Course Detail (Course Description By Faculty)

TL;DR:

• This course requires/expects no background in coding. However, those with a coding background are welcome and can scale up the difficulty of all assignments

• You’ll learn to use ChatGPT and similar tools to write code

• You’ll be writing code yourself every week, in class and on homework 

• You can use ChatGPT on homework and on the exams

• Midterm exam will be take-home. Final exam will be a project with an in-class presentation in week 10 (finals week)

Let’s get this out of the way: A lot of code today is written by ChatGPT. This isn’t a bad thing per se, and it can be a useful adjunct. But ChatGPT is not error-free, so knowing something about coding is still important. In this course, you’ll learn enough computer science, statistics, data analysis, and, yes, generative AI to do data analysis at your job. 

We’ll use two of the most popular programming languages today, Python and SQL. No prior experience in coding or data analytics is required. If you have any Python background, let me know and we’ll work on finding more challenging practice problems. 

You will be writing code every single week. If that scares you a little, that’s ok, that’s what the instructor and TAs are here to help with! We will carry out analytics, including data cleaning, computing summary statistics, basic visualizations and basic linear regression. No math or statistics background is required, we’ll learn everything we need. 

This course could be taken as a preparation for Business Statistics (41000), Applied Regression (41100), and Advanced Decision Models with Python (36109). Understanding how to work with data will also be helpful in other coursework even if programming is not required, such as Healthcare Data Analytics (40205). 

Learning Outcomes: 

1. Write basic code in Python and SQL to carry out fundamental data analysis tasks like data import, data cleaning, and data visualization

2. Prompt ChatGPT and similar genAI tools to optimize your coding workflow 

3. Define data analytics fundamentals to enable you to talk intelligently with programmers and analysts on your team

Required textbook:

• The Data Science Design Manual by Steven Skiena. You can buy a hardcopy if you'd prefer, but you can get a FREE PDF through the UChicago library (link)

Other non-required resources (no guarantee that these will line up in any way with the material we cover in class, but can be useful for those interested in growing in their Python and SQL):

• Python for Data Analysis by William McKinney
• Automate the Boring Stuff with Python (2nd edition) by Al Sweigart
• Learn Python the Hard Way by Zed Shaw
• W3 Schools SQL tutorials
•  Four-hour video introduction to SQL from freeCodeCamp

• Homework - 10%

  • Homework will be assigned every week for total of 7 assignments

  • I’ll automatically drop your lowest HW grade at the end of the quarter. 

• Midterm (take-home, group work) - 20%

• Final (project with in-class presentation in week 10) - 50% 

• Participation/attendance - 20%

  • Attendance will be self-reported through Poll Everywhere