R is a free and open-source language for statistical analysis that is widely used across industries and disciplines. For many, R is the go-to source for data analysis and data visualization. There are several important areas, however, where Python (which is also free and open-source) may be preferred to R. For example, Python’s scikit-learn package for machine learning is far more widely adopted than R’s caret package. In addition, Python has become the primary language for neural networks and deep learning tasks, such as image classification and natural language processing. Researchers wishing to access these tools and more would benefit from becoming comfortable with Python.

This course is not intended to “convert” R users to Python users. There are many areas of data analysis where R excels relative to Python. Rather, this course is intended to give you confidence to program in Python so you can take advantage of Python’s inherent advantages in areas such as machine learning, deep learning, and big data. This course aims to add a new tool to your data toolbox — one increasingly built alongside AI coding assistants, which have made picking up a second language faster and less intimidating than ever. You’ll not only learn Python fundamentals but also how to use these AI tools effectively as part of a modern, efficient Python workflow. AI content comprises approximately 15-20% of the seminar.

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.