I set up this page in an attempt to better organize my bookmarks. It lists resources related to data science, data visualization, or programming. It is by no means complete or authoritative. These are just resources that I've gleaned here and there. I've read or studied some of them, but a lot are still to be explored. However, it gives a good overview of the topics I've been interested in recently or would like to explore in the future.
- Introduction to statistical learning with R - Gareth James et al. - 2013
- An open machine learning course - Joaquin Vanschoren - 2019
- MIT OpenCourseWare - MIT
- Advanced data science - Jeff Leek et al. - 2018
- Harvard Introduction to data science - Harvard T.H. Chan School of Public Health - 2018
- Google machine learning crash course - Google
- Introduction to data science - University of Nebraska - 2014
- Data science theories, models, algorithms, and analytics - Sanjiv Ranjan Das - 2017
- Statistical Inference and Machine Learning - Future Learn
- A Course in machine learning - Hal Daumé III - 2017
- Gaston Sanchez's website
- A machine learning course with Jupyter/IPython - Luis Marti - 2017
- STAT 479: Machine learning - Sebastian Raschka - 2018
- Machine learning for artists - Gene Kogan et al. - 2019
- Model-based machine learning - John Winn et al.
- Open machine learning course - mlcourse.ai - 2019
- Intel AI academy - Intel - 2018
- Navigating ML - Micheleen Harris - 2018
- PH525x series - Biomedical data science - Rafael Irizarry and Michael Love
- HarvardX Biomedical data science - Rafael Irizarry et al.
- Open Classrooms - Data Scientist
- Readings in applied data science - Hadley Wickham - 2018
- The open source data science masters - Clare Corthell
- The best data science courses on the internet - David Venturi - 2017
- Data science courses ranking - David Venturi - 2017
- Class Central
- How to learn machine learning - Deborah Hanus - 2018
- Practical data science for stats - PeerJ collection - 2018
- Most active data scientists, free books, notebooks & tutorials - Analytics Vidhya - 2016
- 7 steps to mastering machine learning with Python - Matthew Mayo - 2015
- How to become a data scientist for free - Nir Goldstein - 2015
- The 10 best AI, data science and machine learning podcasts - Matt Fogel - 2016
- Toulouse data science
- I dropped out of school to create my own data science master's - David Venturi - 2016
- Machine learning in a year - Per Harald Borgen - 2016
- ML from scratch - Erik Linder-Noren
- Materials for the introduction to machine learning class - Andreas Mueller - 2018
- Alexandre Gramfort's github
- Scipy lectures notes - Gaël Varoquaux et al. - 2019
- SciPy 2018
- PyParis tutorial on machine learning using scikit-learn - Guillaume Lemaitre - 2018
- Hands-on machine learning with R - Bradley Boehmke - 2019
- Applied machine learning - Max Kuhn - 2019
- H2O AutoML tutorial - Erin Ledell
- Data science and R: how do I start? - Jesse Maegan - 2017
- A visual introduction to machine learning - Stephanie Yee and Tony Chu
- Top 10 data mining algorithms in plain English - Ray Li - 2015
- DataFramed - Datacamp
- Machine learning guide - Tyler Renelle - 2018
- Hugo Larochelle
- 3Blue1Brown
- Andreas Mueller
- fast.ai - Jeremy Howard et al.
- deeplearning.ai - Andrew Ng et al.
- Notes from Coursera deep learning courses by Andrew Ng - Tess Ferrandez - 2018
- MIT introduction to deep learning - Alexander Amini and Ava Soleimany - 2019
- Stanford CNN for visual recognition - Fei-Fei Li et al. - 2018
- Learn with Google AI
- Deep learning book - Ian Goodfellow et al. - 2016
- Bloomberg foundations of machine learning - David S. Rosenberg - 2018
- DeepMind course
- UCL course on reinforcement learning - David Silver - 2015
- Deep reinforcement learning course - Thomas Simonini - 2019
- Minicourse in deep learning with PyTorch - Alfredo Canziani - 2018
- Cheat sheets - Stefan Kojouharov - 2017
- How do neural nets learn? - Shirin Glander - 2018
- How to learn deep learning - Emil Wallner - 2018
- http://intelligence-artificielle.agency/ - IA - 2018
- ActuIA
- The most cited deep learning papers - Terry Taewoong Um - 2018
- Fake Chinese characters with TensorFlow - Otoro - 2015
- A book from the sky - Gene Kogan - 2015
- Jay Alammar's blog
- Chris Olah's blog
- Practical text classification with Python and Keras - Nikolai Janakiev - 2018
- How to build your own AlphaZero AI using Python and Keras - David Foster - 2018
- Deep painterly harmonization - Fujun Luan - 2018
- Neural networks and deep learning - Michael Nielsen - 2018
- Grokking deep learning - Andrew Trask - 2019
- Reconnaissance des formes et méthodes neuronales - CNAM
- Apprentissage, réseaux de neurones et modèles graphiques - CNAM
- Wikistat
- Xavier Dupré's website
- Joseph Salmon's website
- Comment les réseaux de neurones à convolution fonctionnent - Brandon Rohrer and Charles Crouspeyre - 2017
- Doug Bates's lectures
- Five minutes stats - Matthew Stephens
- Xavier Gendre's lectures
- Seeing theory - Daniel Kunin et al.
- General linear models: the basics - Chris Brown - 2018
- A gentle INLA tutorial - Kathryn Morrison - 2017
- math-as-code - Matt DesLauriers - 2015
- Computational linear algebra course - fast.ai - Rachel Thomas et al. - 2017
- Crash course on linear algebra programming - Craig Johnston - 2018
- Introduction to applied linear algebra - Stephen Boyd and Lieven Vandenberghe - 2018
- Introduction to statistics and basics of mathematics for data science - Amit Kapoor - 2017
- Machine learning explained: dimensionality reduction - Victor Perrier - 2017
- How to use t-SNE effectively - Martin Wattenberg et al. - 2016
- Comprehensive guide on t-SNE algorithm - Saurabh Jaju - 2017
- A one-stop shop for principal component analysis - Matt Brems - 2017
- t-sne intermediate states - Fred Chasen - 2015
- Jeffrey Heer
- Tamara Munzner
- Lynn Cherny
- LyonDataViz
- CS294-10 - 2014
- The work of Edward Tufte
- Data visualization for storytelling and discovery - Alberto Cairo - 2018
- Visualization for data science - Alexander Lex et al. - 2018
- Psychology of data visualization - Michael Friendly - 2019
- Collection of data visualizations
- List of tools for data visualization
- Data visualization tools and books (keshif)
- Visualnews best infographics
- datavisualization.ch
- Visualizing biological data
- Visual Vocabulary - Vega Edition - Pratap Vardhan - 2019
- Fundamentals of data visualization - Claus Wilke - 2019
- R graph gallery
- Elastic man
- Drawing portraits with text - Giora Simchoni _ 2017
- List of physical visualizatons
- Parametric equations
- Cartography guide - Axis Maps - 2017
- kepler.gl
- deckard
- mapdeck
- rgeomatic
- Manuel d'analyse spatiale - INSEE - 2018
- Données géo et R - Nicolas Roelandt - 2019
- The death of interactive infographics? - Dominikus Baur - 2017
- 39 studies about human perception in 30 minutes - Kennedy Elliott - 2016
- Design and redesign - Fernanda Viegas and Martin Wattenberg - 2015
- The architecture of a data visualization - Giorgia Lupi - 2015
- Les courbes de Pierre Bézier ont redessiné le monde - Peter Gabor - 2007
- I want hue - Mathieu Jacomy
- Your friendly guide to colors in data visualisation - Lisa Charlotte Rost - 2016
- D3.js wiki - Mike Bostock
- D3 in depth - Peter Cook - 2018
- D3 tips and tricks v4.x - Malcolm Maclean
- Data visualization course - Curran Kelleher - 2018
- Dashing D3.js
- Interactive information visualization - Mike Freeman
- React + D3 - Mike Freeman - 2018
- Intro to D3.js - Square
- The hitchhiker's guide to D3.js - Ian Johnson - 2017
- 25+ resources to Learn D3.js from scratch - Melissa Bierly - 2017
- D3 hierarchy -Gerardnico
- Learn JS data - Bocoup
- Jason Davies
- Color mesh II
- D3noob's blocks
- Bl.ock builder - Ian Johnson
- Brushable horizontal bar chart - Nadieh Bremer -2018
- All the blocks - Ian Johnson- 2015
- Nutrient database explorer - Kai Chang
- D3.js gallery - Christophe Viau
- Let's make a grid - Chuck Grimmett - 2016
- ease()-y as Math.PI - Scott Murray - 2014
- Bubble chart - Jim Vallandingham -
- d3-composite-projections - Roger Veciana i Rovira
- TnT tree - Miguel Pignatelli - 2017
- D3.js drag and drop zoomable tree - Rob Schmuecker 2013
- phylogram_d3 - Constantino Schillebeeckx - 2018
- PhyD3 - Łukasz Kreft et al. - 2017
- Archaeopteryx-js - Christian Zmasek and Yun Zhang
- iTOL - Ivica Letunic and Peer Bork
- phylotree.js - Stephen Shank - 2018
- IcyTree - Timothy Vaughan - 2017
- dc.js - Gordon Woodhull et al.
- Professional programming - Charles-Axel Dein - 2019
- Khan Academy computer programming
- 450 free online programming & computer science courses - Dhawal Shah - 2017
- Learn X in Y minutes
- Learn enough to be dangerous
- Floating point math
- What is code? - Paul Ford - 2015
- Free programming books - EbookFoundation
- Pascal Viot's courses
- Open Classrooms old pdf
- Stat 430: Topics in applied statistics - Dirk Eddelbuettel - 2019
- The Unix Workbench - Sean Kross - 2019
- Python data science handbook - Jake VanderPlas - 2018
- Apprendre Python
- Jupyter Notebook tutorial: the definitive guide - Karlijn Willems - 2019
- Python application layouts: A reference - Kyle Stratis - 2018
- Python style guide
- Pandas Q&A - Data School - 2018
- Python at Microsoft
- Python development in Visual Studio Code - Jon Fincher - 2019
- MDN Javascript
- FreeCodeCamp
- JavaScript versus Research Computing - Greg Wilson - 2018
- Javascript course - Beau Carnes - 2017
- Mike Freeman's website
- 33 concepts every JavaScript developer should know - Leonardo Maldonado - 2019
- The front-end checklist
- Vue.js tutos
- What every programmer absolutely, positively needs to know about encodings and character sets to work with text - David Zentgraf - 2015
- The absolute minimum every software developer absolutely, positively must know about unicode and character sets - Joel Spolsky - 2003
- String encoding in R - Kevin Ushey - 2018
- UTF-8 file output in R
- Using system fonts in R graphs - Yixuan Qiu - 2014
- Encoding in R - Irene Steves - 2019
- to-do
- Enough Docker to be dangerous - Sean Kross - 2018
- Dockerizing your work in R - Mara Averick - 2018
- How to use Shiny containers with Shinyproxy - Luke Singham - 2017
- Docker for the User - Noam Ross - 2018
- Shiny server in a Singularity container - Vanessa Sochat - 2018
- Build a REST API with R - Chris Walker - 2017
- Shiny application in production with ShinyProxy, Docker, and Debian - Cervan Girard - 2018
- Everything an open source maintainer might need to know about open source licensing - Ben Balter - 2017
- Software 2.0 - Andrej Karpathy - 2017
- Three computer games that make assembly language fun - Stephen Cass - 2017
- An introduction to progressive web apps - Flavio Copes - 2018
- The Julia express - Bogumil Kaminski - 2019
- Am Git book contents - Allen Downey
- Version control with Git - Software carpentry
- Ten simple rules for structuring papers - Mensh and Kording - 2017.
- File organization best practices - Andrew Tran - 2018
- The gold standard of data science project management - Matt.0 - 2018
- The scientific paper is obsolete - James Somers - 2018
- What nobody tells you about documentation - Daniele Procida - 2018
- Blogdown book - Yihui Xie et al. - 2019
- Embed slides in your blog - Tim Mastny - 2018
- Up and running with blogdown - Alison Presmanes Hill - 2017
- Getting going with blogdown and Hugo - Mike Treglia - 2018
- Building an R package with Fortran - Avraham Adler - 2018
- Romp - Drew Schmidt - 2016
- Calling C and Fortran from R - Charles Geyer
- Now you C me - Davis Vaughan - 2019