The 80 Best Data Science Books You Should Read in 2023

Data science is an exciting, rapidly-evolving field that combines math, statistics, programming, and business skills to extract insights from data. If you‘re looking to break into data science, expand your knowledge, or stay up-to-date on the latest techniques, you‘ll need to do a lot of reading and learning.

To help you out, I‘ve compiled this list of the 80 best data science books that are worth your time. I‘ve categorized them into the core skills every data scientist needs:

• Math and Statistics Foundations
• Programming Languages (Python, R, SQL)
• Machine Learning
• Data Mining, Cleaning and Visualization
• Specialized Skills and Real-World Applications
• Communication, Leadership and Interview Prep

Whether you‘re a complete beginner, aspiring data scientist looking to land your first job, or experienced practitioner wanting to specialize in areas like deep learning, NLP or big data, there are books on this list for you. I‘ve included detailed descriptions of what you‘ll learn from each book so you can choose the ones that best fit your current skills and goals.

But first, let‘s look at my top 10 must-read data science books for 2023:

The Top 10 Essential Data Science Books

1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron

If you only read one book on machine learning and modern deep learning, make it this one. Newly updated for TensorFlow 2, this bestseller provides clear explanations, engaging examples and practical coding tutorials in Python. You‘ll learn ML fundamentals and build real models from start to finish.

2. Python for Data Analysis by Wes McKinney

Wes McKinney, creator of the hugely popular Pandas data analysis library for Python, teaches you how to effectively use tools like NumPy, Matplotlib, Pandas and Jupyter Notebooks to wrangle, explore and make sense of real-world datasets. A must-read for learning Python‘s leading data science stack.

3. Data Science from Scratch by Joel Grus

If you have some basic Python skills and want to understand data science concepts from first principles, this is the book for you. Grus walks you through the fundamentals of linear algebra, statistics, and probability before diving into practical examples of data visualization, machine learning algorithms, and more — all from scratch, without any black box libraries.

4. An Introduction to Statistical Learning by James, Witten, Hastie and Tibshirani

This book provides a fantastic introduction to essential statistical learning concepts like regression, classification, resampling methods, regularization and more, with great intuitive explanations and examples in R. It strikes just the right balance between theory and practice for readers with some statistics background.

5. Deep Learning with Python by François Chollet

Deep learning is driving breakthroughs in areas like computer vision, NLP, and more. This clear guide from the creator of the Keras library helps you understand key concepts and start building your own neural networks for practical applications with Python code. You‘ll appreciate the intuitive explanations, crisp illustrations, and realistic examples.

6. Storytelling with Data by Cole Nussbaumer Knaflic

Data scientists need to be great communicators who can tell compelling stories to drive decisions. This highly visual book breaks down the fundamentals of data visualization and presentation with tons of real examples. You‘ll learn how to craft narratives, choose the right visuals, and design slides that inform and influence.

7. The Art of Statistics by David Spiegelhalter

Statistics comes alive in this fascinating book on how we can derive knowledge from data. Spiegelhalter uses real-world examples, from the COVID pandemic to media misrepresentations, to teach key statistical concepts and show their immense power (and limitations) in our data-driven society. An enlightening read.

8. Causal Inference: The Mixtape by Scott Cunningham

Understanding true causal relationships from observational data is a huge challenge. This rigorous book covers a wide range of causal inference methods, from regression discontinuity to instrumental variables, in an engaging way filled with entertaining examples, metaphors and Pearl Jam references.

9. Naked Statistics by Charles Wheelan

If you find statistics intimidating or dull, this bestseller is for you. Wheelan strips away the complexity to reveal the beauty and power of statistical concepts in a fun, breezy style that anyone can understand. You‘ll be amazed at how integral statistics are to fields from insurance to medicine.

10. Designing Data-Intensive Applications by Martin Kleppmann

To work with big data, you need to understand key architecture concepts behind databases, streams, caches, indexes and more. This masterful work clearly explains the practical tradeoffs behind the major technologies for storing and processing data at scale. A deep but rewarding read for data engineers.

Now let‘s dive deeper into each skill area, starting with foundational math and stats skills:

Math and Statistics Foundations

11. Introduction to Linear Algebra by Gilbert Strang
12. The Elements of Statistical Learning by Hastie, Tibshirani, Friedman
13. Pattern Recognition and Machine Learning by Christopher Bishop
14. Think Stats by Allen Downey
15. Statistical Inference by Casella & Berger
16. Bayesian Methods for Hackers by Cameron Davidson-Pilon
17. Probabilistic Programming and Bayesian Methods for Hackers by Davidson-Pilon
18. All of Statistics by Larry Wasserman
19. Computer Age Statistical Inference by Bradley Efron & Trevor Hastie
20. Foundations of Machine Learning by Mohri, Rostamizadeh, Talwalkar

Whether you‘re brushing up on linear algebra, diving into probabilistic programming, or getting a broad survey of ML foundations, these books provide rigorous, in-depth introductions to the math and stats concepts that form the bedrock of data science.

Programming Languages: Python, R and SQL

21. Python for Data Analysis by Wes McKinney
22. R for Data Science by Hadley Wickham
23. Advanced R by Hadley Wickham
24. R Packages by Hadley Wickham
25. The Art of R Programming by Norman Matloff
26. Fluent Python by Luciano Ramalho
27. Effective Computation in Physics by Anthony Scopatz & Kathryn Huff
28. Python Machine Learning by Sebastian Raschka
29. Data Science from Scratch by Joel Grus
30. Python Data Science Handbook by Jake VanderPlas
31. SQL Queries for Mere Mortals by John L. Viescas
32. SQL Cookbook by Anthony Molinaro
33. Learning SQL by Alan Beaulieu
34. Practical SQL by Anthony DeBarros

Proficiency in languages like Python, R and SQL is essential for any data scientist. These guides will take your skills to the next level, whether you‘re manipulating dataframes with Pandas and dplyr, optimizing SQL queries, or building machine learning models. The Wickham trilogy is a must for serious R users, while Python-focused titles by Grus, VanderPlas and Raschka are prime resources.

Machine Learning

35. The Hundred Page Machine Learning Book by Andriy Burkov
36. Machine Learning with R by Brett Lantz
37. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
38. Machine Learning Engineering by Andriy Burkov
39. Foundations of Machine Learning by Mohri, Rostamizadeh, Talwalkar
40. Real-World Machine Learning by Henrik Brink, Joseph Richards, Mark Fetherolf
41. Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville
42. Machine Learning: A Probabilistic Perspective by Kevin Murphy
43. Natural Language Processing with Python by Bird, Klein & Loper

Learn fundamental ML concepts, key algorithms, model evaluation techniques and more through clear code examples, intuitive explanations and real datasets. Hands-on guides like Lantz make it easy to get started with practical tutorials in R or Python, while theoretical works by Mohri and Murphy provide the depth to understand the mathematical underpinnings. Burkov‘s pair of books are excellent intros to both general ML and deploying ML systems, while the Deep Learning bible by Goodfellow, et al. is essential for mastering neural networks.

Data Mining, Cleaning and Visualization

44. Python for Data Analysis by Wes McKinney
45. Storytelling with Data by Cole Nussbaumer Knaflic
46. Doing Data Science by Cathy O‘Neil and Rachel Schutt
47. Data Smart by John Foreman
48. Automate the Boring Stuff with Python by Al Sweigart
49. Algorithms of the Intelligent Web by Haralambos Marmanis & Dmitry Babenko
50. Web Scraping with Python by Ryan Mitchell
51. Mastering regular expressions by Jeffrey E.F. Friedl
52. Visualize This by Nathan Yau
53. Data Visualization with Python and JavaScript by Kyran Dale
54. D3.js in Action by Elijah Meeks
55. Fundamentals of Data Visualization by Claus Wilke
56. OpenIntro Statistics by David Diez, Christopher Barr, et al.

Extracting insights from raw data requires skills in acquisition, cleaning, exploration, feature engineering and visualization. These practical guides walk you through key data wrangling tasks in Python and R, from web scraping and regex to handling missing values. You‘ll also learn to create beautiful, effective visualizations using tools like Matplotlib, Seaborn, D3 and more. Storytelling with Data is a gem on presentation, while OpenIntro provides a free intro to statistical thinking with great examples.

Specialized Skills and Real-World Applications

57. Deep Learning with Python by François Chollet
58. Grokking Deep Learning by Andrew Trask
59. Dive into Deep Learning by Zhang, Lipton, et al.
60. Spark: The Definitive Guide by Bill Chambers & Matei Zaharia
61. Advanced Analytics with Spark by Sandy Ryza, et al.
62. Graph Algorithms by Amy Hodler & Mark Needham
63. Causal Inference in Statistics by Judea Pearl & Madelyn Glymour
64. Causal Inference: The Mixtape by Scott Cunningham
65. Doing Bayesian Analysis by John Kruschke
66. Reinforcement Learning: An Introduction by Sutton & Barto
67. Deep Reinforcement Learning Hands-On by Maxim Lapan
68. Text Analytics with Python by Dipanjan Sarkar
69. Natural Language Processing with PyTorch by Delip Rao
70. Forecasting: Principles and Practice by Rob Hyndman & George Athana­sopou­los
71. Practical Time Series Analysis by Aileen Nielsen
72. Anomaly Detection Principles and Algorithms by Kishan Maladkar

Once you‘ve nailed the fundamentals, dive deeper into specialized techniques and applications with these advanced texts. Whether you want to master deep learning, build graph networks, run Bayesian models, detect anomalies, or forecast time series, you‘ll find focused, practical guides here. The causal inference books by Pearl and Cunningham are illuminating for understanding causality, while the Spark books are invaluable for wrangling big data. Several NLP and PyTorch deep learning resources will expand your ML toolkit.

Communication, Leadership and Interview Prep

73. Storytelling with Data by Cole Nussbaumer Knaflic
74. The Truthful Art by Alberto Cairo
75. Data Science for Business by Foster Provost & Tom Fawcett
76. The Art of Leadership by Michael Lopp
77. Thinking with Data by Max Shron
78. Behind Every Good Decision by Piyanka Jain & Puneet Sharma
79. Cracking the Data Science Interview by Maverick Lin
80. Build a Career in Data Science by Emily Robinson & Jacqueline Nolis

To truly succeed in data science, you need more than just technical chops. These books teach you to communicate insights effectively, develop a data-driven mindset, lead data science teams, and ace the interview. Highlights include the beautiful Truthful Art on dataviz best practices, the practical Data Science for Business on using ML to solve problems, and two essential guides on breaking into data science.

Well, there you have it — an eclectic mix of my 80 top picks for data science books worth your precious reading time. Of course, there are many other great books out there, but I think this selection covers most of the critical bases you need to master data science, from foundations to advanced applications.

Happy reading and learning! I hope these recommendations help accelerate your data science journey, as they certainly did for mine. The more you read, tinker with examples, and apply your skills to real datasets, the faster you‘ll grow into a data science pro. Cheers!