r/datascience · 2025
Most-mentioned books in r/datascience during 2025
This page ranks books by exact tracked mentions in r/datascience within the published source unit for 2025. Counts come from the reconciled subreddit-by-month matrix; a source unit may be a bounded sample.
#1 Trustworthy Online Controlled Experiments
Ron Kohavi
The A/B testing manual that Google, Microsoft, Facebook, and LinkedIn all point at when someone asks how to run experiments correctly.
#2 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Aurélien Géron
Aurélien Géron's end-to-end ML textbook: build a working neural net with Scikit-Learn, Keras, and TensorFlow before you finish the first half.
#3 System Design Interview
Alex Xu
Alex Xu's interview prep guide that one r/cscareerquestions commenter credits with landing his current job.
#4 The Mythical Man-Month
Frederick P. Brooks Jr.
Fred Brooks spent the 1960s building IBM's OS/360 and wrote down everything that went wrong — in 1975, and it still lands.
#5 Domain-Driven Design
Eric Evans
Eric Evans's 2003 blueprint for modeling software around business domains — cited in interviews, assigned in grad programs, rarely finished.
#6 Fluent Python
Luciano Ramalho
The Python book r/Python recommends when you're done with the tutorials and need to know what the language actually does.
#7 How to Win Friends and Influence People
Dale Carnegie
A 1936 salesman's manual on making people like you, still showing up in r/selfimprovement threads 90 years later.
#8 Start with Why
Simon Sinek
Simon Sinek's one-idea book on purpose-driven business: the concept fits on a napkin, and r/Entrepreneur knows it.