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comp-bio-resources's Introduction

Computational Biology Resources

This is a collection of links to freely available resources related to computational biology.

Contents

Contributing

Resources can be contributed by either editing this page directly on GitHub or by forking this repo and submitting a pull request. To edit directly on GitHub, click on the filename, then click on the pencil icon in the upper right corner of the file, make your changes, and then click on the green "Commit changes" button on the bottom of the page.

Bioinformatics

Title Authors Resource type Description
Bioinformatics curriculum Open Source Society University Online curriculum

Computational Neuroscience

Title Authors Resource type Description
Online Resources for Systems and Computational Neuroscience Simons Foundation Online collection of resources
Machine Learning Methods for Neural Data Analysis (STAT 320, Stanford) Scott Linderman Materials from online course
Case Studies in Neural Data Analysis Mark Kramer and Uri Eden Online jupyter notebooks A collection of notebooks with guided analysis of neural data in Python.
Statistical Modeling and Analysis of Neural Data (NEU 560, Princeton) Jonathan Pillow Course materials Spring 2018 course
Statistical analysis of neural data (Columbia) Liam Paninski Course materials Fall 2015 course
Mathematical Tools for Neuroscience (Neurobio 212, Harvard) Ella Batty, Lucy Lai, Alex Chen, and John Assad Course materials
Mathematical Tools for Neural and Cognitive Science (PSYCH-GA.2211 / NEURL-GA.2201) Mike Landy and Eero Simoncelli Course materials Fall 2020
Systems and Theoretical Neuroscience (Gatsby Computational Neuroscience Unit / Sainsbury Wellcome Centre) See course for lecturers Course Materials 2018
Course notes for Gatsby’s Theoretical Neuroscience course Ted Moskovitz Course Notes
Neuronal Dynamics: From single neurons to networks and models of cognition Wulfram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski Online book
RNNs in Neuroscience at CoSyNe 2021 Kanaka Rajan Workshop materials
Computational neuroscience resources Neural Reckoning Online collection of resources
Computational neuroscience resources Austin Soplata Online collection of resources
Neural Data Science Philipp Berens Course materials and recorded lectures Summer 2021
PNI Bootcamp Materials Course Materials Lecture materials and exercises for the Princeton Neuroscience Institute's 2020 Graduate Student Bootcamp.

Computer Science

Title Authors Resource type Description
Computer science curriculum Open Source Society University Online curriculum
Computer science curriculum - chinese Open Source Society University Online curriculum

Programming languages

Python

Title Authors Resource type Description
Programming with Python Software Carpentry Online Course
Plotting and Programming with Python Software Carpentry Online Course
Dataquest Python Courses Dataquest Online Course Free quarterly access through NUIT

R

Title Authors Resource type Description
Programming with R Software Carpentry Online Course
R for Reproducible Scientific Analysis Software Carpentry Online Course
Dataquest R Courses Dataquest Online Course Free quarterly access through NUIT
The R Book, 2nd edition Michael J. Crawley Textbook PDF is available via NU library

CS Misc.

Title Authors Resource type Description
The Missing Semester of Your CS Education (MIT) Anish Athalye, Jon Gjengset, and Jose Javier Gonzalez Ortiz Course Materials
Great Practical Ideas in CS (CMU 07-131) Adam Blank and Josh Zimmerman Course Materials
Project Lovelace Collection of scientific programming problems

Shell scripting

Title Authors Resource type Description
The Unix Shell Software Carpentry Online Course

Version Control

Title Authors Resource type Description
Version Control with Git Software Carpentry Online Course

Machine Learning

Title Authors Resource type Description
Deep Learning Ian Goodfellow, Yoshua Bengio, and Aaron Courville Textbook Online free version and print version is available for purchase.
Max Welling's Classnotes in Machine Learning Max Welling Class Notes
Max Welling's Classnotes in Machine Learning Max Welling Class Notes
Neural Networks 3Blue1Brown - Grant Sanderson YouTube series
Understanding UMAP Andy Coenen and Adam Pearce Tutorial

Mathematics

Differential Equations

Title Authors Resource type Description
Differential equations 3Blue1Brown - Grant Sanderson YouTube series

Dynamical Systems

Title Authors Resource type Description
Nathan Kutz's YouTube Channel Nathan Kutz Recorded lectures
Nonlinear Dynamics and Chaos Steve Strogatz Recorded lectures
Nonlinear Dynamics and Chaos Steve Strogatz Textbook

Linear Algebra

Title Authors Resource type Description
Essence of linear algebra 3Blue1Brown - Grant Sanderson YouTube series

Theoretical Physics

Classical Electrodynamics

Title Authors Resource type Description
Classical Electrodynamics MIT OCW Lecture notes Great for understanding diff eq

Statistics

Title Authors Resource type Description
Statistical Rethinking: A Bayesian Course with Examples in R and STAN Richard McElreath Textbook Highly reccomended book if you are new to Bayesian statistics or have struggled with gaining intuition
Statistical Rethinking: Winter 2019 lectures Richard McElreath Recorded Lectures Highly reccomended lectures if you are new to Bayesian statistics or have struggled with gaining intuition
Bayesian Data Analysis Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin Textbook Website contains links to free pdf, lectures, notes, and more.
Data Analysis Using Regression and Multilevel/Hierarchical Models Andrew Gelman and Jennifer Hill Textbook
Applied Regression and Multilevel Models Andrew Gelman, Jennifer Hill, Ben Goodrich, Jonah Gabry, Daniel Simpson, and Aki Vehtari Should be out in 2021

Northwestern University Resources

Workshops

Title Authors Description
Information Technology Workshops NUIT

Recommended Courses

Title Professor Description
Data Driven Methods for Dynamical Systems (ES_APPM 479) Niall Mangan
Introduction to the Analysis of RNA Sequencing Data (ES_APPM 472) Bill Kath The course is an introduction to the theory and practice of analyzing high-throughput RNA sequencing data

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