This repository contains links to the materials used in our most current Python workshops. Some of the materials are self-guided -- you can work through the notebooks at your own pace. Some of the workshops utilize Jupyter Notebooks.
There are two options for opening and running Jupyter Notebooks:
- On your own computer. If you have the Anaconda distribution of Python and Jupyter and/or you are comfortable installing Python packages, you can run the notebooks on your own computer. Choose a workshop from the list below and click on the link. From the repo, click on the green Code button and choose Download ZIP. Unzip the folder. Open Anaconda Navigator and choose Jupyter Lab. In Jupyter, navigate to the folder you just unzipped and select the desired notebook (Jupyter notebooks end in .ipynb).
- On the cloud. You can also run the notebooks online through Google Colab. Choose a workshop from the list below and click on the Colab link.
Materials for working on your own computer
Material links for working on the cloud through Google Colab:
Monday Lecture
Monday Lecture Answers
Monday Quiz
Monday Quiz Answers
Tuesday Lecture
Tuesday Lecture Answers
Tuesday Quiz
Tuesday Quiz Answers
Wednesday Lecture
Wednesday Lecture Answers
Wednesday Quiz
Wednesday Quiz Answers
Learn how to work with data shaped in rows and columns
Materials for working on your own computer
Material links for working on the cloud through Google Colab:
Day 1 link
Day 1 answer key
Day 2 link
Day 2 answer key
Day 3 link
Day 3 answer key
Day 4 link
Day 4 answer key
Day 5 link
Intermediate Skills that will make your life easier
Materials for working on your own computer
Material links for working on the cloud through Google Colab:
Colab link
Colab answer key
Materials for working on your own computer
Material links for working on the cloud through Google Colab:
Colab link
Colab answer key
Materials for working on your own computer
Material links for working on the cloud through Google Colab:
Colab link
Colab answer key
Materials for working on your own computer
Material links for working on the cloud through Google Colab:
Colab link
Colab answer key
Materials for working on your own computer - no Colab option
Materials for working on your own computer
Material links for working on the cloud through Google Colab:
Colab link
Materials for working on your own computer
Material links for working on the cloud through Google Colab:
Colab link
Colab answer key
Materials for working on your own computer
Material links for working on the cloud through Google Colab:
Colab link
Colab answer key
Materials for working on your own computer
Material links for working on the cloud through Google Colab:
Colab link
Colab answer key
Materials for working on your own computer
Material links for working on the cloud through Google Colab:
Colab link
Colab answer key
Work with biological sequence data in Python
Materials for working on your own computer
Material links for working on the cloud through Google Colab:
Colab Day One
Colab Day Two
Colab Day Three
Colab Day Four
Colab Day Five
Make your code reuseable and efficient - scripts, simple parallelization, and the basics of pipelines
Materials for working on your own computer - no Colab option
Learn how to make custom plots in Python
Materials for working on your own computer
Material links for working on the cloud through Google Colab:
Part 1 Colab
Part 2 Colab
Part 3 Colab
Part 4 Colab
Learn the basics of running predictive models and classifiers
Materials for working on your own computer
Material links for working on the cloud through Google Colab:
Day 1 Colab
Day_1 Answer key Colab
Day 2 Colab
Day_2 Answer key Colab
Day 3 Colab
Day_3 Answer key Colab
Day 4 Colab
Day_4 Answer key Colab
Day 5 Colab
Day_5 Answer key Colab
Previous versions of Python workshops.
Statistical and Machine Learning Models
Intro to Programming with Python
See Resources for a listing of general Python resources, tutorials, and reference materials. Additional resources are linked in the individual workshop repositories.
A few datasets for use across workshops are stored in this repository in the datasets directory.