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Introduction to Python's four-class intro to Python for biomedical researchers

Introduction to Python


This four class course from is designed to introduce Python programming and its broad applications, specifically for data analysis relevant biomedical researchers. At the end of this course, you will be able to use Python to import, manipulate, and visualize data. Please see each class for specific learning objectives. These materials are developed by, the data and computational analysis training program at Fred Hutch. Each class in this course includes brief tutorials interspersed with challenge exercises, and assumes attendees have no prior computer coding experience.

Sessions of the course are periodically taught by instructors at Fred Hutch; each of the four classes is scheduled for two hours. The HackMD (interactive page used for sharing links and information) for instructor-led courses is here. The materials are also freely available for self-guided, work-at-your-own-pace study.

Required software: Software requirements for this course can be found on’s Software page.

Class material

Materials for all lessons in this course include:

Solutions for exercises can be found in solutions.

Information about use of Python and Jupyter notebooks at Fred Hutch is available on the Fred Hutch Biomedical Data Science Wiki

For curriculum contributors and instructors

Please see the following resources for more information on:

Data used for this lesson are identical to that used in Introduction to R; details on obtaining these data from the National Cancer Institute’s Genomic Data Commons can be found in that lesson repository.


This course is adapted from content originally appearing in Python for Ecologists, Copyright (c) Data Carpentry.

Thank you to Eric Bae, Brianna Odle, and Geet Jodhka, high school students from Fred Hutch’s SEP program, for providing additional challenge exercises for these lessons.