Data Science Library (LibDS)

Curated list of books on the theme of Data Science for beginners and specialists (mostly using Python).

Obviously, nobody can read all the books, but this list can help you to decide which book to explore for really useful knowledge.

🔥 Must-read

• Annalyn Ng. Numsense! Data Science for the Layman: No Math Added
• Sally Coldwell. Statistics Unplugged (4th Edition)
• Peter Bruce. Practical Statistics for Data Scientists: 50 Essential Concepts
• Christopher M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics)
• Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep Learning (Adaptive Computation and Machine Learning series)
• Edward R. Tufte. The Visual Display of Quantitative Information
• Aditya Bhargava. Grokking Algorithms: An illustrated guide for programmers and other curious people

Data Science prerequisites

Maths

• Ivan Savov. No bullshit guide to math and physics
• Ivan Savov. No bullshit guide to linear algebra
• Morris Kline. Mathematics for the Nonmathematician
• Paul G. Hoel, Sidney C. Port, Charles J. Stone. Introduction to Probability Theory
• Dimitri P. Bertsekas. Introduction to Probability, 2nd Edition
• Jay L. Devore. Probability and Statistics for Engineering and the Sciences
• Dan Morris. Bayes' Theorem Examples: A Visual Introduction For Beginners
• Barbara Oakley PhD. A Mind for Numbers: How to Excel at Math and Science (Even If You Flunked Algebra)

Statistics

• 🔥 Sally Coldwell. Statistics Unplugged (4th Edition)
• Stanton A. Glantz. Primer of Biostatistics, Seventh Edition
• Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning: with Applications in R
• Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
• 🔥 Peter Bruce. Practical Statistics for Data Scientists: 50 Essential Concepts
• Neil J. Salkind. Statistics for People Who (Think They) Hate Statistics
• Deborah J. Rumsey. Statistics For Dummies
• Charles Wheelan. Naked Statistics: Stripping the Dread from the Data

Programming

Python

• 🔥 Mark Lutz. Python Pocket Reference: Python In Your Pocket
• Nathan Clark. Python: Programming Basics for Absolute Beginners (Step-By-Step Python Book 1)
• Eric Matthes. Python Crash Course: A Hands-On, Project-Based Introduction to Programming
• Joel Grus. Data Science from Scratch: First Principles with Python
• Wes McKinney. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
• Jake VanderPlas. Python Data Science Handbook: Essential Tools for Working with Data
• Al Sweigart. Automate the Boring Stuff with Python: Practical Programming for Total Beginners
• Dan Bader. Python Tricks: A Buffet of Awesome Python Features
• 🔥 Daniel Furtado, Marcus Pennington. Python Programming Blueprints

R

• Hadley Wickham. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
• Paul Teetor. R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics (O'reilly Cookbooks)
• Nina Zumel, John Mount, Jim Porzak. Practical Data Science with R

Machine Learning (ML)

• Scott Hartshorn. Machine Learning With Boosting: A Beginner's Guide
• Oliver Theobald. Machine Learning For Absolute Beginners: A Plain English Introduction
• 🔥 Sebastian Raschka. Python Machine Learning, 1st Edition
• Sebastian Raschka, Vahid Mirjalili. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition
• 🔥 Andreas C. Müller, Sarah Guido. Introduction to Machine Learning with Python: A Guide for Data Scientists
• Aurélien Géron. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Kevin P. Murphy. Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

Deep Learning (DL)

• Michael Taylor. Make Your Own Neural Network: An In-depth Visual Introduction For Beginners
• Michael Taylor. The Math of Neural Networks
• 🔥 Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep Learning (Adaptive Computation and Machine Learning series)
• 🔥 Sebastian Raschka, Vahid Mirjalili. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition
• Francois Chollet. Deep Learning with Python
• Antonio Gulli, Sujit Pal. Deep Learning with Keras: Implementing deep learning models and neural networks with the power of Python
• Indra den Bakker. Python Deep Learning Cookbook
• Navin Kumar Manaswi. Deep Learning with Applications Using Python

Computer Vision (CV)

• Jan Erik Solem. Programming Computer Vision with Python: Tools and algorithms for analyzing images
• Gary Bradski, Adrian Kaehler. Learning OpenCV: Computer Vision with the OpenCV Library
• Michael Beyeler. Machine Learning for OpenCV: Intelligent image processing with Python
• Rajalingappaa Shanmugamani. Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras
• Abhinav Dadhich. Practical Computer Vision: Extract insightful information from images using TensorFlow, Keras, and OpenCV

Natural Language Processing (NLP)

• 🔥 Steven Bird, Ewan Klein, Edward Loper. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit
• Dipanjan Sarkar. Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data
• Krishna Bhavsar. Natural Language Processing with Python Cookbook: Over 60 recipes to implement text analytics solutions using deep learning principles
• Julia Silge, David Robinson. Text Mining with R: A Tidy Approach

Recommender Systems

• Daniel Schall. Social Network-Based Recommender Systems
• Aristomenis S. Lampropoulos, George A. Tsihrintzis. Machine Learning Paradigms: Applications in Recommender Systems
• Michele Usuelli, Suresh K. Gorakala. Building a Recommendation System with R

Data Vizualization

• Edward R. Tufte. The Visual Display of Quantitative Information
• Dona M. Wong. The Wall Street Journal Guide to Information Graphics: The Dos and Don'ts of Presenting Data, Facts, and Figures
• Ben Jones. Communicating Data with Tableau: Designing, Developing, and Delivering Data Visualizations
• Nathan Yau. Visualize This: The FlowingData Guide to Design, Visualization, and Statistics
• Scott Murray. Interactive Data Visualization for the Web: An Introduction to Designing with D3
• Steve Wexler. The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios

Data Collection

• Ryan Mitchell. Web Scraping with Python: Collecting More Data from the Modern Web
• Matthew A. Russell. Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More
• Seppe van den Broucke. Practical Web Scraping for Data Science: Best Practices and Examples with Python
• Dimitrios Kouzis-Loukas. Learning Scrapy

Databases

• Luc Perkins, Eric Redmond, Jim Wilson. Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement
• Martin Kleppmann. Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

SQL

• Alan Beaulieu. Learning SQL: Master SQL Fundamentals
• ClydeBank Technology. SQL: QuickStart Guide - The Simplified Beginner's Guide To SQL (SQL, SQL Server, Structured Query Language)
• Allen G. Taylor. SQL All-in-One For Dummies
• Ben Forta. SQL in 10 Minutes, Sams Teach Yourself (4th Edition)
• Jonathan Gennick. SQL Pocket Guide: A Guide to SQL Usage
• Anthony Molinaro. SQL Cookbook: Query Solutions and Techniques for Database Developers

NoSQL

• Pramod J. Sadalage, Martin Fowler. NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence
• Kristina Chodorow. MongoDB: The Definitive Guide: Powerful and Scalable Data Storage
• Brad Dayley. NoSQL with MongoDB in 24 Hours, Sams Teach Yourself
• Rick Copeland. MongoDB Applied Design Patterns: Practical Use Cases with the Leading NoSQL Database

Big Data

Hadoop

• Benjamin Bengfort, Jenny Kim. Data Analytics with Hadoop: An Introduction for Data Scientists
• Jeffrey Aven. Hadoop in 24 Hours, Sams Teach Yourself
• Tom White. Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale
• Edward Capriolo, Dean Wampler, Jason Rutherglen. Programming Hive: Data Warehouse and Query Language for Hadoop_Aravind Shenoy_. Hadoop Explained
• Ben Spivey, Joey Echeverria. Hadoop Security: Protecting Your Big Data Platform

Spark

• Bill Chambers, Matei Zaharia. Spark: The Definitive Guide: Big Data Processing Made Simple
• Jeffrey Aven. Apache Spark in 24 Hours, Sams Teach Yourself
• Holden Karau, Rachel Warren. High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark
• Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills.Advanced Analytics with Spark: Patterns for Learning from Data at Scale
• Russell Jurney. Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark

Data Science for Business

• 🔥 Eric Siegel. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
• 🔥 Foster Provost. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
• Roger Peng (Author), Elizabeth Matsui (Contributor). The Art of Data Science
• Doug Rose. Data Science: Create Teams That Ask the Right Questions and Deliver Real Value
• Cole Nussbaumer Knaflic. Storytelling with Data: A Data Visualization Guide for Business Professionals
• John W. Foreman. Data Smart: Using Data Science to Transform Information into Insight
• DJ Patil, Hilary Mason. Data Driven
• Dan McCreary, Ann Kelly. Making Sense of NoSQL: A guide for managers and the rest of us

Tools

• Jake VanderPlas. Python Data Science Handbook: Essential Tools for Working with Data
• Alexander Stepanov. Data Science in Python (3 Book Series)
• Dan Toomey. Jupyter for Data Science: Exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter
• Joshua Cook. Docker for Data Science: Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server
• Ryan Hodson. Ry's Git Tutorial
• Scott Chacon, Ben Straub. Pro Git
• Valliappa Lakshmanan. Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning
• Yoshiyasu Takefuji. GPU parallel computing for machine learning in Python: how to build a parallel computer