevance**: Select projects that are pertinent to your specific area, such as marketing analytics for marketers or predictive maintenance for engineers. - **Provide Actionable Insights**: Focus on projects that deliver clear, practical outcomes that demonstrate your ability to impact real-world scenarios. @@ -62,6 +62,21 @@ These criteria ensure that your projects not only highlight your technical skill --- +# Some Open Source Real Datasets + +- **University of Toronto Libraries Data**: [onesearch.library.utoronto.ca/researchdata](https://onesearch.library.utoronto.ca/researchdata/accessing-data-and-tools) +- **Open Government Data Portal (Canada)**: [open.canada.ca/data](https://open.canada.ca/data) +- **Ontario Data Catalogue**: [data.ontario.ca](https://data.ontario.ca) +- **Kaggle Datasets**: [kaggle.com/datasets](https://www.kaggle.com/datasets) +- **UCI Machine Learning Repository**: [archive.ics.uci.edu/ml](https://archive.ics.uci.edu/ml/index.php) +- **World Bank Open Data**: [data.worldbank.org](https://data.worldbankzorg) + +Utilize these resources to find relevant datasets that can help you build projects showcasing your ability to solve real-world problems, demonstrate industry relevance, and provide actionable insights. + +--- + +Let me know if this works for you or if you need any further adjustments! + # Showcase Projects That Use Diverse Data Types Enhance your projects by effectively using a mix of structured, unstructured, and time series data: @@ -74,10 +89,10 @@ Select data types strategically to align with your project's objectives. Ensure --- -# Demonstrating Technical and Non-Technical Skills +# Demonstrating Technical and Business Skills -- **Demonstrate Coding Proficiency**: Include projects that showcase your ability to write clean, efficient, and well-documented code. -- **Showcase Communication Skills**: Feature projects where you’ve effectively communicated complex data-driven insights to non-technical stakeholders. +- **Demonstrate Coding Proficiency**: All projects should showcase your ability to write clean, efficient, and well-documented code. +- **Showcase Communication Skills**: All projects should effectively communicated complex data-driven insights to Non-technical team members. Your ability to document code clearly and communicate effectively can drastically reduce onboarding times for new team members and help non-technical stakeholders make informed decisions, thereby enhancing overall project success and team efficiency. @@ -86,7 +101,7 @@ Your ability to document code clearly and communicate effectively can drasticall # Crafting a Comprehensive Main README File -- **Purpose & Overview:** Introduce the project with essential details and a concise description. +- **Purpose & Overview:** Introduce the project with essential details, concise description and a project objective. - **Goals & Objectives:** Articulate what the project aims to achieve. - **Techniques & Technologies:** Highlight the tools and methods used. - **Key Findings & Instructions:** Summarize outcomes and provide setup instructions. @@ -101,7 +116,7 @@ Focus on the essence of what makes a README effective: clarity and conciseness. # Better Documentation - README -While **you can have multiple README files** in your repository, adhere to the rule of maintaining just one README file per folder to keep each section clear and organized. This structure not only aids in navigation but also showcases your ability to manage and present complex information effectively, increasing your appeal to potential employers. +While **you can have multiple README files** in your repository, include only one README file per folder to keep things clean and simple. This structure showcases your ability to manage and present complex information effectively, increasing your appeal to potential employers. --- @@ -148,6 +163,7 @@ console.log(totalPrice); // Output: 125 - The STAR method is typically used for answering interview questions, applying this structure to your project overviews can be highly effective. - By organizing your projects using the Situation, Task, Action, Result format, your portfolio essentially speaks for you, conducting a virtual interview with potential employers even in your absence. - This approach ensures that employers can clearly understand the value and impact of your work, even without direct interaction. +- Remember, the results don't have to be ground breaking, anything that showcases your abilities in data science/machine learning, even "**learning**" counts as an important result. --- diff --git a/instructional_team/markdown_slides/05_project_planning_and_impact.md b/instructional_team/markdown_slides/05_project_planning_and_impact.md index 69b7d343..d312dce7 100644 --- a/instructional_team/markdown_slides/05_project_planning_and_impact.md +++ b/instructional_team/markdown_slides/05_project_planning_and_impact.md @@ -64,15 +64,20 @@ Setting realistic time frames for your project stages is crucial. Use S.M.A.R.T Explore methodologies like Scrum, Kanban, Waterfall, and Agile on your own. For now, let's focus on an essential concept: **spikes**. -**Spikes** are short, time-boxed research or exploration tasks designed to answer specific questions or solve particular problems before starting the main project work. +**Spikes** are short, time-boxed research tasks designed to answer specific questions or solve particular problems before starting the main project work. -### Benefits of Spikes: +--- + +# Benefits of Spikes - **Clarify Requirements**: Quickly gather necessary information. - **Solve Problems Early**: Address technical uncertainties before main development. - **Prevent Delays**: Avoid excessive brainstorming that can stall progress. +- **Reduce Unknowns**: Lower the project's uncertainties. +- **Efficient Estimates**: Provide well-constrained estimates on specific work items. +- **Maximize Value**: A one-day spike giving 80% confidence can be more valuable than a three-day spike giving 90%. - + --- @@ -139,6 +144,32 @@ Clear communication ensures your projects are comprehensible to all, enhancing y --- +# Example of Making Complex Concepts Accessible + +### Remember the "Healthcare Example" above? + +Before: +``` +"Improved patient outcomes by developing a predictive model using Python and scikit-learn. Data was preprocessed using pandas +to handle missing values and standardize features. Logistic regression was selected through cross-validation due to its +interpretability and performance." +``` +After: +``` +“Improved patient outcomes by developing a predictive model using Python and scikit-learn. Think of the predictive model as a way +to forecast patient health based on past data, much like predicting the weather. Data was preprocessed using pandas to handle +missing values and standardize features. This preprocessing is like organizing and cleaning up messy medical records to ensure all +information is complete and consistent. Logistic regression was selected through cross-validation due to its interpretability and +performance. Imagine trying different medical treatments and choosing the one that consistently gives the best results; that’s +what cross-validation does for model selection.” + +``` + +This approach effectively communicates complex concepts to both technical and non-technical audiences, ensuring clarity and comprehension across diverse stakeholders. + +--- + + # Next Steps Post-Certification As you transition from the certificate program, continue to build and showcase your skills.