Welcome to our post on enjoymachinelearning.com’s favorite data science books.
This post features a proprietary EML ranking system that will give you a way to be confident when making a purchase.
When I started with data science, I couldn’t stand how there seemed to be thousands of books but nothing that told me which was better.
We compiled a ton of data and cooked up a rating formula that gives you the best information available to make a purchase!
Table of Contents
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Top 5 Highest Rated Data Science Books On The Web (Ranked)
The Internets Top Data Science Books Ranked
| Title | Author | EML RATING |
|---|---|---|
| Invisible Women: Data Bias in a World Designed for Men | Caroline Criado Perez | 95.68 |
| Python: - The Bible- 3 Manuscripts in 1 book: -Python Programming For Beginners -Python Programming For | Maurice J. Thompson | 95.47 |
| Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build | Aurélien Géron | 97.54 |
| Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems | Martin Kleppmann | 97.43 |
| The Art of Statistics: How to Learn from Data | David Spiegelhalter | 95.44 |
| Python for Everybody: Exploring Data in Python 3 | Dr. Charles Russell Severance, Sue B | 94.55 |
| Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are | Seth Stephens-Davidowitz | 95.11 |
| Naked Statistics: Stripping the Dread from the Data | Charles Wheelan, Jonathan Davis | 95.07 |
| Deep Learning (Adaptive Computation and Machine Learning series) | Goodfellow , Yoshua Bengio | 94.7 |
| A Thousand Brains: A New Theory of Intelligence | Jeff Hawkins, Richard Dawkins | 93.77 |
| R for Data Science: Import, Tidy, Transform, Visualize, and Model Data | Garrett Grolemund and Hadley Wickham | 94.07 |
| Deep Learning with Python | Francois Chollet | 94.06 |
| Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning from Scratch) | Oliver Theobal | 93.69 |
| Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking | Foster Provost and Tom Fawcett | 93.41 |
| The Hundred-Page Machine Learning Book | Andriy Burkov | 93.11 |
| Approaching (Almost) Any Machine Learning Problem | Abhishek Thakur | 92.6 |
| Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python | Peter Bruce, Andrew Bruce | 92.6 |
| The Data Detective: Ten Easy Rules to Make Sense of Statistics | Tim Harford and Penguin Audio | 92.48 |
| Data Science from Scratch: First Principles with Python | Joel Grus | 92.46 |
| Python Data Science Handbook: Essential Tools for Working with Data | Jake VanderPlas | 92.44 |
| You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder | Janelle Shane | 92.16 |
| Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street | Nick Singh and Kevin Huo | 92.38 |
| Mathematics for Machine Learning | Marc Peter | 92.38 |
| Introduction to Machine Learning with Python: A Guide for Data Scientists | Andreas Müller and Sarah Guido | 92.35 |
| DataStory: Explain Data and Inspire Action Through Story | Nancy Duarte | 92.14 |
| Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD | Jeremy Howard and Sylvain Gugger | 93.07 |
| Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 | Sebastian Raschka and Vahid Mirjalili | 91.6 |
| Data Science (The MIT Press Essential Knowledge series) | John D. Kelleher and Brendan Tierney | 92.88 |
| Python Programming for Beginners: The #1 Python Programming Crash Course for Beginners to Learn Pytho | Codeone Publishing | 91.53 |
| Machine Learning: 4 Books in 1: The #1 Beginner's Guide to Master the Basics of Python Programming | Andrew Park | 91.5 |
| Artificial Intelligence: A Modern Approach (Pearson Series in Artifical Intelligence) | Peter Norvig | 90.97 |
| Data Smart: Using Data Science to Transform Information into Insight | John W. Foreman | 91.41 |
| Spark: The Definitive Guide: Big Data Processing Made Simple | Bill Chambers and Matei Zaharia | 91.4 |
| Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) | Kevin P. Murphy | 91.23 |
| Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, | Sebastian Raschka and Vahid Mirjalil | 90.95 |
| Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython | Wes McKinney | 89.71 |
| Business Intelligence, Analytics, and Data Science: A Managerial Perspective | Ramesh Sharda, Dursun Dele | 90.9 |
| Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps | Valliappa Lakshmanan , Sara Robinson | 90.69 |
| Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative | Stefan Jansen | 90.21 |
| The StatQuest Illustrated Guide To Machine Learning | Josh Starmer PhD | 91.87 |
| Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks | Jonathan Schwabish | 90.67 |
| Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control | Steven L. Brunton and J. Nathan Kutz | 90.58 |
| SQL for Data Analytics: Perform fast and efficient data analysis with the power of SQL | Upom Malik, Matt Goldwasser | 90.56 |
| Qualitative Data Analysis: A Methods Sourcebook | Matthew B. Miles, A. Michael Huberman | 91.45 |
| The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do | Erik J. Larson, Perry Daniels, et al. | 90.03 |
| Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence (Addison-Wesley Data & Analytics | Grant Beyleveld | 90.24 |
| Introduction to Statistics: An Intuitive Guide for Analyzing Data and Unlocking Discoveries | Jim Frost | 90.22 |
| Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data | EMC Education Services | 90.16 |
| Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit | Steven Bird , Ewan Klein | 90.13 |
| Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning | Chris Albon | 90.06 |
| Doing Data Science: Straight Talk from the Frontline | Cathy O'Neil | 90.04 |
| Becoming a Data Head: How to Think, Speak and Understand Data Science, Statistics and Machine Learning | Alex J. Gutman and Jordan Goldmeier | 90.03 |
| Learning R: A Step-by-Step Function Guide to Data Analysis | Richard cotton | 89.79 |
| Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete | Maxim Lapan | 89.25 |
| Machine Learning Pocket Reference: Working with Structured Data in Python | Matt harison | 89.69 |
| Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems | Sowmya Vajjala , Bodhisattwa | 89.6 |
| Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines | Chris Fregly and Antje Barth | 89.56 |
| Grokking Deep Learning | Andrew Trask | 89.56 |
| Text Mining with R: A Tidy Approach | Julia Silge and David Robinson | 89.34 |
| Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python | Sebastian Raschka , Yuxi (Hayden) Liu | 89.4 |
| Statistics: The Art and Science of Learning from Data | Alan Agresti , Christine Franklin | 89.37 |
| Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 | Antonio Gulli , Amita Kapoor | 88.84 |
| Natural Language Processing in Action: Understanding, Analyzing, and Generating Text with Python | Hobson Lane, Hannes Hapke | 88.81 |
| Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib | Robert Johansson | 89.17 |
| Effective Pandas: Patterns for Data Manipulation (Treading on Python) | Matt harison | 89.11 |
| Real World AI: A Practical Guide for Responsible Machine Learning | Alyssa Simpson Rochwerger and Wilson Pang | 89.11 |
| Introduction to Computation and Programming Using Python, third edition: With Application to Computational | John V | 89.06 |
| Getting Started with Data Science: Making Sense of Data with Analytics (IBM Press) | Murtaza Haider | 88.94 |
| Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studie | John D. Kelleher | 88.92 |
| Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using | Paul Crickard | 87.89 |
| Natural Language Processing with Transformers: Building Language Applications with Hugging Face | Lewis Tunstall , Leandro von | 88.88 |
| Pattern Recognition and Machine Learning (Information Science and Statistics) | Christopher M. Bishop | 87.89 |
| Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms | Nikhil Buduma and Nicholas Lacascio | 87.84 |
| Build a Career in Data Science | Emily Robinson, Jacqueline | 88.75 |
| Deep Learning: A Visual Approach | Andrew Glassne | 88.71 |
| Malware Data Science: Attack Detection and Attribution | Joshua Saxe and Hillary Sanders | 88.71 |
| Data Mesh: Delivering Data-Driven Value at Scale | Zhamak Dehghani | 88.68 |
| Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications | Chip Huyen | 88.61 |
| Deep Learning from Scratch: Building with Python from First Principles | Seth Weidman | 88.51 |
| Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning | Benjamin Bengfort | 88.28 |
| Deep Learning: A Practitioner's Approach | Josh Patterson , Mike Loukides, | 87.41 |
| Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit- | Yuxi (Hayden) Liu | 88.16 |
| The Deep Learning Revolution | Terrence J. Sejnowski, Shawn Compton, | 87.25 |
| The Kaggle Book: Data analysis and machine learning for competitive data science | Konrad Banachewicz , Luca Massaron | 88.1 |
| Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and | Stefanie Molin and Ken Jee | 88.07 |
| Representation Learning for Natural Language Processing | Zhiyuan Liu, Yankai Lin | 87.71 |
| Introduction to Machine Learning, fourth edition (Adaptive Computation and Machine Learning series) | Ethem Alpaydin | 88.93 |
| Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, | Rowel Atienza | 88 |
| Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing | Magnus Ekman | 88 |
| Text Analytics with Python: A Practitioner's Guide to Natural Language Processing | Dipanjan Sarkar | 87.55 |
| Advanced Natural Language Processing with TensorFlow 2: Build effective real-world NLP applications using NER, RNN | Ashish Bansal | 87.66 |
| Practical Machine Learning in R | Fred Nwanganga and Mike Chapple | 87.83 |
| Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications | Ian Pointer | 86.93 |
| Python Machine Learning for Beginners: Learning from scratch NumPy, Pandas, Matplotlib, Seaborn, Scikitlearn And More | AI Publishing | 87.41 |
| Deep Learning with Keras: Implementing deep learning models and neural networks with the power of Python | Antonio Gulli and Sujit Pal | 86.01 |
| Data Science Interview: Prep for SQL, Panda, Python, R Language, Machine Learning, DBMS and RDBMS – And Mor | DSI ACE PREP | 88.54 |
| Modern Computer Vision with PyTorch: Explore deep learning concepts and implement over 50 real-world | V Kishore Ayyadevara and Yeshwanth Reddy | 87.61 |
| Grokking Deep Reinforcement Learning | Miguel Morales | 87.52 |
| Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning | Delip Rao and Brian McMahan | 86.61 |
| Cracking the Data Science Interview: 101+ Data Science Questions & Solutions | Maverick Lin | 85.75 |
| Foundations of Data Science | Avrim Blum, John Hopcroft, | 87.4 |
| The Art of Data Science | Roger Peng and Elizabeth Matsu | 87.4 |
| Programming Machine Learning: From Coding to Deep Learning | Paolo Perrotta | 88.12 |
| Introduction to Natural Language Processing (Adaptive Computation and Machine Learning series) | Jacob Einstein | 87 |
| Building Chatbots with Python: Using Natural Language Processing and Machine Learning | Sumit Raj | 86.27 |
| SQL for Data Analysis: Advanced Techniques for Transforming Data into Insights | Cathy Tanimura | 87.84 |
| Data Engineering with AWS: Learn how to design and build cloud-based data transformation pipelines using | Gareth Eagar | 87 |
| Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine Learning | Mark Girolami, Zhi-Hua Zhou, Haiping Lu, Konstantinos N. Plataniotis | 87.78 |
| Natural Language Processing with Python and spaCy: A Practical Introduction | Yuli Vasiliev | 86.23 |
| Python Natural Language Processing Cookbook: Over 50 recipes to understand, analyze, and generate text for | Zhenya Anti | 86.18 |
| R Programming for Beginners: An Introduction to Learn R Programming with Tutorials and Hands-On Examples | Nathan Metzler | 86.95 |
| Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis | Ethan Bueno de Mesquita and Anthony | 85.97 |
| Advanced Deep Learning with Python: Design and implement advanced next-generation AI solutions using | Ivan Vasilev | 86.65 |
| How to Lead in Data Science | Jike Chong and Yue Cathy Ch | 87.39 |
| Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering, and transforming features to | Soledad Galli | 85.91 |
| Deep Learning with JavaScript: Neural networks in TensorFlow.js | Shanqing Cai , Stan Bileschi | 87.31 |
| Probability and Statistics for Data Science: Math + R + Data (Chapman & Hall/CRC Data Science Series) | Norman Matloff | 85.85 |
| Matplotlib for Python Developers | Sandro Tos | 84.92 |
| Transformers for Natural Language Processing: Build, train, and fine-tune deep neural network architectures for NLP | Denis Rothman and Antonio | 86.51 |
| Cleaning Data for Effective Data Science: Doing the other 80% of the work with Python, R, and command-line tools | David Mertz | 87.16 |
| The TensorFlow Workshop: A hands-on guide to building deep learning models from scratch using real-world | Matthew Moocarme, Anthony S | 86.37 |
| The ABCs of Data Science: By Real Data Scientists, For Future Data Scientists (Very Young Professionals) | Rikin Mathur, Varun Bhartia | 86.29 |
| Blueprints for Text Analytics Using Python: Machine Learning-Based Solutions for Common Real World (NLP) | Jens Albrecht , Sidharth Ramachandran | 86.9 |
| Python 3 Image Processing: Learn Image Processing with Python 3, NumPy, Matplotlib, and Scikit-image | Ashwin Pajankar | 85.52 |
| Data Science Programming All-in-One For Dummies | John Paul Mueller and Luca Massaron | 86.81 |
| Analytical Skills for AI and Data Science: Building Skills for an AI-Driven Enterprise | Daniel Vaughan | 86.04 |
| Murach's Python for Data Analysis (Training & Reference) | Scott Mccoy | 85.75 |
| Exploring GPT-3: An unofficial first look at the general-purpose language processing API from OpenAI | Steve Tingiris and Bret Kinsella | 85.65 |
| Hands-On Data Science with Anaconda: Utilize the right mix of tools to create high-performance data science | Dr. Yuxing Yan and James Yan | 83.77 |
| Mastering Transformers: Build state-of-the-art models from scratch with advanced natural language processing | Sava? Y?ld?r?m (Author), Meysam Asgari-Chenaghlu (Author) | 85.52 |
| Data Science at the Command Line: Obtain, Scrub, Explore, and Model Data with Unix Power Tools | Jeroen Janssens | 85.54 |
| End-to-End Data Science with SAS®: A Hands-On Programming Guide | James Gearheart | 85.54 |
| Minding the Machines: Building and Leading Data Science and Analytics Teams | Jeremy Adamson | 85.54 |
| NumPy Cookbook | Ivan Idris | 84.06 |
| The Python Bible Volume 3: Data Science (Numpy, Matplotlib, Pandas) | Florian Dedov | 84.92 |
| Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics | Thomas Nield | 85.29 |
| Essential PySpark for Scalable Data Analytics: A beginner's guide to harnessing the power and ease of PySpark 3 | Sreeram Nudurupati | 85.29 |
| Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax | Emily M. Bender | 85.06 |
| Practical MATLAB Deep Learning: A Project-Based Approach | Michael Paluszek and Stephanie Thoma | 84.46 |
| Applied Natural Language Processing in the Enterprise: Teaching Machines to Read, Write, and Understand | Ankur A. Patel and Ajay Uppili | 84.32 |
| Quantum Machine Learning with Python: Using Cirq from Google Research and IBM Qiskit | Santanu Pattanayak | 83.78 |
| Building Big Data Pipelines with Apache Beam: Use a single programming model for both batch and stream data | Jan Lukavsky | 84.17 |
| Executive Data Science | Roger Peng | 84.17 |
| Roman's Data Science: How to monetize your data | Roman Zykov , Vladimir Vishvanyuk | 84.69 |
| Pandas in Action | Boris Paskhaver | 84.52 |
| Python and Matplotlib Essentials for Scientists and Engineers (Iop Concise Physics) | Matt A. Wood | 83.61 |
| The Natural Language Processing Workshop: Confidently design and build your own NLP projects with | Rohan Chopra , Aniruddha M. Godbole | 83.97 |
| TensorFlow 2.0 Computer Vision Cookbook: Implement machine learning solutions to overcome various computer | Jesús Martinez | 83.55 |
| Handbook of Univariate and Multivariate Data Analysis with IBM SPSS | Robert Ho | 83.89 |
| Data Juice: 101 Stories of How Organizations Are Squeezing Value from Available Data Assets | Douglas B. Laney | 83.01 |
| GPT-3: Building Innovative NLP Products Using Large Language Models | Sandra Kublik and Shubham Saboo | 82.41 |
| Hands-On Natural Language Processing with PyTorch 1.x: Build smart, AI-driven linguistic applications using deep | Thomas Dop | 82.41 |
| Human-Centered Data Science: An Introduction | Cecilia Aragon , Shion Guha, | 82.39 |
| Natural Language Processing: A Machine Learning Perspective | Yue Zhang and Zhiyang Teng | 82.39 |
| Machine Learning - A Journey To Deep Learning: With Exercises And Answers | Andreas Miroslaus Wichert and Luis Sa-couto | 81.05 |
| Natural Language Processing: Python and NLTK | Nitin Hardeniya, Jacob Perkins | 81.2 |
| Advanced Analytics with PySpark: Patterns for Learning from Data at Scale Using Python and Spark | Akash Tandon , Sandy Ryza | 80 |
| Python for Data Science: A Hands-On Introduction | Yuli Vasiliev | 80 |
| Data Science Projects with Python: A case study approach to gaining valuable insights from real data with machine | Stephen Klosterman | 80 |
| Data Structures the Fun Way: An Amusing Adventure with Coffee-Filled Examples | Jeremy Kubica | 80 |
| Deep Learning for Sustainable Agriculture (Cognitive Data Science in Sustainable Computing) | Ramesh Poonia, Vijander Sing | 80 |
| Getting Started with Natural Language Processing | Ekaterina Kochmar | 80 |
| Interpretable AI: Building explainable machine learning systems | Ajay Thampi | 80 |
| Natural Language Processing in Action, Second Edition | Hobson Lane and Maria Dyshel | 80 |
How we built this ranking
As an avid enjoymachinelearning.com reader, you know we don’t personally recommend too many books.
But others do.
We compiled tons of data from around the world to find 165 of the top-rated data science books currently out there.
This is compiled from ratings, reviews, purchase numbers, and visibility on a logarithmic scale with a base addition.
We hope it helps you make an informed and comfortable decision!
If you were looking for a new processor for data science, look no further!
Dylan Kaplan has years of experience as a Senior Data Scientist. He enjoys coding and teaching and has created this website to make Machine Learning accessible to everyone.
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