165 of The Internets Top Data Science Books [Ranked]

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!

 

Top 5 Highest Rated Data Science Books On The Web (Ranked)

Invisible Women: Data Bias in a World Designed for Men
Python: - The Bible- 3 Manuscripts in 1 book: -Python Programming For Beginners -Python Programming For Intermediates -Python Programming for Advanced (Your place to learn Python with ease)
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
The Art of Statistics: How to Learn from Data
Invisible Women: Data Bias in a World Designed for Men
Python: - The Bible- 3 Manuscripts in 1 book: -Python Programming For Beginners -Python Programming For Intermediates -Python Programming for Advanced (Your place to learn Python with ease)
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
The Art of Statistics: How to Learn from Data
Invisible Women: Data Bias in a World Designed for Men
Invisible Women: Data Bias in a World Designed for Men
Python: - The Bible- 3 Manuscripts in 1 book: -Python Programming For Beginners -Python Programming For Intermediates -Python Programming for Advanced (Your place to learn Python with ease)
Python: - The Bible- 3 Manuscripts in 1 book: -Python Programming For Beginners -Python Programming For Intermediates -Python Programming for Advanced (Your place to learn Python with ease)
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
The Art of Statistics: How to Learn from Data
The Art of Statistics: How to Learn from Data


The Internets Top Data Science Books Ranked 

TitleAuthorEML RATING
Invisible Women: Data Bias in a World Designed for MenCaroline Criado Perez95.68
Python: - The Bible- 3 Manuscripts in 1 book: -Python Programming For Beginners -Python Programming ForMaurice J. Thompson95.47
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to BuildAurélien Géron97.54
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable SystemsMartin Kleppmann 97.43
The Art of Statistics: How to Learn from DataDavid Spiegelhalter95.44
Python for Everybody: Exploring Data in Python 3Dr. Charles Russell Severance, Sue B94.55
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really AreSeth Stephens-Davidowitz95.11
Naked Statistics: Stripping the Dread from the DataCharles Wheelan, Jonathan Davis95.07
Deep Learning (Adaptive Computation and Machine Learning series)Goodfellow , Yoshua Bengio 94.7
A Thousand Brains: A New Theory of IntelligenceJeff Hawkins, Richard Dawkins93.77
R for Data Science: Import, Tidy, Transform, Visualize, and Model DataGarrett Grolemund and Hadley Wickham 94.07
Deep Learning with PythonFrancois Chollet 94.06
Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning from Scratch)Oliver Theobal93.69
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic ThinkingFoster Provost and Tom Fawcett93.41
The Hundred-Page Machine Learning BookAndriy Burkov93.11
Approaching (Almost) Any Machine Learning ProblemAbhishek Thakur92.6
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and PythonPeter Bruce, Andrew Bruce92.6
The Data Detective: Ten Easy Rules to Make Sense of StatisticsTim Harford and Penguin Audio92.48
Data Science from Scratch: First Principles with PythonJoel Grus92.46
Python Data Science Handbook: Essential Tools for Working with DataJake VanderPlas92.44
You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a WeirderJanelle Shane92.16
Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall StreetNick Singh and Kevin Huo92.38
Mathematics for Machine LearningMarc Peter92.38
Introduction to Machine Learning with Python: A Guide for Data ScientistsAndreas Müller and Sarah Guido 92.35
DataStory: Explain Data and Inspire Action Through StoryNancy Duarte92.14
Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhDJeremy Howard and Sylvain Gugger93.07
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2Sebastian Raschka and Vahid Mirjalili91.6
Data Science (The MIT Press Essential Knowledge series)John D. Kelleher and Brendan Tierney92.88
Python Programming for Beginners: The #1 Python Programming Crash Course for Beginners to Learn PythoCodeone Publishing91.53
Machine Learning: 4 Books in 1: The #1 Beginner's Guide to Master the Basics of Python ProgrammingAndrew Park91.5
Artificial Intelligence: A Modern Approach (Pearson Series in Artifical Intelligence)Peter Norvig90.97
Data Smart: Using Data Science to Transform Information into InsightJohn W. Foreman91.41
Spark: The Definitive Guide: Big Data Processing Made SimpleBill Chambers and Matei Zaharia91.4
Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)Kevin P. Murphy91.23
Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn,Sebastian Raschka and Vahid Mirjalil90.95
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPythonWes McKinney 89.71
Business Intelligence, Analytics, and Data Science: A Managerial PerspectiveRamesh Sharda, Dursun Dele90.9
Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOpsValliappa Lakshmanan , Sara Robinson90.69
Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternativeStefan Jansen90.21
The StatQuest Illustrated Guide To Machine LearningJosh Starmer PhD91.87
Better Data Visualizations: A Guide for Scholars, Researchers, and WonksJonathan Schwabish 90.67
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and ControlSteven L. Brunton and J. Nathan Kutz90.58
SQL for Data Analytics: Perform fast and efficient data analysis with the power of SQLUpom Malik, Matt Goldwasser90.56
Qualitative Data Analysis: A Methods SourcebookMatthew B. Miles, A. Michael Huberman91.45
The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We DoErik J. Larson, Perry Daniels, et al.90.03
Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence (Addison-Wesley Data & AnalyticsGrant Beyleveld 90.24
Introduction to Statistics: An Intuitive Guide for Analyzing Data and Unlocking DiscoveriesJim Frost90.22
Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting DataEMC Education Services90.16
Natural Language Processing with Python: Analyzing Text with the Natural Language ToolkitSteven Bird , Ewan Klein90.13
Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep LearningChris Albon 90.06
Doing Data Science: Straight Talk from the FrontlineCathy O'Neil90.04
Becoming a Data Head: How to Think, Speak and Understand Data Science, Statistics and Machine LearningAlex J. Gutman and Jordan Goldmeier 90.03
Learning R: A Step-by-Step Function Guide to Data AnalysisRichard cotton89.79
Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discreteMaxim Lapan89.25
Machine Learning Pocket Reference: Working with Structured Data in PythonMatt harison89.69
Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP SystemsSowmya Vajjala , Bodhisattwa89.6
Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning PipelinesChris Fregly and Antje Barth89.56
Grokking Deep LearningAndrew Trask89.56
Text Mining with R: A Tidy ApproachJulia Silge and David Robinson89.34
Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with PythonSebastian Raschka , Yuxi (Hayden) Liu 89.4
Statistics: The Art and Science of Learning from DataAlan Agresti , Christine Franklin89.37
Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2Antonio Gulli , Amita Kapoor88.84
Natural Language Processing in Action: Understanding, Analyzing, and Generating Text with PythonHobson Lane, Hannes Hapke88.81
Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and MatplotlibRobert Johansson89.17
Effective Pandas: Patterns for Data Manipulation (Treading on Python)Matt harison89.11
Real World AI: A Practical Guide for Responsible Machine LearningAlyssa Simpson Rochwerger and Wilson Pang89.11
Introduction to Computation and Programming Using Python, third edition: With Application to ComputationalJohn V89.06
Getting Started with Data Science: Making Sense of Data with Analytics (IBM Press)Murtaza Haider88.94
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case StudieJohn D. Kelleher88.92
Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines usingPaul Crickard87.89
Natural Language Processing with Transformers: Building Language Applications with Hugging FaceLewis Tunstall , Leandro von88.88
Pattern Recognition and Machine Learning (Information Science and Statistics)Christopher M. Bishop87.89
Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms Nikhil Buduma and Nicholas Lacascio87.84
Build a Career in Data ScienceEmily Robinson, Jacqueline88.75
Deep Learning: A Visual ApproachAndrew Glassne88.71
Malware Data Science: Attack Detection and AttributionJoshua Saxe and Hillary Sanders88.71
Data Mesh: Delivering Data-Driven Value at ScaleZhamak Dehghani88.68
Designing Machine Learning Systems: An Iterative Process for Production-Ready ApplicationsChip Huyen88.61
Deep Learning from Scratch: Building with Python from First PrinciplesSeth Weidman88.51
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine LearningBenjamin Bengfort88.28
Deep Learning: A Practitioner's ApproachJosh 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 RevolutionTerrence J. Sejnowski, Shawn Compton,87.25
The Kaggle Book: Data analysis and machine learning for competitive data scienceKonrad Banachewicz , Luca Massaron88.1
Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, andStefanie Molin and Ken Jee88.07
Representation Learning for Natural Language ProcessingZhiyuan Liu, Yankai Lin87.71
Introduction to Machine Learning, fourth edition (Adaptive Computation and Machine Learning series)Ethem Alpaydin88.93
Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning,Rowel Atienza88
Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language ProcessingMagnus Ekman 88
Text Analytics with Python: A Practitioner's Guide to Natural Language ProcessingDipanjan Sarkar87.55
Advanced Natural Language Processing with TensorFlow 2: Build effective real-world NLP applications using NER, RNNAshish Bansal87.66
Practical Machine Learning in RFred Nwanganga and Mike Chapple 87.83
Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning ApplicationsIan Pointer86.93
Python Machine Learning for Beginners: Learning from scratch NumPy, Pandas, Matplotlib, Seaborn, Scikitlearn And MoreAI Publishing87.41
Deep Learning with Keras: Implementing deep learning models and neural networks with the power of PythonAntonio Gulli and Sujit Pal86.01
Data Science Interview: Prep for SQL, Panda, Python, R Language, Machine Learning, DBMS and RDBMS – And MorDSI ACE PREP88.54
Modern Computer Vision with PyTorch: Explore deep learning concepts and implement over 50 real-worldV Kishore Ayyadevara and Yeshwanth Reddy87.61
Grokking Deep Reinforcement LearningMiguel Morales 87.52
Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep LearningDelip Rao and Brian McMahan86.61
Cracking the Data Science Interview: 101+ Data Science Questions & SolutionsMaverick Lin85.75
Foundations of Data ScienceAvrim Blum, John Hopcroft,87.4
The Art of Data ScienceRoger Peng and Elizabeth Matsu87.4
Programming Machine Learning: From Coding to Deep LearningPaolo Perrotta 88.12
Introduction to Natural Language Processing (Adaptive Computation and Machine Learning series)Jacob Einstein87
Building Chatbots with Python: Using Natural Language Processing and Machine LearningSumit Raj86.27
SQL for Data Analysis: Advanced Techniques for Transforming Data into InsightsCathy Tanimura87.84
Data Engineering with AWS: Learn how to design and build cloud-based data transformation pipelines usingGareth Eagar87
Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine LearningMark Girolami, Zhi-Hua Zhou, Haiping Lu, Konstantinos N. Plataniotis87.78
Natural Language Processing with Python and spaCy: A Practical IntroductionYuli Vasiliev86.23
Python Natural Language Processing Cookbook: Over 50 recipes to understand, analyze, and generate text forZhenya Anti86.18
R Programming for Beginners: An Introduction to Learn R Programming with Tutorials and Hands-On ExamplesNathan Metzler86.95
Thinking Clearly with Data: A Guide to Quantitative Reasoning and AnalysisEthan Bueno de Mesquita and Anthony 85.97
Advanced Deep Learning with Python: Design and implement advanced next-generation AI solutions usingIvan Vasilev 86.65
How to Lead in Data ScienceJike Chong and Yue Cathy Ch87.39
Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering, and transforming features toSoledad Galli 85.91
Deep Learning with JavaScript: Neural networks in TensorFlow.jsShanqing Cai , Stan Bileschi87.31
Probability and Statistics for Data Science: Math + R + Data (Chapman & Hall/CRC Data Science Series)Norman Matloff 85.85
Matplotlib for Python DevelopersSandro Tos84.92
Transformers for Natural Language Processing: Build, train, and fine-tune deep neural network architectures for NLPDenis Rothman and Antonio86.51
Cleaning Data for Effective Data Science: Doing the other 80% of the work with Python, R, and command-line toolsDavid Mertz87.16
The TensorFlow Workshop: A hands-on guide to building deep learning models from scratch using real-worldMatthew Moocarme, Anthony S86.37
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-imageAshwin Pajankar85.52
Data Science Programming All-in-One For DummiesJohn Paul Mueller and Luca Massaron86.81
Analytical Skills for AI and Data Science: Building Skills for an AI-Driven EnterpriseDaniel Vaughan86.04
Murach's Python for Data Analysis (Training & Reference)Scott Mccoy85.75
Exploring GPT-3: An unofficial first look at the general-purpose language processing API from OpenAISteve Tingiris and Bret Kinsella85.65
Hands-On Data Science with Anaconda: Utilize the right mix of tools to create high-performance data scienceDr. Yuxing Yan and James Yan 83.77
Mastering Transformers: Build state-of-the-art models from scratch with advanced natural language processingSava? 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 ToolsJeroen Janssens85.54
End-to-End Data Science with SAS®: A Hands-On Programming GuideJames Gearheart85.54
Minding the Machines: Building and Leading Data Science and Analytics TeamsJeremy Adamson85.54
NumPy CookbookIvan Idris84.06
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 StatisticsThomas Nield85.29
Essential PySpark for Scalable Data Analytics: A beginner's guide to harnessing the power and ease of PySpark 3Sreeram Nudurupati85.29
Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and SyntaxEmily M. Bender85.06
Practical MATLAB Deep Learning: A Project-Based ApproachMichael Paluszek and Stephanie Thoma84.46
Applied Natural Language Processing in the Enterprise: Teaching Machines to Read, Write, and UnderstandAnkur A. Patel and Ajay Uppili84.32
Quantum Machine Learning with Python: Using Cirq from Google Research and IBM QiskitSantanu Pattanayak 83.78
Building Big Data Pipelines with Apache Beam: Use a single programming model for both batch and stream dataJan Lukavsky84.17
Executive Data ScienceRoger Peng84.17
Roman's Data Science: How to monetize your dataRoman Zykov , Vladimir Vishvanyuk84.69
Pandas in ActionBoris Paskhaver 84.52
Python and Matplotlib Essentials for Scientists and Engineers (Iop Concise Physics)Matt A. Wood83.61
The Natural Language Processing Workshop: Confidently design and build your own NLP projects withRohan Chopra , Aniruddha M. Godbole83.97
TensorFlow 2.0 Computer Vision Cookbook: Implement machine learning solutions to overcome various computerJesús Martinez83.55
Handbook of Univariate and Multivariate Data Analysis with IBM SPSSRobert Ho83.89
Data Juice: 101 Stories of How Organizations Are Squeezing Value from Available Data AssetsDouglas B. Laney83.01
GPT-3: Building Innovative NLP Products Using Large Language ModelsSandra Kublik and Shubham Saboo82.41
Hands-On Natural Language Processing with PyTorch 1.x: Build smart, AI-driven linguistic applications using deepThomas Dop82.41
Human-Centered Data Science: An IntroductionCecilia Aragon , Shion Guha, 82.39
Natural Language Processing: A Machine Learning PerspectiveYue Zhang and Zhiyang Teng82.39
Machine Learning - A Journey To Deep Learning: With Exercises And AnswersAndreas Miroslaus Wichert and Luis Sa-couto81.05
Natural Language Processing: Python and NLTKNitin Hardeniya, Jacob Perkins81.2
Advanced Analytics with PySpark: Patterns for Learning from Data at Scale Using Python and SparkAkash Tandon , Sandy Ryza80
Python for Data Science: A Hands-On IntroductionYuli Vasiliev80
Data Science Projects with Python: A case study approach to gaining valuable insights from real data with machineStephen Klosterman80
Data Structures the Fun Way: An Amusing Adventure with Coffee-Filled ExamplesJeremy Kubica80
Deep Learning for Sustainable Agriculture (Cognitive Data Science in Sustainable Computing)Ramesh Poonia, Vijander Sing80
Getting Started with Natural Language ProcessingEkaterina Kochmar80
Interpretable AI: Building explainable machine learning systemsAjay Thampi 80
Natural Language Processing in Action, Second EditionHobson Lane and Maria Dyshel80

 

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