This document is a curated list of data science resources covering everything from statistics, mathematical theory, and coding languages to real world applications of machine learning and artificial intelligence techniques. Resource types include books, courses, tutorials, scholarly articles, and blog posts.
Books | Courses | Tutorials | Posts
Books | Courses | Tutorials | Cheat Sheets
Books | Courses | Libraries | Tutorials | Cheat Sheets
Books | Courses | Cheat Sheets
Books | Courses | Cheat Sheets
- An Introduction to Statistical Learning with Applications in R
- The Elements of Statistical Learning Data Mining, Inference, and Prediction
- Naked Statistics: Stripping the Dread from the Data
- Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
- How to Lie with Statistics
- Principles of Statistics
- Statistics in Plain English
- Statistics in a Nutshell: A Desktop Quick Reference
- MITx's Statistics and Data Science MicroMasters® Program
- UCx's Statistical Analysis in R Professional Certificate
- UMGC and USMx's Bioinformatics MicroMasters® Program
- Statistics for Business Analytics and Data Science A-Z™
- Statistics for Data Science and Business Analysis
- Probability and Statistics for Business and Data Science
- Statistics and Probability | Khan Academy
- Introductory Statistics
- Statistics for Data Science | Probability and Statistics | Statistics Tutorial | Ph.D. (Stanford)
- A Complete Tutorial On Statistics And Probability | Edureka
- Git Pocket Guide: A Working Introduction
- Version Control with Git: Powerful Tools and Techniques for Collaborative Software Development
- Pro Git
- Git for Teams: A User-Centered Approach to Creating Efficient Workflows in Git
- An introduction to Git and GitHub
- Git Cheat Sheet | GitHub
- Learn Python 3 the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code
- Automate the Boring Stuff with Python: Practical Programming for Total Beginners
- Python Cookbook
- Think Python: How to Think Like a Computer Scientist
- Python Data Science Handbook: Essential Tools for Working with Data
- Python Feature Engineering Cookbook
- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
- Python Data Cleaning Cookbook: Modern techniques and Python tools to detect and remove dirty data and extract key insights
- IBM's Python Data Science
- Using Python for Research
- Python for Everybody
- Applied Data Science with Python
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
- R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics
- The Book of R: A First Course in Programming and Statistics
- SAS Certified Specialist Prep Guide: Base Programming Using SAS 9.4
- SAS Certified Professional Prep Guide: Advanced Programming Using SAS 9.4
- Learning SQL: Generate, Manipulate, and Retrieve Data
- Practical SQL: A Beginner's Guide to Storytelling with Data
- SQL Cookbook: Query Solutions and Techniques for Database Developers
- SQL for Data Analytics: Perform fast and efficient data analysis with the power of SQL
- SQL for Data Science: Data Cleaning, Wrangling and Analytics with Relational Databases
- The Visual Display of Quantitative Information
- Now You See It: Simple Visualization Techniques for Quantitative Analysis
- The Wall Street Journal Guide to Information Graphics: The Dos and Don'ts of Presenting Data, Facts, and Figures
- Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations
- Storytelling with Data: A Data Visualization Guide for Business Professionals
- Information Is Beautiful
- Knowledge Is Beautiful: Impossible Ideas, Invisible Patterns, Hidden Connections
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- Introduction to Machine Learning with Python: A Guide for Data Scientists
- Mastering Machine Learning Algorithms
- AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence
- Introduction to Time Series and Forecasting
- Time Series Analysis and Its Applications: With R Examples
- Practical Time Series Analysis: Prediction with Statistics and Machine Learning
- The Analysis of Time Series: An Introduction with R
- Time-Series Prediction and Applications: A Machine Intelligence Approach
- A Comprehensive Beginner's Guide to Time Series Forecast
- Time Series Forecasting With Prophet in Python
- Time Series Analysis in Python - A Comprehensive Guide with Examples
- Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing
- Designing with Data: Improving the User Experience with A/B Testing
- Optimal Design of Experiments: A Case Study Approach
- Design and Analysis of Experiments
- A/B Testing by Google
- A/B Testing for Business Analysts
- Statistical Inference and Modeling for High-throughput Experiments
- Data Science: Inferential Thinking through Simulations
- UX Evaluation
- Data.gov
- Kaggle
- Datahub.io
- UCI Machine Learning Repository
- Earth Data
- CERN Open Data
- Global Health Observatory
- FBI Crime Data Explorer
- The Cancer Genomic Atlas
- How to pass the Facebook Data Science Interview
- The Facebook Data Scientist Interview
- Facebook Data Science Interview Questions and Solutions
- 109 Data Science Interview Questions and Answers
- Top 30 Data Science Interview Questions
- Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
- Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results
- Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
- The Enterprise Big Data Lake: Delivering the Promise of Big Data and Data Science