This Blog contains a few useful stuff for regarding data science. They focus more on the technical side. My suggestion is, do not try to learn them all at once, human beings are more productive when we work on a single task.

  1. Andrew Ng’s ML Course CS229
  2. Columbia University’s EdX ML Course
  3. Hugo Larochelle’s Sherbrooke U Neural Networks Course
  4. Chris Manning & Richard Socher’s Stanford NLP with Deep Learning Lectures
  5. Google’s ML Crash Course
  6. Intro to Database - CMU
  7. CS Missing Semester About Tools - MIT
  8. Statistical Rethinking
  9. Introduction to Algorithms
  1. Machine Learning: The High Interest Credit Card of Technical Debt
  2. A Few Useful Things to Know about Machine Learning
  3. Rules of Machine Learning: Best Practices for ML Engineering
  4. Machine Learning Cheatsheet
  5. Statistical Rethinking
  6. Mathematics for Machine Learning
  7. Machine Learning Cheatsheets CS229
  8. Graphical Probability
  9. Python Algorithms Implementations
  10. Convolutional Neural Networks for Visual Recognition - Stanford CS231N

Textbooks

  1. Bishop - Pattern Recognition & Machine Learning
  2. Murphy - Machine Learning: A Probabilistic Perspective
  3. MacKay - Information Theory, Inference, and Learning Algorithms
  4. Sutton, Barto - Reinforcement Learning: An Introduction
  5. Goodfellow, Bengio, Courville - Deep Learning
  6. Jurafsky, Martin - Speech and Language Processing
  7. An Introduction to Statistical Learning
  8. The Elements of Statistical Learning
  9. Convex Optimization - Boyd
  10. Math For Machine Learning - UPenn

Major Academic Conferences

  1. NIPS
  2. ICML
  3. ICLR
  4. ACL
  5. CVPR

Interesting Algorithms/Implementations

  1. Annoy - Approximate Nearest Neighbor (Oh Yeah!) - Spotify
  2. Listing2Vec - Airbnb
  3. Uplift modeling and causal inference -Uber
  4. Machine Learning Pipeline
  5. Personalized Cuisine Filter
  6. Recurrent Unit Recommender
  7. Distill Pub
  8. Stitch Fix

Visualization

  1. Kepler - Uber
  2. Pyecharts - Baidu
  3. R2D3
  4. Plotly
  5. ShinyR
  6. Easy Website
  7. Sample Shiny Tool
  8. Flourish
  9. D3 Visualization

Experimentation

  1. Facebook - PlanOut
  2. Netflix
  3. Uber - Experimentation
  4. Airbnb
  5. Convoy - Bayesian A/B
  6. Experiment Design Tool R
  7. Non-Inferiority - StitchFix
  8. Simple Experiment Design in R
  9. Simpsons Paradox
  10. Experiment Pitfalls

Multiple Arms Bandit Models

  1. Thompson Sampling Tutorial
  2. Bernoulli Distribution Case
  3. Doordash Recommendation Case
  4. R contextual Package
  5. Python Application
  6. Optimizely Multi Bandit Introduction
  7. Introduction to Multi Bandit Arms
  8. Algorithms for the multi-armed bandit problem

Natural Language Processing

  1. Allen-NLP
  2. spaCy
  3. CoreNLP
  4. XLNet

Chinese Machine Learning Resources

  1. Basic Machine Leaning - Chinese
  2. 神经网络与深度学习
  3. 动手学深度学习
  4. 南瓜书

Bayesian

  1. Bayesian AB in R
  2. Bayesian Methods for Hackers
  3. Bayesian Introduction
  4. Bayesian Power Analysis
  5. abtest in R - A Bayesian Approach

Useful Stat Courses

  1. Probability at Duke
  2. Probability at UCLA
  3. Mathematical Statistics at Duke
  4. Mathematical Statistics at UCLA
  5. Optimization and Statistics
  6. BRMS
  7. Statistics for Applications - MIT

Data Science Interview

  1. 120 Data Science Interview Questions
  2. Data Science Interview at Top Tech Companies
  3. Basic Stats Models in R
  4. Statistical Test and GLM
  5. R and Machine Learning

Causal Inference

  1. Causal Impact
  2. Causal Net
  3. Causal ML - Uber
  4. DoWhy - MicroSoft
  5. Basic Notations - MIT
  6. CAUSAL INFERENCE: THE MIXTAPE

Time Series

  1. Prophet - Facebook
  2. RNN - Tensorflow
  3. BSTS - Google
  4. seq2seq
  5. LGBM/LSTM
  6. LSTNet

Business

  1. TechCrunch KPIs
  2. Case Study Tech Companies - Lewis C. Lin
  3. Experimentation Culture - HBR

Python Programming

  1. Python to Numpy
  2. Guido van Rossum’s Python Doc
  3. Algorithms
  4. Pandas Samples
  5. The Hitchhiker’s Guide to Python
  6. Python Official Guide
  7. Fluent Python
  8. Python Cookbook