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 fredhutch.io, 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 fredhutch.io's Software page.
Materials for all lessons in this course include:
- Class 1: Intro to python, jupyter notebooks, and data types
- Class 2: Using pandas to explore data frames
- Class 3: Extracting data from data frames
- Class 4: Data visualization with ggplot
Solutions for exercises can be found in
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:
- Teaching these materials.
instructors.mdincludes information for instructors to facilitate teaching each lesson.
hackmdio.mdis an archive of the interactive webpage used during lessons.
- Contributing to lessons.
Each lesson's materials are described in a Jupyter notebook.
The links to lessons above are for the notebooks rendered through nbviewer,
since GitHub rendering frequently fails.
To make changes to the material,
modify the relevant
ipynbfile in the GitHub repository.
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.