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. R users who wish to access these tools and more would benefit from becoming comfortable using Python, which they can do from inside the familiar RStudio environment.

This course is not intended to “convert” R users to Python users. There are many areas and aspects of data analysis in which R excels relative to Python. Rather, this course is intended to give R users confidence about programming in Python so they may 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.

This course is more than just a how-to guide for translating R code to Python. While these kinds of translations can be helpful, 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.

This course is thoroughly hands-on. You are encouraged to write code with the instructor and participate in the carefully designed exercises interspersed throughout the seminar and 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.