| Eren Bilen | |
|---|---|
| [email protected] | |
| Office | Rector North 1309 |
| Office Hours | calendly M 9:00-10:30am, Th 10:20-11:50am |
| GitHub | ernbilen |
- Meeting day/time: T-F 3:00-4:15pm, Tome 121
- Office hours also available by appointment.
- QRA: Elsa Hritz [email protected]
- QRA Office Hours: TBA
Welcome to Data 200! This course provides an introduction to the management and manipulation of database systems as it applies to data analytics. Topics include data query languages, relational database, APIs/webscraping, transforming and restructuring data representations. Upon successful completion of the course a student will be able to
- understand the tabular data model, the relational data model, and hierarchical data model
- retrieve data using Structured Query Language (SQL)
- understand the client-server model for communication
- acquire data from a spectrum of external systems, ranging from structured to unstructured systems using APIs and web-scraping
- manipulate unstructured data into meaningful representations
- utilize the Python language for data analysis
We will make extensive use of Python and Anaconda distribution to generate graphical and numerical representations of data and complete data processing techniques using SQL.
Grades will be based on the categories listed below with the corresponding weights.
| Assignment | Points | Percent |
|---|---|---|
| Exam #1 | 25 | 25.0% |
| Exam #2 | 25 | 25.0% |
| Take-home Final | 25 | 25.0% |
| Homework | 25 | 25.0% |
| Total points | 100 | 100.0% |
- Accept my hw invitation link (this automatically creates a clone repo just for you)
- On this repo, hit Code -> Open with Github Desktop
- In Github Desktop -> hit Show in Finder (or explorer if you are on Windows)
- In your local Finder window, you will see the hw00 folder, go inside, work on the assignment. (Note: Remember you can't double click on .ipynb files, so navigate to where this local folder is from within Jupyter Notebook)
- Once you are finished, save your changes in Jupyter Notebook + export to Markdown. Drag and drop the Markdown file you just created into your local Github folder, where you have your .ipynb file.
- Go back to Github Desktop, you will see that it recognized your changes in your local file, and it’s waiting for you to commit. Go ahead and commit (you must add a short comment at this stage about what changes you have made.)
- Push your changes by clicking on “Push origin” (blue button in the middle of Github Desktop window). You are done!
- Visualizing spatial data with geopandas
- Cluster analysis with python
- Regular expressions in python
- Software engineering and data science
- Python for Data Analysis by Wes McKinney
- SQL Basics
- LaTeX Cheat Sheet and an excellent tutorial by Dave Richeson
- QuantEcon
- Live question link
- Eval link