Resources

Coursera: Principles of fMRI 1

by Martin Lindquist & Tor Wager

This course covers the design, acquisition, and analysis of Functional Magnetic Resonance Imaging (fMRI) data.

Materials

Coursera: Principles of fMRI 2

by Martin Lindquist & Tor Wager

This course covers the analysis of Functional Magnetic Resonance Imaging (fMRI) data. It is a continuation of part 1.

Materials

Neuromatch Academy

An online course designed to introduce traditional and emerging computational neuroscience tools to trainees with hands-on tutorials. See syllabus here.

Tutorials

MIND Computational Summer School

A YouTube video playlist of previous lectures and tutorials held during the Methods in Neuroscience at Dartmouth (MIND) Computational Summer School

Materials

Neurohackademy

An online repository of lectures and tutorials on neuroimaging and electrophysiology from previous years of Neurohackademy summer school.

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OHBM OnDemand

Online repository of lectures and workshops from previous years of the Organization for Human Brain Mapping conference.

Materials

Intro to Nilearn

by Lukas Snoek

This Jupyter Notebook introduces the nilearn Python package for applying statistical techniques (from GLMs to multivariate "decoding") to neuroimaging data and more.

Materials

BrainIAK Tutorials

by Manoj Kumar et al.

These tutorials provide an overview of basic and advanced techniques for analyzing neuroimaging data using the Brain Imaging Analysis Kit (BrainIAK) toolbox in Python

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Andy's Brain Blog

by Andrew Jahn

A series of YouTube video tutorials on preprocessing and statistical analyses in major neuroimaging software packages.

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Mumford Brain Stats

by Jeanette Mumford

A series of YouTube video tutorials and lessons about different neuroimaging data analysis strategies.

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DartBrains

by Luke J. Chang

An interactive Jupyter Book providing an introduction to human functional magnetic resonance imaging (fMRI) using Python.

Materials

Hitchhacker's guide to the brain!

by Remi Gau et al.

This website is full of links and resources for all your neuro-needs!

Materials

Statistical Thinking for the 21st Century

by Russell A. Poldrack

An open-source online book complete with R and Python code focused on approaches that take advantage of today’s increased computing power to solve statistical problems in ways that go far beyond the more standard methods usually taught in undergraduate statistics.

Materials

Whirlwind Tour of Python

by Jake Vanderplas

A fast-paced introduction to essential components of the Python language for researchers and developers who are already familiar with programming in another language.

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What is a hackathon?

by Shawn Rhoads

A brief primer on hackathons and what to expect at your first hackathon.

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Learning R Resources

by Joscelin Rocha Hidalgo

This GitHub Wiki page containing resources to learn/teach R, statistics, and more!

Materials

From Data to Viz

by Yan Holtz & Conor Healy

A classification of chart types based on input data format. It comes in the form of a decision tree leading to a set of potentially appropriate visualizations to represent your dataset.

Materials

Essence of Linear Algebra

by Grant Sanderson

A crash-course series of 15 short YouTube videos on the basics of linear algebra.

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Git (and GitHub) Cheat Sheet

by Olivia Guest

Quick reference, basic introduction to some of the basics of version control (git: a system to track your code) and a website providing such services (GitHub).

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Version Control with Git

by Richard Kalehoff

Free online course covering the essentials of using the version control system Git. You'll be able to create a new Git repo, commit changes, and review the commit history of an existing repo.

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Resources to learn Git

try.github.io

Compiled resources (e.g., cheat sheets, tutorials, reading materials) to learn Git and GitHub.

Materials

Pro Git 2nd Edition

by Scott Chacon & Ben Straub

This online free book covers Version Control Systems (VCSs) and Git basics.

Materials

Apprenticeship in Teaching Program

Georgetown Center for New Designs in Learning & Scholarship

The Apprenticeship in Teaching (AT) Program is designed to help graduate students and other developing teachers become more reflective and skilled instructors, informed by the research on teaching and learning in higher education, and familiar with pedagogical best practices.

Materials