Data Science Learning Resources

• Programming
• Machine Learning
• Leadership & Strategy

Programming

General

• The Pragmatic Programmer (Book)
• Clean Code (Book)

Python

• A Whirlwind Tour of Python (Book)
• Python Data Science Handbook
• Python Tricks (Book)
• Learning Python (Book)
• Effective Python (Book)

R

• R for Data Science (Book)
• Advanced R (Book)
• R Markdown: The Definitive Guide (Book)
• bookdown: Authoring Books and Technical Documents with R Markdown (Book)
• Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving (Book)
• Automated Data Collection with R (Book)
• Introduction to Data Science (Book)

Spark

• Spark: The Definitive Guide: Big Data Processing Made Simple (Book)
• Learning Spark: Lightning-Fast Big Data Analysis (Book)
• Mastering Spark with R: The Complete Guide to Large-Scale Analysis and Modeling (Book)

Command Line

• The Missing Semester of Your CS Education (Online course)
• Learning the bash Shell (Book)
• The Art of the Command Line (GitHub resources)
• explainshell.com (Online help)

Containers

• Docker tips & tricks or just useful commands (Online article)
• Rocker: R configurations for Docker (GitHub resources)
• Docker and Python: making them play nicely and securely for Data Science and ML (PyCon Talk)

Functional Programming

• An Introduction to the Basic Principles of Functional Programming (Online article)
• R for Data Science, Ch. 21 (Book)
• Advanced R, Ch. 9 (Book)
• Jenny Bryan's purrr tutorials (Online tutorial)
• Foundations of Functional Programming with purrr (DataCamp)
• Intermediate Functional Programming with purrr (DataCamp)

Version Control

• Excuse me, do you have a moment to talk about version control? (Paper)
• Happy Git and GitHub for the useR (Book)
• Learn Git (Online tutorial)
• Git Commit Message Style Guide (Online guide)

Code Packaging

• Python Packaging Authority
• Python Packaging User Guide
• R Packages

Style Guide, Readability, Best Practices

• The Art of Readable Code (Book)
• The Tidyverse Style Guide (Online book)
• PEP 8 -- Style Guide for Python Code (Online guide)
• Guidelines for code reviews (README)
• Code Review Best Practices (Blog post)

Testing

• Testing R Code (Book)
• Python Testing with pytest (Book)
• Multiply your Testing Effectiveness with Parameterized Testing (PyCon Talk)
• Test-Driven Development (Book)

Machine Learning

General

• Introduction to Statistical Learning (Book)
• Applied Predictive Modeling (Book)
• Elements of Statistical Learning (Book)
• Computer Age of Statistical Inference (Book)
• Statistical Modeling: The Two Cultures (Paper)
• Deep Learning (Book)
• Hands-On Machine Learning with Scikit-Learn & TensorFlow (Book | GitHub)
• Hands-On Machine Learning with R (Book)
• Google's Machine Learning Crash Course (MOOC)

Unsupervised Modeling

• ISLR: Ch. 10.3 Clustering Methods (Book chapter)
• A K-Means Clustering Algorithm (Paper)
• Generalized Low Rank Models (Paper)
• Deep Learning Ch. 15 Autoencoders (Book chapter)
• Hands-On Mach. Learning with Scikit-Learn Ch. 15 Autoencoders (Book chapter | GitHub resource)
• Sparse autoencoder (Andrew Ng CS294A lecture notes)

A/B Testing

• Lessons from Running Thoursands of A/B Tests (Online presentation with many references)
• Online Controlled Experiments at Large Scale (Paper)
• Peaking at A/B Tests (Paper)
• Multi-armed Bandit (Online tutorial)
• A Modern Bayesian Look at the Multi-armed Bandit (Paper behind above online tutorial)
• Predicting Search Satisfaction Metrics with Interleaved Comparisons (Paper)
• Evaluating Retrieval Performance using Clickthrough Data (Paper)

Multivariate Adaptive Regression Splines

• Multivariate Adaptive Regression Splines (Friedman's original paper)
• APM: Ch. 7.2 Multivariate Adaptive Regression Splines (Book chapter)
• ESL: Ch. 9.4 Multivariate Adaptive Regression Splines (Book chapter)
• Notes on the earth package (Paper)

K-Nearest Neighbor

• k-Nearest neighbour classifiers (Paper)
• APM: Ch. 7.4 & 13.5 K-Nearest Neighbors (Book chapter)
• ESL: Ch. 13.3 k-Nearest-Neighbor Classifiers (Book chapter)

Random Forests

• An Introduction to Recursive Partitioning Using the RPART Routines (Paper)
• Random Forests - Leo Breiman's original research paper (Paper)

Gradient Boosting Machines

• How to explain gradient boosting (Online tutorial)
• Trevor Hastie - Gradient Boosting & Random Forests at H2O World 2014 (YouTube)
• Trevor Hastie - Data Science of GBM (2013) (slides)
• Mark Landry - Gradient Boosting Method and Random Forest at H2O World 2015 (YouTube)
• Peter Prettenhofer - Gradient Boosted Regression Trees in scikit-learn at PyData London 2014 (YouTube)
• Alexey Natekin1 and Alois Knoll - Gradient boosting machines, a tutorial (Paper)

Deep Learning

• Deep Learning with R (Book)
• Deep Learning with Python (Book)
• Deep Learning Specialization (MOOC)
• keras.rstudio.com (Online articles & tutorials)
• blogs.rstudio.com/tensorflow (Online articles & tutorials)
• Illustrated Guide to Recurrent Neural Networks (Blog)
• Illustrated Guide on Vanishing Gradients (Blog)
• Illustrated Guide to LSTMs and GRUs (Blog)
• Understanding LSTMs (Blog)
• Rohan & Lenny: Recurrent Neural Networks & LSTMs (Blog)
• The Unreasonable Effectiveness of Recurrent Neural Networks (Blog)
• Revisiting Small Batch Training for Deep Neural Networks (Paper)
• On Loss Functions for Deep Neural Networks in Classification (Paper)
• Practical Recommendations for Gradient-Based Training of Deep Architectures (Paper)
• Efficient BackProp (Paper)
• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification (Paper)
• Cyclical Learning Rates for Training Neural Networks (Paper)
• A Disciplined Approach to Neural Network Hyperparameters: Part 1 – Learning Rate, Batch Size, Momentum, and Weight Decay (Paper)
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (Paper)

Ensembles / Model Stacking / Super Learners

• Ensemble Methods in Machine Learning (Paper)
• Stacked Regressions (Paper)
• Super Learner (Paper)

Natural Language Processing / Text Mining

• Text Mining with R (Book)
• Probabilistic Topic Models (Paper)
• The Illustrated Word2vec (Online tutorial)
• Sebastian Ruder's series on Word Embeddings (Online articles & tutorials)
• Neural Models for Information Retrieval (Paper)
• Why do we use word embeddings in NLP? (Blog)

Tuning

• Hyperparameters and Tuning Strategies for Random Forest (Paper)
• Tunability: Importance of Hyperparameters of Machine Learning Algorithms (Paper)
• Machine Learning Benchmarks and Random Forest Regression (Paper)
• Random Search for Hyperparameter Optimization (Paper)

Feature Engineering

• Feature Engineering for Machine Learning (Book)
• Feature Engineering and Selection: A Practical Approach for Predictive Models (Book)

Feature Selection

• Feature Selection with the Boruta Package (Paper)
• APM: Ch. 19 An Introduction to Feature Selection (Book chapter)

Machine Learning Interpretability

• Scott Lundberg's presentation on SHAP
• H2O.ai Machine Learning Interpretability Resources (GitHub resources)
• Patrick Hall's Awesome Machine Learning Interpretability Resources (GitHub resources)
• Interpretable Machine Learning (Book)
• Visualizing the Feature Importance for Black Box Models (Paper)
• A Simple and Effective Model-Based Variable Importance Measure (Paper)
• Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation (Paper)
• pdp: An R Package for Constructing Partial Dependence Plots (Paper)
• "Why Should I Trust You?": Explaining the Predictions of Any Classifier (Paper)
• A Unified Approach to Interpreting Model Predictions (Paper)
• Consistent Individualized Feature Attribution for Tree Ensembles (Paper)
• On the Art and Science of Machine Learning Explanations (Paper)
• Explanation in artificial intelligence: Insights from the social sciences (Paper)
• Please Stop Permuting Features: An Explanation and Alternatives (Paper)
• A Stratification Approach to Partial Dependence for Codependent Variables (Paper)
• Explaining Machine Learning Classifiers through Diverse Counterfactual Examples (Paper)

Auto ML

• A Review of Automatic Selection Methods for Machine Learning Algorithms and Hyperparameter Values (Paper)
• Learning Multiple Defaults for Machine Learning Algorithms (Paper)

Benchmarking

• The Design and Analysis of Benchmark Experiments (Paper)
• Szilard Pafka's ML Benchmarking Research (GitHub resources)
• Data-driven advice for applying machine learning to bioinformatics problems (Paper)

Resampling Procedures

• Futility Analysis in the Cross-Validation of Machine Learning Models (Paper)
• Estimating Classification Error Rate: Repeated Cross-validation, Repeated Hold-out, and Bootstrap (Paper)

Productionalization

• 150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com (Paper)
• Hidden Technical Debt in Machine Learning Systems (Paper)
• Deep Learning in Production (Github resources)

Leadership & Strategy

• Platform Revolution (Book)
• No Rules Rules: Netflix and the Culture of Reinvention (Book)
• The Influential Product Manager: How to Lead and Launch Successful Technology Products (Book)