Resources

This is a collection of helpful resources for learning GitHub, Python, Machine Learning, and fMRI.

Git

• cheat sheet
• interative tutorial

Python

• general tutorials
  • learnpython: a list of short interactive tutorials supported by datacamp
  • Lectures on Scientific Computing with Python by J.R. Johansson. We recommend the 1st 5 notebooks.
  • Introduction to Python for Data Science: This is a short online open course by Microsoft.
  • numpy for Matlab users
• jupyter notebook
  • installation
  • online interactive tutorial about python notebook
  • a bunch of notebook tutorials about general python stuff, plotting, machine learning, etc.

* If you want to run notebooks/programs locally, we strongly recommend setting up a virtual environment with anaconda.

Data visualization

• Fundamentals of Data Visualization: a nice reference book
• Recommended plotting packages
  • matplotlib: examples, cheat sheet
  • seaborn: examples, cheat sheet
  • bokeh: some tutorial notebooks

Cluster related information.

We have collected a few links to help with running on clusters. We also list university specific sites to help with cluster information on your campus.

• job scheduling

  • slurm (used @ princeton) quick reference

• cluster access: Princeton

• shell scripting

  • learnshell: interactive tutorial for unix shell

• text editor

  • vim: interactive tutorial for vim

fMRI

• Python tutorials on fMRI preprocessing, General Linear Model (highly recommended)
  • DartBrains by Luke Chang (2019)
• general intro
  • fMRI bootcamp, by Rebecca Saxe (2017)
  • The human brain, by Nancy Kanwisher (2018)
• preprocessing
  • FSL
• brain viewer, from gallantlab
• python packages
  • nilearn: examples
  • pysurfer: examples

Machine learning

• general intro
  • Machine Learning by Tom Mitchell and Maria-Florina Balcan (2015)
• python packages
  • sklearn: examples, notebooks
• other stuff
  • cheatsheet

Basics

• linear algebra intuition, by 3Blue1Brown

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