This is a collection of links to freely available resources related to computational biology.
- Bioinformatics
- Computational Neuroscience
- Computer Science
- Machine Learning
- Mathematics
- Theoretical Physics
- Statistics
- Northwestern University Resources
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.
| Title | Authors | Resource type | Description |
|---|---|---|---|
| Bioinformatics curriculum | Open Source Society University | Online curriculum |
| 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. |
| Title | Authors | Resource type | Description |
|---|---|---|---|
| Computer science curriculum | Open Source Society University | Online curriculum | |
| Computer science curriculum - chinese | Open Source Society University | Online curriculum |
| 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 |
| 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 |
| 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 | ||
| The Good Research Code Handbook | Patrick Mineault | Online Handbook |
| Title | Authors | Resource type | Description |
|---|---|---|---|
| The Unix Shell | Software Carpentry | Online Course |
| Title | Authors | Resource type | Description |
|---|---|---|---|
| Version Control with Git | Software Carpentry | Online Course |
| 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 |
| Title | Authors | Resource type | Description |
|---|---|---|---|
| Differential equations | 3Blue1Brown - Grant Sanderson | YouTube series |
| Title | Authors | Resource type | Description |
|---|---|---|---|
| Nathan Kutz's YouTube Channel | Nathan Kutz | Recorded lectures | |
| Data Driven Science and Engineering | Steven Brunton and Nathan Kutz | Online Videos | This website has a bunch of great videos to explain topics from the book |
| Nonlinear Dynamics and Chaos | Steve Strogatz | Recorded lectures | |
| Nonlinear Dynamics and Chaos | Steve Strogatz | Textbook |
| Title | Authors | Resource type | Description |
|---|---|---|---|
| Essence of linear algebra | 3Blue1Brown - Grant Sanderson | YouTube series |
| Title | Authors | Resource type | Description |
|---|---|---|---|
| Classical Electrodynamics | MIT OCW | Lecture notes | Great for understanding diff eq |
| 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 |
| Title | Authors | Description |
|---|---|---|
| Information Technology Workshops | NUIT |
| 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 |