- An Introduction to R and Python for Data Analysis: A Side-By-Side Approach (2023)
- Introduction to Python for Econometrics, Statistics, and Data Analysis (2021)
- Veridical Data Science: Data Analysis and Decision Making (2024)
- Julia Data Science (Jose Storopoli, et al.)
- The Data Science Workshop, 2nd Edition (Anthony So, et al.)
- Data Science Live Book: An Intuitive and Practical Approach
- Introduction to Data Science (Rafael A. Irizarry)
- Python Data Science Handbook: Essential Tools (Jake VanderPlas)
- Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter (2023)
- Tidyverse Skills for Data Science (Carrie Wright, et al)
- Doing Data Science in R: An Introduction for Social Scientists
- Data Mining and Machine Learning: Fundamental Concepts and Algorithms
- 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.)
- Geographic Data Science with R: Visualizing and Analyzing
- R for Geographic Data Science (Stefano De Sabbata)
- Spatial Data Science: With Applications in R (Edzer Pebesma, 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)
- Statistical Inference via Data Science: A ModernDive into R and the Tidyverse