Best Mathematical & Statistical Software Books of 2025

Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street is a must-read guide for anyone who wants to land their dream job in data science, data analysis, or machine learning. Authored by two ex-Facebook employees, this 301-page book offers comprehensive coverage of the most frequently tested topics in data interviews. It provides detailed step-by-step solutions to 201 real data science interview questions asked by top companies, including Facebook, Google, Amazon, Netflix, Two Sigma, and Citadel. Additionally, the book offers valuable career advice on crafting your resume, creating portfolio projects, networking, and more. Overall, Ace the Data Science Interview is an invaluable resource for anyone looking to break into the data science industry.

The second edition of "Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python" is a comprehensive guide that provides practical guidance on applying statistical methods to data science. The book covers key statistical techniques, including exploratory data analysis, regression, and classification, and teaches readers how to avoid common statistical mistakes. The authors, Peter Bruce and Andrew Bruce, have extensive experience in statistics and data science, and the book is written in an accessible, readable format. This book is a must-read for data scientists who want to improve their statistical knowledge and apply it to real-world problems.

This comprehensive guide, "Essential Math for AI", is a must-read for anyone interested in the AI field. It provides a comprehensive understanding of the underlying mathematics necessary to build successful AI systems, focusing on real-world applications and state-of-the-art models. The book is written in an immersive and conversational style, making it easy for anyone to learn and understand. It covers topics such as regression, neural networks, convolution, optimization, probability, graphs, random walks, Markov processes, differential equations, and more. The author has done an exceptional job of making math fun and engaging, making this book stand out from other math textbooks.

If you're looking to improve your Microsoft Excel skills and save valuable time from your everyday work life, "Excel: The most updated bible to master Microsoft Excel from scratch in less than 7 minutes a day" is the solution to your problems. This book offers a complete guide to mastering Excel, from basic commands to the most advanced formulas, with step-by-step pictures and tips from a professional Excel user. The book is a top choice for anyone who wants to increase productivity and reduce stress at work.

The second edition of "A Common-Sense Guide to Data Structures and Algorithms" by Jay Wengrow offers practical techniques and real-world scenarios for mastering algorithms and data structures. The book covers Big O notation, recursion, advanced data structures, and more, with examples in JavaScript, Python, and Ruby. The exercises in each chapter help readers apply the concepts and improve their coding skills. Overall, this book is an excellent resource for developers who want to write faster, more efficient code and scale their applications.

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data is a comprehensive guide for turning raw data into insights using R. The authors, Hadley Wickham and Garrett Grolemund, introduce readers to R, RStudio, and the tidyverse, a collection of R packages designed to make data science fast, fluent, and fun. The book covers the data science cycle, from importing and wrangling data to modeling and communicating results. Each section is paired with exercises to reinforce learning. This book is an excellent resource for anyone looking to learn data science with R.

An Introduction to Statistical Learning: with Applications in R is a comprehensive guide to statistical learning techniques for analyzing complex data sets. The book covers important modeling and prediction techniques such as linear regression, clustering, and deep learning, with real-world examples and tutorials on implementing the analyses in R. This second edition includes new chapters on deep learning, survival analysis, and multiple testing, making it an excellent resource for statisticians and non-statisticians alike. Overall, the book is well-structured, accessible, and provides a valuable toolset for practitioners in various fields.

Discovering Statistics Using IBM SPSS Statistics: North American Edition by Andy Field is a comprehensive guide for students to learn statistics in a fun and accessible way. The fifth edition incorporates IBM SPSS Statistics© version 25, taking students from initial theory to regression, factor analysis, and multilevel modeling. The book offers a unique combination of humor and step-by-step instruction. It also provides a research roadmap to navigate projects and fascinating examples throughout. The text has received positive reviews from students and professors alike for its captivating writing style and practical guides for reporting results and findings. Overall, this book is an excellent resource for those who want to learn statistics in an engaging way.

This book, "Excel 2023: The Easiest Way to Master Microsoft Excel in 7 Days," is a comprehensive guide for absolute beginners to learn Excel. With 200 clear illustrations and 100+ exercises, the book offers a step-by-step learning path to master Excel in just seven days. The book covers everything from setting up the first worksheet to using functions and formulas, working with tables and charts, and creating monthly budget planners. The book also provides access to 23 online Excel templates. This guide is an excellent resource for anyone looking to learn Excel quickly and efficiently.

"SQL for Data Analysis: Advanced Techniques for Transforming Data into Insights" is a practical book that provides new and hidden ways to improve your SQL skills, solve problems, and make the most of SQL as part of your workflow. It covers both common and exotic SQL functions such as joins, window functions, subqueries, and regular expressions in new, innovative ways. The author, Cathy Tanimura, has over 20 years of experience analyzing data with SQL across most of the major proprietary and open source databases. This book is a must-have reference for anyone who works with SQL databases.