Monday, April 6, 2026, noon-1:30 p.m., Online
Taught by Alexis Cenname
Register for Python Bootcamp Week 1

Join us for our Python Bootcamp Week, offering a comprehensive dive into Python programming. Mirroring the approach of its R counterpart, the bootcamp unfolds through a sequence of dedicated sessions that encompass the data analysis workflow. This includes importing and cleaning data, moving on to data manipulation, and employing advanced visualization techniques with the pandas and seaborn libraries. Each day’s workshop builds on the last, ensuring a comprehensive understanding and practical application of Python for data analysis.

This is part one of a three-part Python Bootcamp. We look forward to seeing you for all three sessions, as they build on each other; however, we understand if you are not able to make it to all of them – please still register and attend what you can!

In Week 1, “Introduction to Python, JupyterLab, and Jupyter Notebook,” you’ll familiarize yourself with the JupyterLab interface and learn to create integrated Jupyter notebooks.

Target Audience: The target audience for this workshop series is beginners and intermediates in data science, particularly those interested in learning Python programming for data importation, exploration, cleaning, and visualization.
Upon completing this class, you should be able to:
  • Utilize the Anaconda Navigater and JupyterLab interface
  • Describe basic components of Python code
  • Describe the basic functionality of Jupyter notebook
  • Use JupyterLab to create a Jupyter notebook
Level: Novice
Prerequisites: Installation of Anaconda Navigator/JupyterLab
What should you do before attending this class: If you do not have any previous knowledge of Anaconda/JupyterLab, or do not have it downloaded on your computer, please follow the instructions given in the Overview file. It is important you complete this before the first day. The document will also provide links to additional help and resources for Python programming.
Recording status: This class will be recorded and shared with attendees.
Class Materials: Class materials will be shared with attendees.