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Recommended Books on Mathematics and Data Science

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Recommended Books on Mathematics and Data Science

Reader's Guide

A. Machine Learning / Data Science (Beginners – Engineers)

  • Data with Dao, Programming Made Easy (数据有道，编程不难)
  • Iris Book Series ③④⑤⑦ (Mathematical Elements / Power of Matrices / Statistics Simplified / Machine Learning)
  • Math with Bad Drawings
  • Numbers Don't Lie
  • The Ten Equations That Rule the World

B. Probability / Statistics / Bayesian (Researchers – Advanced Enthusiasts)

  • Probability Theory: The Logic of Science (概率论沉思录)
  • The Mathematics of Selfishness (利己主义的数学解析)

C. Mathematical Foundations & General Knowledge (High School – Undergraduate & General Public)

  • What Is Mathematics? (什么是数学)
  • Mathematics and Life 1 / 2 / 3 (数学与生活)
  • Strange Curves, Counting Rabbits… (怪曲线、数兔子…)

D. Competitions / Problem Sets / Reference Handbooks (Students – Teachers)

  • Berkeley Problems in Mathematics (伯克利数学问题集)
  • Mathematical Handbook (数学手册)

E. History of Mathematics & Biographies & Philosophy (Those Interested in Cultural Background)

  • From Mathematics to Philosophy (从数学到哲学)
  • Logical Dilemmas: The Life and Work of Kurt Gödel (哥德尔传)
  • A Mathematician's Apology (一个数学家的辩白)

1. Iris Book Series

  • Iris Book Series 01 · Programming Made Easy
    — Dr Ginger (Jiang Weisheng). A Python introduction for those with no programming experience, covering variables, control flow, functions, and object-oriented programming.

  • Iris Book Series · Data with Dao
    Using Python as the main thread, this book introduces data acquisition, cleaning, visualization, and basic analysis workflows for complete beginners — an entry-level data science read.

  • Iris Book Series · Mathematical Elements (Volume 3)
    A quick refresher on the linear algebra, calculus, and probability and statistics needed for machine learning.

  • Iris Book Series · Power of Matrices (Volume 4)
    Focuses on linear algebra and matrix decomposition (SVD, eigenvalues, singular values) and their applications in ML.

  • Iris Book Series · Statistics Simplified (Volume 5)
    Uses intuitive examples to explain core statistical concepts including Bayes, hypothesis testing, and regression analysis.

  • Iris Book Series · Machine Learning (Volume 7)
    Threads through the iris dataset to explain common supervised and unsupervised learning algorithms and hands-on scikit-learn usage.

  • Math with Bad Drawings
    — Ben Orlin. Explains functions, statistics, and game theory using cartoons with deliberately bad drawings — lighthearted and humorous.

  • Numbers Don't Lie: 71 Things You Need to Know About the World
    Interprets global topics such as energy, population, and technology through the lens of data and orders of magnitude.

  • The Ten Equations That Rule the World and How You Can Use Them
    Ten equations from linear regression to power laws, demonstrating their predictive power in finance, social networks, and sports.

2. Competitions / Reference Books

  • Berkeley Problems in Mathematics (3rd Edition) — P. N. Sousa et al.
    A collection of classic problems from UC Berkeley entrance exams and competitions, focusing on analysis, algebra, and combinatorics; complete solutions included.

  • Mathematical Handbook — Compiled by Sichuan Mining Institute
    A formula reference book covering common algebra, trigonometry, calculus, and statistical tables.

3. Probability / Bayesian

  • Probability Theory: The Logic of Science — Edwin T. Jaynes
    Advocates unifying probability theory using maximum entropy and a Bayesian perspective; often called the "Bayesian Bible."

  • The Mathematics of Selfishness — Karl Sigmund
    An introduction to evolutionary game theory, using the Prisoner's Dilemma to explain mathematical models of cooperation and altruism.

  • Strange Curves, Counting Rabbits, & Other Mathematical Explorations
    A collection of popular mathematics essays for general audiences, covering fractals, the Fibonacci sequence, topological paradoxes, and more.

  • What Is Mathematics? — Courant & Robbins (Classic New Edition)
    A general mathematics culture book covering number theory, geometry, calculus, and the concept of infinity.

  • Mathematics and Life 1 / 2 / 3 — Tohyama Hiraku
    A classic Japanese popular science series that uses everyday scenarios to explain mathematical ideas, methods, and fascinations.

5. History of Mathematics · Biographies · Philosophy

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Recommended Books on Mathematics and Data ScienceReader's GuideA. Machine Learning / Data Science (Beginners – Engineers)B. Probability / Statistics / Bayesian (Researchers – Advanced Enthusiasts)C. Mathematical Foundations & General Knowledge (High School – Undergraduate & General Public)D. Competitions / Problem Sets / Reference Handbooks (Students – Teachers)E. History of Mathematics & Biographies & Philosophy (Those Interested in Cultural Background)1. Iris Book Series2. Competitions / Reference Books3. Probability / Bayesian4. Popular Science5. History of Mathematics · Biographies · Philosophy