More Data Science Books

Programming Collective Intelligence by Toby Segaran (O’Reilly, 2007), available at O’Reilly or Amazon

Stat Labs: Mathematical Statistics Through Applications by Deborah Nolan and Terry Speed (Springer, 2000), available at SpringerLink or Amazon

Version Control with Subversion, 2nd Edition, by C. Michael Pilato, Ben Collins-Sussman, and Brian W. Fitzpatrick (O’Reilly, 2008), available at O’Reilly or Amazon or the book can be legally downloaded for free in pdf format.

Introduction to Data Technologies by Paul Murrell (CRC Press, 2009), available at O’Reilly or Amazon or the book can be legally downloaded for free in pdf format.

Computational Statistics by James E. Gentle (Springer, 2009), available at SpringerLink or Amazon

Hadoop: The Definitive Guide, 3rd Edition, by Tom White (O’Reilly, 2012)

Thinking with Data by Max Shron (O’Reilly, 2014), available at O’Reilly or Amazon

Machine Learning for Hackers by Drew Conway and John Myles White (O’Reilly, 2012), available at O’Reilly or Amazon

Bad Data Handbook by Q. Ethan McCallum (O’Reilly, 2013), available at O’Reilly or Amazon

Agile Data Science 2.0 by Russell Jurney (O’Reilly, 2017), available at O’Reilly or Amazon

Doing Data Science by Cathy O’Neil and Rachel Schutt (O’Reilly, 2014), available at O’Reilly or Amazon

Statistical Methods in Bioinformatics: An Introduction, 2nd Edition by Warren J. Ewens and Gregory R. Grant (Springer, 2005), available at SpringerLink or Amazon

The Elements of Statistical Learning, Second Edition, by Trevor Hastie, Robert Tibshirani, and Jerome Friedman (Springer, 2009), available at SpringerLink or Amazon

Introductory Time Series with R by Andrew V. Metcalfe and Paul S.P. Cowpertwait (Springer, 2009), available at SpringerLink or Amazon

Forest Analytics with R by Andrew P. Robinson and Jeff D. Hamann (Springer, 2011), available at SpringerLink or Amazon

Applied Spatial Data Analysis with R, Second Edition, by Roger S. Bivand, Edzer Pebesma, and Virgilio Gómez-Rubio (Springer, 2013), available at SpringerLink or Amazon

An Introduction to Statistical Learning, Second Edition, by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (Springer, 2021), available at SpringerLink or Amazon

Time Series Analysis and Its Applications, Fourth Edition by Robert H. Shumway and David S. Stoffer (Springer, 2017), available at SpringerLink or Amazon

An Approach to Providing Mathematical Annotation in Plots, by Paul Murrell and Ross Ihaka, Journal of Computational and Graphical Statistics 9(3):582-599, 2000, JSTOR

Creating More Effective Graphs by Naomi B. Robbins (Wiley-Interscience, 2005)

The Elements of Graphing Data, Revised Edition, by William S. Cleveland (Hobart Press, 1994)

The Grammar of Graphics, 2nd Edition, by Leland Wilkinson (Springer, 2005)

Graphics of Large Datasets, by Antony Unwin, Martin Theus, Heike Hofmann (Springer, 2006)

How to display data badly, by Howard Wainer, The American Statistician 38(2), 1984, JSTOR

Maps for Advocacy, by Tactical Technology Collective (2008) www.tacticaltech.org/maps-advocacy

S-PLUS Trellis Graphics User’s Manual, by Richard A. Becker and William S. Cleveland (1996), www.stat.purdue.edu/~wsc/papers/trellis.user.pdf

The Visual Display of Quantitative Information, 2nd Edition, by Edward R. Tufte (Graphics Press, 2001)

Beautiful Evidence by Edward R. Tufte (Graphics Press, 2006)

Visual Explanations by Edward R. Tufte (Graphics Press, 1997)

Envisioning Information by Edward R. Tufte (Graphics Press, 1990)

Seeing with Fresh Eyes by Edward R. Tufte (Graphics Press, 2020)

Visualizing Data, by William S. Cleveland (Hobart Press, 1993)

Visualizing Data, by Ben Fry (O’Reilly, 2008)

Visualizing Information for Advocacy, by Tactical Technology Collective (2008) visualisingadvocacy.org/

Learn Git in a Month of Lunches by Rick Umali (Manning, 2015)

Interactive Data Visualization for the Web, 2nd Edition, by Scott Murray (O’Reilly, 2017)

C++ Software Design by Klaus Iglberger (O’Reilly, 2022)

Sport Business Analytics by C. Keith Harrison and Scott Bukstein (CRC Press, 2017)

Introduction to DevOps with Chocolate, LEGO and Scrum Game by Dana Pylayeva (APress, 2017)

The Atlas of the Real World by Daniel Dorling, Mark Newman, and Anna Barford (Thames & Hudson, 2008)

Tableau Desktop Cookbook by Lorna Brown (O’Reilly, 2021)

Innovative Tableau by Ryan Sleeper (O’Reilly, 2020)

Practical Tableau by Ryan Sleeper (O’Reilly, 2018)

Communicating Data with Tableau by Ben Jones (O’Reilly, 2014)

Tableau Strategies by Ann Jackson and Luke Stanke (O’Reilly, 2021)

Tableau Prep: Up & Running by Carl Allchin (O’Reilly, 2020)

97 Things Every Engineering Manager Should Know by Camille Fournier (O’Reilly, 2020)

D3 for the Impatient by Philipp K. Janert (O’Reilly, 2019)

Building Tools with GitHub by Chris Dawson and Ben Straub (O’Reilly, 2016)

Git for Teams by Emma Jane Hogbin Westby (O’Reilly, 2015)

Version Control with Git, 2nd Edition, by Jon Loeliger and Matthew McCullough (O’Reilly, 2012)

Natural Language Processing with Transformers by Lewis Tunstall, Leandro von Werra, and Thomas Wolf (O’Reilly, 2022)

Practical Natural Language Processing by Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana (O’Reilly, 2020)

Natural Language Processing with PyTorch by Delip Rao and Brian McMahan (O’Reilly, 2019)

GPT-3 by Sandra Kublik and Shubham Saboo (O’Reilly, 2022)

Natural Language Processing with Spark NLP by Alex Thomas (O’Reilly, 2020)

Deep Learning Cookbook by Douwe Osinga (O’Reilly, 2018)

Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow, 2nd Edition, by Aurélien Géron (O’Reilly, 2019)

Machine Learning by Peter Flach (Cambridge, 2012)

Deep Learning for Coders with Fastai and PyTorch by Jeremy Howard and Sylvain Gugger (O’Reilly, 2020)

Blast by Ian Korf, Mark Yandell, and Joseph Bedell (O’Reilly, 2003)

Developing Bioinformatics Computer Skills by Cynthia Gibas and Per Jambeck (O’Reilly, 2001)

Learning Microsoft Power BI: Transforming Data into Insights by Jeremey Arnold (O’Reilly, 2022)

Becoming a Data Head by Alex J. Gutman and Jordan Goldmeier (Wiley, 2021)

Computational Mathematics with SageMath by Paul Zimmermann (SIAM, 2018)

97 Things Every Cloud Engineer Should Know by Emily Freeman and Nathen Harvey (O’Reilly, 2021)

Raspberry Pi Cookbook, 3rd Edition, by Simon Monk (O’Reilly, 2020)

97 Things About Ethics Everyone in Data Science Should Know, by Bill Franks (O’Reilly, 2020)

97 Things Every Data Engineer Should Know, by Tobias Macey (O’Reilly, 2021)

97 Things Every Programmer Should Know, by Kevlin Henney (O’Reilly, 2010)

Sage for Undergraduates, 2nd Edition, by Gregory V. Bard (AMS, 2022)

Sage Beginner’s Guide, by Craig Finch (Packt, 2011)

Command-Line Rust, by Ken Youens-Clark (O’Reilly, 2022)

Programming Rust, by Jim Blandy, Jason Orendorff, and Leonora F. S. Tindall (O’Reilly, 2021)

Rust for Rustaceans by Jon Gjengset (No Starch Press, 2022)

Learning GNU Emacs, 3rd Edition, by Debra Cameron, James Elliott, Marc Loy, Eric Raymond, and Bill Rosenblatt (O’Reilly, 2005)

The Rust Programming Language, by Steve Klabnik and Carol Nichols (No Starch Press, 2019)

Introducing Data Science by Davy Cielen, Arno D. B. Meysman, and Mohamed Ali (Manning, 2016)

flex & bison by John Levine (O’Reilly, 2009)

Learning React, 2nd Edition, by Alex Banks and Eve Porcello (O’Reilly, 2020)

Practical Time Series Analysis by Aileen Nielsen (O’Reilly, 2020)

Practical Statistics for Data Scientists by Peter Bruce, Andrew Bruce, and Peter Gedeck (O’Reilly, 2017)

Building Secure and Reliable Systems by Heather Adkins, Betsy Beyer, Paul Blankinship, Piotr Lewandowski, Ana Oprea, and Adam Subblefield (O’Reilly, 2020)

JavaScript: The Good Parts by Douglas Crockford (O’Reilly, 2008)

Learning PHP, MySQL & JavaScript, 5th Edition, by Robin Nixon (O’Reilly, 2018)

Programming JavaScript Applications by Eric Elliott (O’Reilly, 2014)

Speaking JavaScript by Axel Rauschmayer (O’Reilly, 2014)

Data Science on AWS by Chris Fregly and Antje Barth (O’Reilly, 2021)

Data Science from Scratch, 2nd Edition, by Joel Grus (O’Reilly, 2019)

Think Like a Data Scientist by Brian Godsey (Manning, 2017)

Design Patterns by Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides (Addison Wesley, 1995)

Mastering Kafka Streams and ksqlDB by Mitch Seymour (O’Reilly, 2021)

Understanding Compression by Colt McAnlis and Aleks Haecky (O’Reilly, 2016)

Kubernetes Operators by Jason Dobies and Joshua Wood (O’Reilly, 2020)

Production Kubernetes by Josh Rosso, Rich Lander, Alexander Brand, and John Harris (O’Reilly, 2021)

Spark: The Definitive Guide by Bill Chambers and Matei Zaharia (O’Reilly, 2018)

Foundations for Architecting Data Solutions by Ted Malaska and Jonathan Seidman (O’Reilly, 2018)

High Performance Spark by Holden Karau and Rachel Warren (O’Reilly, 2017)

Mastering Azure Analytics by Zoiner Tejada (O’Reilly, 2017)

Programming Hive by Edward Capriolo, Dean Wampler, and Jason Rutherglen (O’Reilly, 2012)

The Enterprise Big Data Lake by Alex Gorelik (O’Reilly, 2019)

Stream Processing with Apache Spark by Gerard Maas and Francois Garillot (O’Reilly, 2019)

Modern Statistics for Modern Biology by Susan Holmes and Wolfgang Huber (Cambridge, 2019)

Data Science at the Command Line by Jeroen Janssens (O’Reilly, 2015)

Fundamentals of Data Visualization by Claus O. Wilke (O’Reilly, 2019)

Presenting to Win by Jerry Weissman (Pearson, 2009)

JavaScript Patterns by Stoyan Stefanov (O’Reilly, 2010)

JavaScript Enlightenment by Cody Lindley (O’Reilly, 2013)

Mapping Experiences, 2nd Edition, by James Kalbach (O’Reilly, 2021)

Introduction to JavaScript Object Notation by Lindsay Bassett (O’Reilly, 2015)

JavaScript Cookbook, 2nd Edition, by Shelley Powers (O’Reilly, 2015)

Kubernetes Best Practices by Brendan Burns, Eddie Villalba, Dave Strebel, and Lachlan Evenson (O’Reilly, 2020)

Kubernetes Patterns by Bilgin Ibryam and Roland Huss (O’Reilly, 2019)

Data Analysis with Open Source Tools by Philipp K. Janert (O’Reilly, 2011)

Learning to Love Data Science by Mike Barlow (O’Reilly, 2015)

Statistical Modeling, 2nd Edition, by Daniel T. Kaplan (2012) dtkaplan.github.io/SM2-bookdown/

JavaScript: The Definitive Guide, 7th Edition, by David Flanagan (O’Reilly, 2020)

CSS: The Definitive Guide, 4th Edition, by Eric Meyer and Estelle Weyl (O’Reilly, 2018)

Code Complete, 2nd Edition, by Steve McConnell (Microsoft, 2004)

Software Engineering at Google by Titus Winters, Tom Manshreck, and Hyrum Wright (O’Reilly, 2020)

Asked and Answered by Pamela E. Harris and Aris Winger (2020)

Practices and Policies by Pamela E. Harris and Aris Winger (2021)

Read and Rectify by Pamela E. Harris and Aris Winger (2022)

Testimonios by Pamela E. Harris, Alicia Prieto-Langarica, Vanessa Rivera Quiñones, Luis Sordo Vieira, Rosaura Uscanga, and Andrés R. Vindas Meléndez

Unleash Different by Rich Donovan (2018)

Hadoop Application Architectures by Mark Grover, Ted Malaska, Jonathan Seidman, and Gwen Shapira (O’Reilly, 2015)

MapReduce Design Patterns by Donald Miner and Adam Shook (O’Reilly, 2013)

Advanced Analytics with Spark, 2nd Edition, by Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills (O’Reilly, 2017)

Low-Power Computer Vision by George K. Thiruvathukal, Yung-Hsiang Lu, Jaeyoun Kim, Yiran Chen, and Bo Chen (CRC Press, 2022)

Statistics Done Wrong by Alex Reinhart (No Starch Press, 2015)

Statistics in a Nutshell by Sarah Boslaugh (O’Reilly, 2008)

Learning Spark by Jules S. Damji, Brooke Wenig, Tathagata Das, and Denny Lee (O’Reilly, 2020)

Strengthening Deep Neural Networks by Katy Warr (O’Reilly, 2019)

Reinforcement Learning by Phil Winder (O’Reilly, 2021)

Machine Learning Design Patterns by Valliappa Lakshmanan, Sara Robinson, and Michael Munn (O’Reilly, 2021)

Fundamentals of Deep Learning by Nithin Buduma (O’Reilly, 2017)

Deep Learning by Josh Patterson and Adam Gibson (O’Reilly, 2017)

AI and Machine Learning for Coders by Laurence Moroney (O’Reilly, 2021)

Building Machine Learning Powered Applications by Emmanuel Ameisen (O’Reilly, 2020)

Deep Learning for the Life Sciences by Bharath Ramsundar, Peter Eastman, Patrick Walters, and Vijay Pande (O’Reilly, 2019)

Generative Deep Learning by David Foster (O’Reilly, 2019)

Deep Learning from Scratch by Seth Weidman (O’Reilly, 2019)

Grokking Deep Learning by Andrew Trask (O’Reilly, 2019)

Real-World Machine Learning by Henrik Brink, Joseph W. Richards, and Mark Fetherolf (Manning, 2017)

Deep Learning and the Game of Go by Max Pumperla and Kevin Ferguson (Manning, 2019)

TensorFlow for Deep Learning by Bharath Ramsundar and Reza Bosagh Zadeh (O’Reilly, 2018)

Learning TensorFlow by Tom Hope, Yehezkel S. Resheff, and Itay Lieder (O’Reilly, 2017)

Practical Deep Learning for Cloud, Mobile, and Edge by Anirudh Koul, Siddha Ganju, and Meher Kasam (O’Reilly, 2020)

Algorithms in a Nutshell, 2nd Edition, by George T. Heineman, Gary Pollice, and Stanley Selkow (O’Reilly, 2016)

Making Data Visual by Danyel Fisher and Miriah Meyer (O’Reilly, 2018)

Baseball Hacks by Joseph Adler (O’Reilly, 2006)

Programming PHP, 2nd Edition, by Rasmus Lerdorf, Kevin Tatroe, and Peter MacIntyre (O’Reilly, 2006)

Software Architecture: The Hard Parts by Neal Ford, Mark Richards, Pramod Sadalage, and Zhamak Dehghani (O’Reilly, 2022)

AWS Cookbook by John Culkin and Mike Zazon (O’Reilly, 2022)

Migrating to AWS: A Manager’s Guide by Jeff Armstrong (O’Reilly, 2020)

Building Machine Learning Pipelines by Hannes Hapke and Catherine Nelson (O’Reilly, 2020)

Kafka: The Definitive Guide, 2nd Edition, by Gwen Shapira, Todd Palino, Rajini Sivaram, and Krit Petty (O’Reilly, 2022)

Data Algorithms by Mahmoud Parsian (O’Reilly, 2015)

Mining the Social Web by Matthew A. Russell and Mikhail Klassen (O’Reilly, 2019)

Bioinformatics Data Skills by Vince Buffalo (O’Reilly, 2015)

Data Analytics with Hadoop by Benjamin Bengfort and Jenny Kim (O’Reilly, 2016)

Architecting Modern Data Platforms by Jan Kunigk, Ian Buss, Paul Wilkinson, and Lars George (O’Reilly, 2019)

Hadoop in Practice by Alex Holmes (Manning, 2015)

Agile for Everybody by Matt LeMay (O’Reilly, 2019)

97 Things Every Scrum Practitioner Should Know by Gunther Verheyen (O’Reilly, 2020)

Learning Agile by Andrew Stellman and Jennifer Greene (O’Reilly, 2015)

Agile Project Management by Sam Ryan (2019)

Agile Practice Guide (Project Management Institute, 2017)