- Mining of Massive Datasets (Jure Leskovec, et al)
- Data Mining for the Masses (Matthew North)
- Fundamentals of Data Visualization: Informative Figures
- Data Visualization: A Practical Introduction (Kieran Healy)
- Hands-On Data Visualization: From Spreadsheets to Code
- R Graphics Cookbook: Practical Recipes for Visualizing Data
- Engineering of Big Data Processing (Piotr Fulmański)
- Algorithms for Big Data (Hannah Bast, et al)
- Engineering Agile Big-Data Systems (Kevin Feeney, et al)
- SQL Performance Explained: Everything Developers Need to Know
- Database Lifecycle Management: Achieving Continuous Delivery
- The Internals of PostgreSQL (Hironobu Suzuki)
- Introduction to Data Science (Rafael A. Irizarry)
- Introduction to Probability for Data Science (Stanley Chan)
- Data Science at the Command Line, 2nd Ed. (Jeroen Janssens)
- Computational and Inferential: The Foundations of Data Science
- Data Science: Theories, Models, Algorithms, and Analytics
- Geographic Data Science with Python (Sergio Rey, et al.)
- R for Data Science: Visualize, Model, Transform, Tidy, Import
- R Programming for Data Science (Roger D. Peng)
- Spectral Feature Selection for Data Mining (Zheng A. Zhao, et al.)
- Understanding Big Data: Analytics for Hadoop and Streaming Data
- Kafka: The Definitive Guide: Real-Time Data and Stream Processing
- Making Sense of Stream Processing: Behind Apache Kafka
- Scientific Visualisation: Python and Matplotlib (Nicolas P. Rougier)
- Bayesian Data Analysis (Andrew Gelman, et al.)
- Mining Social Media: Finding Stories in Internet Data
- Text Processing in Python (David Mertz)