Python for R Users with AI Assistance - A Short Course

This course is more than just a how-to guide for translating R code to Python. While these kinds of translations can be helpful — and are now easier than ever thanks to AI-assisted tools — the end goal is to help you become fluent in Python so that you will be able to incorporate popular Python libraries into your personal toolbox, and to do so with the confidence to work productively alongside AI coding assistants rather than depend on them blindly.

Hands-on practice is central to the seminar. You are encouraged to write code with the instructor and to participate in the carefully designed exercises interspersed throughout the seminar, which are assigned as “take-home” practice after the first session. By the end of the course, you can expect to log more than six hours of guided practice coding in Python, including practice using AI tools thoughtfully as part of that process.

To participate in the hands-on exercises, you are strongly encouraged to use a computer with the most recent version of the Anaconda distribution for Python installed. If you’re unable to install Python locally, we’ll introduce Google Colab as a free cloud-based option for writing and executing Python code using a web browser.

AI-assisted coding techniques will be demonstrated using tools such as ChatGPT and Claude, though any widely used AI assistant will work just as well. You’re not required to use a specific tool.

This course is for R users who also want to work in Python. It’s particularly useful for data analysts and data scientists, who may typically work in R but want the ability to work with Python’s more advanced libraries in machine learning and deep learning.

Getting started with Python

• • Installation and setup
  • Working with Python within the RStudio environment
  • Installing and loading packages

Essential Python skills

• • Basic Python syntax and functionality
  • Loops and logical operators
  • Functions
  • Basics of object-oriented programming (OOP): how to understand and access Python’s attributes and methods
  • Help and accessing package documentation

Working with data

• • Loading data from various sources (CSV, XLSX, etc.)
  • Viewing and modifying data in R and Python
  • Slicing data
  • Manipulating and performing dplyr-like operations on data
  • Descriptive statistics

Data visualization

• • Brief introduction to plotting using Python’s Matplotlib
  • Exploratory data plots

Basic model fitting in Python

• • Fitting several simple models to understand model syntax and access model outputs
  • Simple linear regression using the statsmodels package
  • Basic classification using the scikit-learn package for machine learning
  • Post-estimation prediction (time permitting)

“Time was taken to be very clear about how to get started with Python coding. There is clear dedication from the instructor to make sure everyone is able to take away as much as they can during the short time of the course.”
  Scott Boyce, Indiana University

“The instructor was excellent. He was clearly experienced, explained everything exceptionally, and handled class questions very well. All of the resources were great, and Dr. Rennhoff was very accessible and helpful. I will be looking for other classes to take from him.”
  Edie Sperling, Western University of Health Sciences

“The pace of the course made it very engaging. I felt like it was appropriate for people who had a decent amount of R experience.”
  Maggie Kuzemchak, UPMC Health Systems