“What are the best books to Learn Data Science?” We looked at 153 of the top books, aggregating and ranking them so we could answer that very question!

The top 43 titles, all appearing on 3 or more “Best Data Science” book lists, are ranked below by how many times they appear. The remaining 100+ books, as well as the lists we used, are in alphabetical order on the bottom of the page.

Happy Scrolling!

Top 43 Data Science Books

43 .) Advanced Analytics with Spark: Patterns for Learning from Data at Scale

Lists It Appears On:

• Darren Wilkinson
• A.I. & Optimization
• People Maven

“In the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming.

You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—including classification, clustering, collaborative filtering, and anomaly detection—to fields such as genomics, security, and finance.

If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find the book’s patterns useful for working on your own data applications.”

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42 .) Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inferenc

Lists It Appears On:

• Big Data Made Simple
• William Chen
• A.I. & Optimization

“Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power.

Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention.

Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects.”

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41 .) Big Data at Work: Dispelling the Myths, Uncovering the Opportunities by Thomas H. Davenport

Lists It Appears On:

• Datapine
• Goodreads
• Master’s In Data Science

“When the term “big data” first came on the scene, bestselling author Tom Davenport (Competing on Analytics, Analytics at Work) thought it was just another example of technology hype. But his research in the years that followed changed his mind.

Now, in clear, conversational language, Davenport explains what big data means—and why everyone in business needs to know about it. Big Data at Work covers all the bases: what big data means from a technical, consumer, and management perspective; what its opportunities and costs are; where it can have real business impact; and which aspects of this hot topic have been oversold.”

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40 .) Building Machine Learning Systems with Python by Willi Richert

Lists It Appears On:

• Analytics Vidhya
• Big Data Made Simple
• Goodreads

”
Machine learning, the field of building systems that learn from data, is exploding on the Web and elsewhere. Python is a wonderful language in which to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation and an increasing number of machine learning libraries are developed for Python.

Building Machine Learning system with Python shows you exactly how to find patterns through raw data. The book starts by brushing up on your Python ML knowledge and introducing libraries, and then moves on to more serious projects on datasets, Modelling, Recommendations, improving recommendations through examples and sailing through sound and image processing in detail.

Using open-source tools and libraries, readers will learn how to apply methods to text, images, and sounds. You will also learn how to evaluate, compare, and choose machine learning techniques.

Written for Python programmers, Building Machine Learning Systems with Python teaches you how to use open-source libraries to solve real problems with machine learning. The book is based on real-world examples that the user can build on.

Readers will learn how to write programs that classify the quality of StackOverflow answers or whether a music file is Jazz or Metal. They will learn regression, which is demonstrated on how to recommend movies to users. Advanced topics such as topic modeling (finding a text’s most important topics), basket analysis, and cloud computing are covered as well as many other interesting aspects.

Building Machine Learning Systems with Python will give you the tools and understanding required to build your own systems, which are tailored to solve your problems.”

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39 .) D3 Tips and Tricks and Interactive Data Visualization

Lists It Appears On:

• Springboard
• William Chen
• A.I. & Optimization

“Is this book for you?

It’s not written for experts. It’s put together as a guide to get you started if you’re unsure what d3.js can do. It reads more like a story as it leads the reader through the basics of line graphs and on to discover animation, tooltips, tables, interfacing with MySQL databases via PHP, sankey diagrams, force diagrams, maps and more…

Why was D3 Tips and Tricks written?

Because in the process of learning things, it’s a great way to remember them if you write them down :-).
As a result, learning how to do cool stuff with D3 meant that I accumulated a sizeable number ways to help me out when the going got tricky. Then I realised that these could be useful for others who were trying out d3.js and who were at a similar knowledge level.
So here we are! A collection of tips and tricks for d3.js written by a noob for people who might consider that they’re in the same situation :-).

What’s in the book?

I’ve captured the appropriate code (in cool looking coloured text) and added in heaps of illustrations of what’s going on so that you will get more traction at the start of your learning process than I did.
“

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38 .) Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World by Bruce Schneier

Lists It Appears On:

• Goodreads
• 3Blades
• Data Science Central

“Your cell phone provider tracks your location and knows who’s with you. Your online and in-store purchasing patterns are recorded, and reveal if you’re unemployed, sick, or pregnant. Your e-mails and texts expose your intimate and casual friends. Google knows what you’re thinking because it saves your private searches. Facebook can determine your sexual orientation without you ever mentioning it.

The powers that surveil us do more than simply store this information. Corporations use surveillance to manipulate not only the news articles and advertisements we each see, but also the prices we’re offered. Governments use surveillance to discriminate, censor, chill free speech, and put people in danger worldwide. And both sides share this information with each other or, even worse, lose it to cybercriminals in huge data breaches.

Much of this is voluntary: we cooperate with corporate surveillance because it promises us convenience, and we submit to government surveillance because it promises us protection. The result is a mass surveillance society of our own making. But have we given up more than we’ve gained? In Data and Goliath, security expert Bruce Schneier offers another path, one that values both security and privacy. He brings his bestseller up-to-date with a new preface covering the latest developments, and then shows us exactly what we can do to reform government surveillance programs, shake up surveillance-based business models, and protect our individual privacy. You’ll never look at your phone, your computer, your credit cards, or even your car in the same way again.”

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37 .) Data Science For Dummies by Lillian Pierson

Lists It Appears On:

• 3Blades
• Forbes
• Online Book Review

“Discover how data science can help you gain in-depth insight into your business – the easy way!
Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. “

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36 .) Data Science from Scratch: First Principles with Python by Joel Grus

Lists It Appears On:

• Goodreads
• Kali Tut
• Online Book Review

“Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.

If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.”

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35 .) Lean Analytics: Use Data to Build a Better Startup Faster by A. Croll and B. Yoskovitz

Lists It Appears On:

• Hackernoon
• Datapine
• Goodreads

“Marc Andreesen once said that “”markets that don’t exist don’t care how smart you are.”” Whether you’re a startup founder trying to disrupt an industry, or an intrapreneur trying to provoke change from within, your biggest risk is building something nobody wants.

Lean Analytics can help. By measuring and analyzing as you grow, you can validate whether a problem is real, find the right customers, and decide what to build, how to monetize it, and how to spread the word. Focusing on the One Metric That Matters to your business right now gives you the focus you need to move ahead–and the discipline to know when to change course.

Written by Alistair Croll (Coradiant, CloudOps, Startupfest) and Ben Yoskovitz (Year One Labs, GoInstant), the book lays out practical, proven steps to take your startup from initial idea to product/market fit and beyond. Packed with over 30 case studies, and based on a year of interviews with over a hundred founders and investors, the book is an invaluable, practical guide for Lean Startup practitioners everywhere.”

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34 .) Learning Spark: Lightning-Fast Big Data Analysis

Lists It Appears On:

• Darren Wilkinson
• A.I. & Optimization
• People Maven

“Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates.

Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You’ll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.”

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33 .) Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More by Matthew A. Russell

Lists It Appears On:

• Big Data Made Simple
• Goodreads
• A.I. & Optimization

Want to tap the tremendous amount of valuable social data in Facebook, Twitter, LinkedIn, GitHub, Instagram, and Google+? This new edition helps you discover who’s making connections with social media, what they’re talking about, and where they’re located. You’ll learn how to combine social web data, analysis techniques, and visualization to find what you’ve been looking for in the social haystack—as well as useful information you didn’t know existed.

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32 .) Natural Language Processing with Python by Steven Bird

Lists It Appears On:

• A.I. & Optimization
• Big Data Made Simple
• Goodreads

This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you’ll learn how to write Python programs that work with large collections of unstructured text. You’ll access richly annotated datasets using a comprehensive range of linguistic data structures, and you’ll understand the main algorithms for analyzing the content and structure of written communication.

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31 .) Privacy in the Age of Big Data by Theresa M. Payton and Ted Claypoole

Lists It Appears On:

• 3Blades
• Springboard
• Master’s In Data Science

“Digital data collection and surveillance gets more pervasive and invasive by the day; but the best ways to protect yourself and your data are all steps you can take yourself. The devices we use to get just-in-time coupons, directions when we’re lost, and maintain connections with loved ones no matter how far away they are, also invade our privacy in ways we might not even be aware of. Our devices send and collect data about us whenever we use them, but that data is not safeguarded the way we assume it would be.

Privacy is complex and personal. Many of us do not know the full extent to which data is collected, stored, aggregated, and used. As recent revelations indicate, we are subject to a level of data collection and surveillance never before imaginable. While some of these methods may, in fact, protect us and provide us with information and services we deem to be helpful and desired, others can turn out to be insidious and over-arching.

Privacy in the Age of Big Data highlights the many positive outcomes of digital surveillance and data collection while also outlining those forms of data collection to which we may not consent, and of which we are likely unaware. Payton and Claypoole skillfully introduce readers to the many ways we are ‘watched,’ and how to adjust our behaviors and activities to recapture our privacy. The authors suggest the tools, behavior changes, and political actions we can take to regain data and identity security. Anyone who uses digital devices will want to read this book for its clear and no-nonsense approach to the world of big data and what it means for all of us.”

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30 .) R Graphics Cookbook by Winston Chang

Lists It Appears On:

• 3Blades
• A.I. & Optimization
• Analytics Vidhya

“This practical guide provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.

Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you’re ready to get started.”

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29 .) The Art of Data Science: A Guide for Anyone Who Works with Data by Roger D. Peng

Lists It Appears On:

• A.I. & Optimization
• William Chen
• Goodreads

This book describes, simply and in general terms, the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

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28 .) The Data Science Handbook

Lists It Appears On:

• Springboard
• Towards Data Science
• A.I. & Optimization

The Data Science Handbook contains interviews with 25 of the world s best data scientists. We sat down with them, had in-depth conversations about their careers, personal stories, perspectives on data science and life advice. In The Data Science Handbook, you will find war stories from DJ Patil, US Chief Data Officer and one of the founders of the field. You ll learn industry veterans such as Kevin Novak and Riley Newman, who head the data science teams at Uber and Airbnb respectively. You ll also read about rising data scientists such as Clare Corthell, who crafted her own open source data science masters program. This book is perfect for aspiring or current data scientists to learn from the best. It s a reference book packed full of strategies, suggestions and recipes to launch and grow your own data science career.

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27 .) The Human Face of Big Data by Rick Smolan and Jennifer Erwitt

Lists It Appears On:

• 3Blades
• Data Science Central
• Goodreads

“The images and stories captured in The Human Face of Big Data are the result of an extraordinary artistic, technical, and logistical juggling act aimed at capturing the human face of the Big Data Revolution.

Big Data is defined as the real time collection, analyses, and visualization of vast amounts of the information. In the hands of Data Scientists this raw information is fueling a revolution which many people believe may have as big an impact on humanity going forward as the Internet has over the past two decades. Its enable us to sense, measure, and understand aspects of our existence in ways never before possible.

The Human Face of Big Data captures, in glorious photographs and moving essays, an extraordinary revolution sweeping, almost invisibly, through business, academia, government, healthcare, and everyday life. It’s already enabling us to provide a healthier life for our children. To provide our seniors with independence while keeping them safe. To help us conserve precious resources like water and energy. To alert us to tiny changes in our health, weeks or years before we develop a life-threatening illness. To peer into our own individual genetic makeup. To create new forms of life. And soon, as many predict, to re-engineer our own species. And we’ve barely scratched the surface . . .”

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26 .) The Visual Display of Quantitative Information by Edward R. Tufte

Lists It Appears On:

• 3Blades
• Goodreads
• People Maven

The classic book on statistical graphics, charts, tables. Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relational graphics, data maps, multivariate designs. Detection of graphical deception: design variation vs. data variation. Sources of deception. Aesthetics and data graphical displays. This is the second edition of The Visual Display of Quantitative Information.

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25 .) Think Bayes by Allen B. Downey

Lists It Appears On:

• A.I. & Optimization
• Big Data Made Simple
• William Chen

“If you know how to program with Python and also know a little about probability, you’re ready to tackle Bayesian statistics. With this book, you’ll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you’ll begin to apply these techniques to real-world problems.

Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this book’s computational approach helps you get a solid start.”

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24 .) Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil

Lists It Appears On:

• Bridgei2i
• Goodreads
• People Maven

“We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we get a car loan, how much we pay for health insurance—are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated.

But as Cathy O’Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination: If a poor student can’t get a loan because a lending model deems him too risky (by virtue of his zip code), he’s then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a “toxic cocktail for democracy.” Welcome to the dark side of Big Data.

Tracing the arc of a person’s life, O’Neil exposes the black box models that shape our future, both as individuals and as a society. These “weapons of math destruction” score teachers and students, sort résumés, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health.

O’Neil calls on modelers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, it’s up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change.”

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23 .) Applied Predictive Modeling by Kuhn and Johnson

Lists It Appears On:

• Analytics Vidhya
• Goodreads
• Kali Tut
• People Maven

“Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance―all of which are problems that occur frequently in practice.

The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code for each step of the process. The data sets and corresponding code are available in the book’s companion AppliedPredictiveModeling R package, which is freely available on the CRAN archive.”

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22 .) Machine Learning for Hackers by Drew Conway

Lists It Appears On:

• Analytics Vidhya
• Goodreads
• Imarticus
• 3Blades

“If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.

Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.”

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21 .) Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman and Jeff Ullman

Lists It Appears On:

• A.I. & Optimization
• Goodreads
• KD Nuggets
• William Chen

Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering.

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20 .) Naked Statistics: Stripping the Dread from the Data by Charles Wheelan

Lists It Appears On:

• Hackernoon
• People Maven
• Goodreads
• Online Book Review

“Once considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called “sexy.” From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.
For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.”

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19 .) Python Machine Learning by Sebastian Raschka

Lists It Appears On:

• Analytics Vidhya
• DeZyre
• Goodreads
• People Maven

“Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.

Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.

Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities in today’s world.

If you’ve read the first edition of this book, you’ll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You’ll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.”

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18 .) R for Data Science by Garrett Grolemund and Hadley Wickham

Lists It Appears On:

• A.I. & Optimization
• DeZyre
• William Chen
• Online Book Review

“Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.

Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way.”

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17 .) Building Data Science Teams by D.J. Patil

Lists It Appears On:

• A.I. & Optimization
• 3Blades
• Data Science Central
• Goodreads
• William Chen

As data science evolves to become a business necessity, the importance of assembling a strong and innovative data teams grows. In this in-depth report, data scientist DJ Patil explains the skills, perspectives, tools and processes that position data science teams for success.

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16 .) Data Smart: Using Data Science to Transform Information into Insight by John W. Foreman

Lists It Appears On:

• A.I. & Optimization
• Datapine
• Goodreads
• Online Book Review
• People Maven

“Data Science gets thrown around in the press like it’s magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It’s a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.

But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the “”data scientist,”” to extract this gold from your data? Nope.

Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that’s done within the familiar environment of a spreadsheet.”

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15 .) Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville

Lists It Appears On:

• Goodreads
• Kali Tut
• KD Nuggets
• William Chen
• A.I. & Optimization

“Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.”

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14 .) Practical Data Science with R by Nina Zumel and John Mount

Lists It Appears On:

• A.I. & Optimization
• Analytics Vidhya
• Data Science Central
• DeZyre
• Goodreads

“Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you’ll face as you collect, curate, and analyze the data crucial to the success of your business. You’ll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Book

Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics.

Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels.”

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13 .) The Signal and the Noise: Why So Many Predictions Fail – But Some Don’t by Nate Silver

Lists It Appears On:

• Goodreads
• Master’s In Data Science
• Springboard
• People Maven
• 3Blades

“Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. He solidified his standing as the nation’s foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of FiveThirtyEight.com.

Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future.

In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good—or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary—and dangerous—science.”

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12 .) Think Stats: Probability and Statistics for Programmers by Allen B. Downey

Lists It Appears On:

• DeZyre
• Goodreads
• William Chen
• Digital Vidya
• KD Nuggets

“If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts.”

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11 .) Hadoop, the Definitive Guide by Tom White

Lists It Appears On:

• 3Blades
• Coursera Blog
• Data Science Central
• Towards Data Science
• Goodreads
• People Maven

“Get ready to unlock the power of your data. With the fourth edition of this comprehensive guide, you’ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. This book is ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters.

Using Hadoop 2 exclusively, author Tom White presents new chapters on YARN and several Hadoop-related projects such as Parquet, Flume, Crunch, and Spark. You’ll learn about recent changes to Hadoop, and explore new case studies on Hadoop’s role in healthcare systems and genomics data processing.”

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10 .) Programming Collective Intelligence by Toby Segaran

Lists It Appears On:

• 3Blades
• Analytics Vidhya
• Big Data Made Simple
• Springboard
• People Maven
• Digital Vidya

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you’ve found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general–all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application.

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9 .) R Cookbook by Paul Teetor

Lists It Appears On:

• 3Blades
• A.I. & Optimization
• Analytics Vidhya
• Springboard
• DeZyre
• People Maven

“With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.

Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an experienced data programmer, it will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process.”

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8 .) Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic

Lists It Appears On:

• A.I. & Optimization
• Bridgei2i
• Coursera Blog
• Goodreads
• Online Book Review
• Towards Data Science

“Don’t simply show your data—tell a story with it!
Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You’ll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation.

Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don’t make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. “

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7 .) The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, Jerome Friedman

Lists It Appears On:

• KD Nuggets
• William Chen
• 3Blades
• Data Science Central
• Goodreads
• Kali Tut

“This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for “”wide” data (p bigger than n), including multiple testing and false discovery rates.”

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6 .) Big Data A Revolution That Will Transform How We Live, Work And Think by Viktor Mayer-Schonberger and Kenneth Cukier

Lists It Appears On:

• Imarticus
• 3Blades
• Data Science Central
• Springboard
• Goodreads
• Master’s In Data Science
• Datapine

“It seems like “big data” is in the news every day, as we read the latest examples of how powerful algorithms are teasing out the hidden connections between seemingly unrelated things. Whether it is used by the NSA to fight terrorism or by online retailers to predict customers’ buying patterns, big data is a revolution occurring around us, in the process of forever changing economics, science, culture, and the very way we think. But it also poses new threats, from the end of privacy as we know it to the prospect of being penalized for things we haven’t even done yet, based on big data’s ability to predict our future behavior. What we have already seen is just the tip of the iceberg.

Big Data is the first major book about this earthshaking subject, with two leading experts explaining what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards. “

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5 .) An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

Lists It Appears On:

• William Chen
• DeZyre
• Coursera Blog
• KD Nuggets
• Towards Data Science
• Goodreads
• Kali Tut
• People Maven

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

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4 .) Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking by Foster Provost and Tom Fawcett

Lists It Appears On:

• 3Blades
• Imarticus
• Datapine
• A.I. & Optimization
• Goodreads
• K2 Data Science
• Online Book Review
• People Maven
• Towards Data Science

“Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “”data-analytic thinking”” necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.

Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.”

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3 .) Doing Data Science: Straight Talk From The Frontline by Cathy O’Neil and Rachel Schutt

Lists It Appears On:

• 3Blades
• Goodreads
• Hackernoon
• DeZyre
• A.I. & Optimization
• Imarticus
• Master’s In Data Science
• People Maven
• Springboard

“Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.

In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.”

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2 .) Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel

Lists It Appears On:

• 3Blades
• Data Science Central
• Coursera Blog
• Goodreads
• K2 Data Science
• Master’s In Data Science
• People Maven
• Towards Data Science
• Datapine

“Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you’re going to click, buy, lie, or die.

Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections.

How? Prediction is powered by the world’s most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.

Predictive analytics (aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.”

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1 .) Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney

Lists It Appears On:

• Analytics Vidhya
• Big Data Made Simple
• Data Science Central
• Digital Vidya
• Goodreads
• Hackernoon
• A.I. & Optimization
• DeZyre
• People Maven

“Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.

Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.”

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The 100+ Additional Best Data Science Books

24 Best _ Book Sources/Lists