The course continues building programming fundamentals in Python programming and data analysis. You will learn how to make use of complex data structures, iterate repeated tasks that scales naturally, and create your own functions. You will apply these skills to develop a custom data analysis.
Dates: Winter 2026: Jan 21, 28, Feb 4, 11, 25, March 4
Time: Wednesdays Noon - 1:30 pm PT
Time commitment: 6 weeks of 1.5 hour classes and 1-2 hours of practice outside class.
Audience: The course is intended for researchers who want to continue learning the fundamentals of Python programming and how to write custom data analysis functions when dealing with messy datasets. The audience should know how to work with Lists and Pandas Dataframes and conduct basic data analysis, and/or have taken our Intro to Python course.
Prerequisites: Intro to Python
Followed by: Machine Learning for Python
Understand and distinguish the use case of data structures to store different types of data.
Implement code to Iterate over a collection (such as files, elements of a column, or a list of objects) to batch process each item
Implement code that has a branching structure depending on input data’s condition.
Create simple, modular functions that can be reused.
Describe the difference between copying an object vs. referencing an object and how that could affect variables in a data analysis.
This class is offered once a year. Check the schedule to see when it will next be offered.
Yes, you can access the course materials on your own any time.
You need to complete the live class to get a badge.
The class is open to members of the Cancer Consortium including Fred Hutch, the University of Washington, and Seattle Children’s Hospital.
You can, but we recommend you don’t mix R and Python at the same time, because it can be confusing to switch between the two.
You can sign up more than once, but be aware that there is tyspically a waitlist. We will prioritize students who have not taken the class. If there is room you are welcome to take it again.