List of resources that I have found useful to improve my data science skills
- Trustworthy online controlled experiments
- Building Machine Learning Powered Applications: Going from Idea to Product
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
- Sean Taylor Keynote at CDSM20
- Principles of Good Machine Learning Systems Design by Chip Huyen
- How To Communicate Stats from Research
- Statistical rethinking (video lectures)
- Regression and other stories (examples)
- Bayesian Data Analysis
- Bayesian Methods for Hackers
- Bayesian statistics and modelling by Rens van de Schoot, Sarah Depaoli, Ruth King, Bianca Kramer, Kaspar Märtens, Mahlet G. Tadesse, Marina Vannucci, Andrew Gelman, Duco Veen, Joukje Willemsen and Christopher Yau
- Bayesian Workflow by Andrew Gelman, Aki Vehtari, Daniel Simpson, Charles C. Margossian, Bob Carpenter, Yuling Yao, Lauren Kennedy, Jonah Gabry, Paul-Christian Bürkner and Martin Modrák
- An Introduction to Probability and Computational Bayesian Statistics
- Michael Betancourt - case studies
- Causal Inference in Statistics: A Primer by Judea Pearl, Madelyn Glymour and Nicholas P. Jewell
- Causal Inference: What If by Miguel Hernan and Jamie Robins
- Causal Inference: The Mixtape by Scott Cunningham
- Elements of Causal Inference by Jonas Peters, Dominik Janzing and Bernhard Schölkopf
- Introduction to Causal Inference by Brady Neal
- A Crash Course in Good and Bad Controls by Carlos Cinelli, Andrew Forney and Judea Pearl
- Does obesity shorten life? The importance of well-defined interventions to answer causal questions
- Does Obesity Shorten Life? Or is it the Soda? On Non-manipulable Causes
- Selection Mechanisms and Their Consequences: Understanding and Addressing Selection Bias
- Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising by Léon Bottou, Jonas Peters, Joaquin Quiñonero-Candela, Denis X. Charles, D. Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, Ed Snelson
- Grokking Algorithms: An Illustrated Guide for Programmers and Other Curious People
- Real-World Algorithms: A Beginner's Guide
- Fairness and machine learning by Solon Barocas, Moritz Hardt and Arvind Narayanan
- The Ethical Algorithm by Michael Kearns and Aaron Roth
- Having confidence in confidence intervals by Ellie Murray
- Essence of linear algebra - 3Blue1Brown
- Optimization Algorithms course by Constantine Caramanis
- Pattern Recognition and Machine Learning
- Probabilistic Machine Learning: An Introduction (2nd edition)
- Model-Based Machine Learning
- Deep Learning
- Dive into Deep Learning
- Convex Optimization Basics - Intelligent Systems Lab
- Introduction to Multi-Armed Bandits by Aleksandrs Slivkins
- A Tutorial on Thompson Sampling by Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband and Zheng Wen
- Algorithms for Decision Making by Mykel Kochenderfer, Tim Wheeler and Kyle Wray
- Architecture Patterns with Python by Harry Percival and Bob Gregory
- Fluent Python by Luciano Ramalho
- Effective Python by Brett Slatkin
- Practical Python Programming by David Beazley
- Pandas
- Numpy
- SQL
- Jax
- Geopandas
- Spark
- PyTorch
- Airflow
- Prefect
- Dagster
- scikit-learn
- statsmodels
- PyMC3
- Pyro / NumPyro
- Stan
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Git
-
Docker
-
Python environments
- Altair
- Matplotlib