100 Best Data Mining Books of All Time

We've ranked the best data mining books using expert recommendations, sales data, and millions of reader ratings. At Shortform, we know books. Our book guides are the best in the world. Learn why.

Data Science for Business: What you need to know about data mining and data-analytic thinking book cover1

Data Science for Business: What you need to know about data mining and data-analytic thinking

Foster Provost and Tom Fawcett

5.0
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 Pr
Kirk Borne portrait Recommended by Kirk Borne.
The Hundred-Page Machine Learning Book book cover2

The Hundred-Page Machine Learning Book

Andriy Burkov

4.9
WARNING! To avoid buying counterfeit on Amazon, click on "See All Buying Options" and choose "Amazon.com" and not a third-party seller.

Concise and to the point — the book can be read during a week. During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.

Companion wiki — the book has a conti
Kirk Borne portrait Recommended by Kirk Borne.
Kirk Borne portrait
Kirk Borne

Recent top-selling books in #AI & #MachineLearning: https://t.co/Ij9I7SzR4d ————— #BigData #DataScience #DataMining #Algorithms #PredictiveAnalytics #Python ————— ...in the TOP 10: 1)The Hundred-Page ML Book: https://t.co/dQ7nP6gwP0 2)Hands-on ML with...: https://t.co/Y0Iz3GbtGP https://t.co/72rAFN1FwW [source]

An Introduction to Statistical Learning: With Applications in R book cover3

An Introduction to Statistical Learning: With Applications in R

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

4.8
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 a
Roger D. Peng portrait Recommended by Roger D. Peng.
Data Mining: Practical Machine Learning Tools and Techniques book cover4

Data Mining: Practical Machine Learning Tools and Techniques

Ian H. Witten, Eibe Frank

4.7
The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface;
The Signal and the Noise: Why So Many Predictions Fail - But Some Don't book cover5

The Signal and the Noise: Why So Many Predictions Fail - But Some Don't

Nate Silver

4.6
Preview Summary
One of Wall Street Journal's Best Ten Works of Nonfiction in 2012

New York Times Bestseller

"Not so different in spirit from the way public intellectuals like John Kenneth Galbraith once shaped discussions of economic policy and public figures like Walter Cronkite helped sway opinion on the Vietnam War…could turn out to be one of the more momentous books of the decade."
-New York Times Book Review

"Na
Bill Gates portrait Recommended by Bill Gates.
Data Smart: Using Data Science to Transform Information into Insight book cover6

Data Smart: Using Data Science to Transform Information into Insight

JOHN W. FOREMAN

4.6
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 prie
Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence book cover7

Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence

Jon Krohn, Grant Beyleveld, et al.

4.5
"The authors' clear visual style provides a comprehensive look at what's currently possible with artificial neural networks as well as a glimpse of the magic that's to come."
--Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning

Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented alg
Kirk Borne portrait Recommended by Kirk Borne.
Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing book cover8

Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing

Ron Kohavi

4.5
Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, an
Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites book cover9

Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites

Matthew A. Russell

4.5
Want to tap the tremendous amount of valuable social data in Facebook, Twitter, LinkedIn, and Google+? This refreshed 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 u
Applied Predictive Modeling book cover10

Applied Predictive Modeling

Max Kuhn and Kjell Johnson

4.5
This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic
Kirk Borne portrait Recommended by Kirk Borne.
Building Machine Learning Powered Applications: Going from Idea to Product book cover11

Building Machine Learning Powered Applications: Going from Idea to Product

Emmanuel Ameisen

4.5
Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn the tools, best practices, and challenges involved in building a real-worl
Machine Learning: 4 Books in 1: A Complete Overview for Beginners to Master the Basics of Python Programming and Understand How to Build Artificial Intelligence Through Data Science book cover12

Machine Learning: 4 Books in 1: A Complete Overview for Beginners to Master the Basics of Python Programming and Understand How to Build Artificial Intelligence Through Data Science

Ethem Mining

4.5
SQL QuickStart Guide: The Simplified Beginner's Guide to Managing, Analyzing, and Manipulating Data With SQL book cover13

SQL QuickStart Guide: The Simplified Beginner's Guide to Managing, Analyzing, and Manipulating Data With SQL

Walter Shields

4.5
SQL Made Easy- The Ultimate Step by Step Guide To Success Do you want to learn SQL programming without the complicated explanations?
Do you want to understand how to manage databases without all the confusion?
Well than, this is your go to guide to help you master SQL programming in no time!
This book breaks down the fundamentals elements that are essential to make you proficient in SQL programm
Tinyml: Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers book cover14

Tinyml: Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers

Pete Warden

4.5
Neural networks are getting smaller. Much smaller. The OK Google team, for example, has run machine learning models that are just 14 kilobytes in size--small enough to work on the digital signal processor in an Android phone. With this practical book, you'll learn about TensorFlow Lite for Microcontrollers, a miniscule machine learning library that allows you to run machine learning algorithms on
Pattern Recognition and Machine Learning book cover15

Pattern Recognition and Machine Learning

Christopher M. Bishop

4.4
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general
Hands-On Machine Learning with Microsoft Excel 2019: Build complete data analysis flows, from data collection to visualization book cover16

Hands-On Machine Learning with Microsoft Excel 2019: Build complete data analysis flows, from data collection to visualization

Julio Cesar Rodriguez Martino

4.4
Python for Data Analysis: Data Wrangling with Pandas, Numpy, and Ipython book cover17

Python for Data Analysis: Data Wrangling with Pandas, Numpy, and Ipython

Wes McKinney

4.4
Looking for complete instructions on manipulating, processing, cleaning, and crunching structured data in Python? The second edition of this hands-on guide--updated for Python 3.5 and Pandas 1.0--is packed with practical cases studies that show you how to effectively solve a broad set of data analysis problems, using Python libraries such as NumPy, pandas, matplotlib, and IPython.

Written by Wes Mc
Programming Collective Intelligence: Building Smart Web 2.0 Applications book cover18

Programming Collective Intelligence: Building Smart Web 2.0 Applications

Toby Segaran

4.4
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 da
The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care book cover19

The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care

Eric Topol

4.4
What if your cell phone could detect cancer cells circulating in your blood or warn you of an imminent heart attack? Mobile wireless digital devices, including smartphones and tablets with seemingly limitless functionality, have brought about radical changes in our lives, providing hyper-connectivity to social networks and cloud computing. But the digital world has hardly pierced the medical cocoo
Vinod Khosla portraitVinod Khosla portrait Recommended by Vinod Khosla and Vinod Khosla.
Pattern Classification book cover20

Pattern Classification

Richard O. Duda, Peter E. Hart, David G. Stork

4.4
The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and
Eric Weinstein portrait Recommended by Eric Weinstein.
Building Recommender Systems with Machine Learning and AI: Help People Discover New Products and Content with Deep Learning, Neural Networks, and Machine Learning Recommendations. book cover21

Building Recommender Systems with Machine Learning and AI: Help People Discover New Products and Content with Deep Learning, Neural Networks, and Machine Learning Recommendations.

Frank Kane

4.4
Learn how to build recommender systems from one of Amazon's pioneers in the field. Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon's personalized product recommendation technologies. You've seen automated recommendations everywhere - on Netflix's home page, on YouTube, and on Amazon as these machine learning algorithms learn about your unique
Mining of Massive Datasets book cover22

Mining of Massive Datasets

Anand Rajaraman, Jeffrey David Ullman

4.4
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 which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms auto
Database Internals: A deep-dive into how distributed data systems work book cover23

Database Internals: A deep-dive into how distributed data systems work

Alex Petrov

4.4
When it comes to choosing, using, and maintaining a database, understanding its internals is essential. But with so many distributed databases and tools available today, it’s often difficult to understand what each one offers and how they differ. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals.

Throughout the book, yo
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Google BigQuery: The Definitive Guide: Data Warehousing, Analytics, and Machine Learning at Scale

Valliappa Lakshmanan and Jordan Tigani

4.4
Work with petabyte-scale datasets while building a collaborative, agile workplace in the process. This practical book is the canonical reference to Google BigQuery, the query engine that lets you conduct interactive analysis of large datasets. BigQuery enables enterprises to efficiently store, query, ingest, and learn from their data in a convenient framework. With this book, you'll examine how to
Super Crunchers: Why Thinking-By-Numbers Is the New Way to Be Smart book cover25

Super Crunchers: Why Thinking-By-Numbers Is the New Way to Be Smart

Ian Ayres

4.4
Why would a casino try and stop you from losing? How can a mathematical formula find your future spouse? Would you know if a statistical analysis blackballed you from a job you wanted?Today, number crunching affects your life in ways you might never imagine. In this lively and groundbreaking new book, economist Ian Ayres shows how today's best and brightest organizations are analyzing massive data
R in Action book cover26

R in Action

Robert Kabacoff

4.4
Summary

R in Action is the first book to present both the R system and the use cases that make it such a compelling package for business developers. The book begins by introducing the R language, including the development environment. Focusing on practical solutions, the book also offers a crash course in practical statistics and covers elegant methods for dealing with messy and incomplete data usi
Data Mining: Introductory and Advanced Topics book cover27

Data Mining: Introductory and Advanced Topics

Margaret H. Dunham

4.4
Thorough in its coverage from basic to advanced topics, this book presents the key algorithms and techniques used in data mining. An emphasis is placed on the use of data mining concepts in real world applications with large database components. Includes unique chapters on Web mining, spatial mining, temporal mining, and prototypes and DM products. Separate case studies section highlights real wor
Big Data: Principles and best practices of scalable realtime data systems book cover28

Big Data: Principles and best practices of scalable realtime data systems

Nathan Marz, James Warren

4.4
Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. As scale and demand increase, so does Complexity. Fortunately, scalability and simplicity are not mutually exclusive—rather than using some trendy technology, a different approach is needed. Big data systems use many machines working in parallel to store
Vicki Boykis portrait Recommended by Vicki Boykis.
Data Visualization: A Practical Introduction book cover29

Data Visualization: A Practical Introduction

Kieran Healy

4.4
An accessible primer on how to create effective graphics from data

This book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effecti
Технологии анализа данных. Data Mining Visual Mining Text Mining OLAP book cover30

Технологии анализа данных. Data Mining Visual Mining Text Mining OLAP

I. I. Holod A. A. Barsegyan, M. S. Kupriyanov, V. V. Stepanenko

4.4
Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition book cover31

Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition

Brett Lantz

4.3
Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science ta
Learn T-SQL Querying: A guide to developing efficient and elegant T-SQL code book cover32

Learn T-SQL Querying: A guide to developing efficient and elegant T-SQL code

Pedro Lopes

4.3
33

The Data Science Design Manual

Steven S. Skiena

4.3
This book serves an introduction to data science, focusing on the skills and principles needed to build systems for collecting, analyzing, and interpreting data. As a discipline, data science sits at the intersection of statistics, computer science, and machine learning, but it is building a distinct heft and character of its own.

In particular, the book stresses the following basic principles as f
ABCs of Machine Learning (Tinker Toddlers) book cover34

ABCs of Machine Learning (Tinker Toddlers)

Dr. Dhoot

4.3
The Elements of Statistical Learning: Data Mining, Inference, and Prediction book cover35

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Trevor Hastie, Robert Tibshirani, Jerome Friedman

4.3

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Ma

Nassim Nicholas Taleb portrait Recommended by Nassim Nicholas Taleb.
How to Hack Like a LEGEND: A hacker’s tale breaking into a secretive offshore company (Hacking the Planet Book 7) book cover36

How to Hack Like a LEGEND: A hacker’s tale breaking into a secretive offshore company (Hacking the Planet Book 7)

Sparc FLOW

4.3
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management book cover37

Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management

Gordon S. Linoff, Michael J. A. Berry

4.3
The leading introductory book on data mining, fully updated and revised! When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition--more than 50% new and revised-- is a significant update from the previous
Kirk Borne portrait Recommended by Kirk Borne.
Practical SQL: A Beginner's Guide to Storytelling with Data book cover38

Practical SQL: A Beginner's Guide to Storytelling with Data

Anthony DeBarros

4.3
Practical SQL is an approachable and fast-paced guide to SQL (Structured Query Language), the standard programming language for defining, organizing, and exploring data in relational databases. The book focuses on using SQL to find the story your data tells, with the popular open-source database PostgreSQL and the pgAdmin interface as its primary tools.

You'll first cover the fundamentals of databa
Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery book cover39

Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery

Graham Williams

4.3
With a focus on the hands-on, end-to-end process for data mining, this book guides the reader through various capabilities of the easy-to-use, free and open source Rattle Data Mining Software built on the sophisticated R Statistical Software.
Machine Learning with Python for Everyone book cover40

Machine Learning with Python for Everyone

Mark Fenner

4.3
The Complete Beginner's Guide to Understanding and Building Machine Learning Systems with Python

Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you're an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal
Spark: The Definitive Guide: Big Data Processing Made Simple book cover41

Spark: The Definitive Guide: Big Data Processing Made Simple

Bill Chambers

4.3
Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals.

You'll explore the basic operations and common functions of Spark's st
Supercharge Power BI: Power BI Is Better When You Learn to Write DAX book cover42

Supercharge Power BI: Power BI Is Better When You Learn to Write DAX

Matt Allington

4.3
Power BI is a self-service (and enterprise) Business Intelligence (BI) tool that facilitates data acquisition, modeling, and visualization—and the skills needed to succeed with Power BI are fully transferable to Microsoft Excel. There are three learning areas required to master everything Power BI Desktop has to offer: TheM Language, The DAX Language, and analysis. Super Charge Power BI clearly ex
Introduction to Data Mining book cover43

Introduction to Data Mining

Pang-Ning Tan, Michael Steinbach, et al.

4.3
'Introduction to Data Mining' presents fundamental concepts and algorithms for those learning data mining. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, f
Kirk Borne portrait Recommended by Kirk Borne.
Machine Learning: 3 books in 1: - Hacking Tools for Computer + Hacking With Kali Linux + Python Programming- The ultimate beginners guide to improve your knowledge of programming and data science book cover44

Machine Learning: 3 books in 1: - Hacking Tools for Computer + Hacking With Kali Linux + Python Programming- The ultimate beginners guide to improve your knowledge of programming and data science

Adam Bash

4.3
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data book cover45

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

Hadley Wickham and Garrett Grolemund

4.3

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 Wic

Super Charge Power BI: Power BI Is Better When You Learn to Write DAX book cover46

Super Charge Power BI: Power BI Is Better When You Learn to Write DAX

Matt Allington

4.3
Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) book cover47

Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

Jiawei Han, Micheline Kamber, Jian Pei

4.3
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a trem
Text Mining with R: A Tidy Approach book cover48

Text Mining with R: A Tidy Approach

Julia Silge, David Robinson

4.3
Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you'll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You'll learn how tidytext and
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Microsoft Excel Data Analysis and Business Modeling

Wayne Winston

4.3
Master business modeling and analysis techniques with Microsoft Excel 2016, and transform data into bottom-line results. Written by award-winning educator Wayne Winston, this hands on, scenario-focused guide helps you use Excel's newest tools to ask the right questions and get accurate, actionable answers. This edition adds 150+ new problems with solutions, plus a chapter of basic spreadsheet mode
Neural Networks and Deep Learning book cover50

Neural Networks and Deep Learning

Charu C. Aggarwa

4.3
Neural Networks and Deep Learning is a free online book. The book will teach you about:
* Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
* Deep learning, a powerful set of techniques for learning in neural networks

Neural networks and deep learning currently provide the best solutions to many problems in image recognit
51

SQL for Data Analytics: Perform fast and efficient data analysis with the power of SQL

Upom Malik

4.3
Big Data: A Revolution That Will Transform How We Live, Work, and Think book cover52

Big Data: A Revolution That Will Transform How We Live, Work, and Think

Viktor Mayer-Schönberger, Kenneth Cukier

4.3
A revelatory exploration of the hottest trend in technology and the dramatic impact it will have on the economy, science, and society at large.

Which paint color is most likely to tell you that a used car is in good shape? How can officials identify the most dangerous New York City manholes before they explode? And how did Google searches predict the spread of the H1N1 flu outbreak?

The key to answe
Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die book cover53

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

Eric Siegel

4.3
"Mesmerizing & fascinating..." -- The Seattle Post-Intelligencer

"The Freakonomics of big data." --Stein Kretsinger, founding executive of Advertising.com

Award-winning - Used by over 30 universities - Translated into 9 languages

An introduction for everyone. In this rich, fascinating -- surprisingly accessible -- introduction, leading expert Eric Siegel reveals how predictive analytics (aka machi
The Model Thinker: What You Need to Know to Make Data Work for You book cover54

The Model Thinker: What You Need to Know to Make Data Work for You

Scott E. Page

4.3
How anyone can become a data ninja

From the stock market to genomics laboratories, census figures to marketing email blasts, we are awash with data. But as anyone who has ever opened up a spreadsheet packed with seemingly infinite lines of data knows, numbers aren't enough: we need to know how to make those numbers talk. In The Model Thinker, social scientist Scott E. Page shows us the mathematica
Macroanalysis: Digital Methods and Literary History book cover55

Macroanalysis: Digital Methods and Literary History

Matthew L. Jockers

4.3
In this volume, Matthew L. Jockers introduces readers to large-scale literary computing and the revolutionary potential of macroanalysis--a new approach to the study of the literary record designed for probing the digital-textual world as it exists today, in digital form and in large quantities. Using computational analysis to retrieve key words, phrases, and linguistic patterns across thousands o
Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists book cover56

Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists

Philipp K. Janert

4.3
Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those i
Machine Learning Essentials: Practical Guide in R book cover57

Machine Learning Essentials: Practical Guide in R

Alboukadel Kassambara

4.2
Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques.

This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring real word data sets, as well as, for building predictive models.

The main parts of the book include: A) Unsupervised learning methods, to explore and discover
Mastering Social Media Mining with Python book cover58

Mastering Social Media Mining with Python

Marco Bonzanini

4.2
Acquire and analyze data from all corners of the social web with Python About This Book Make sense of highly unstructured social media data with the help of the insightful use cases provided in this guide Use this easy-to-follow, step-by-step guide to apply analytics to complicated and messy social data This is your one-stop solution to fetching, storing, analyzing, and visualizing social media da
Statistical Analysis of Network Data with R (Use R!) book cover59

Statistical Analysis of Network Data with R (Use R!)

Eric D. Kolaczyk

4.2

Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistica

Cassandra High Availability book cover60

Cassandra High Availability

Robbie Strickland

4.2
If you are a developer or DevOps engineer who understands the basics of Cassandra and are ready to take your knowledge to the next level, then this book is for you. Perhaps you've just got your hands dirty working on a small application, but now you have a use case that demands greater scale and zero downtime. An understanding of the essentials of Cassandra is needed, including knowing how to inst
Information Visualization: Perception for Design book cover61

Information Visualization: Perception for Design

Colin Ware

4.2

"This unique and essential guide to human visual perception and related cognitive principles will enrich courses on information visualization and empower designers to see their way forward. Ware's updated review of empirical research and interface design examples will do much to accelerate innovation and adoption of information visualization."
—Ben Shneiderman, University of Maryland

"Colin Ware is

Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining book cover62

Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining

ChengXiang Zhai and Sean Massung

4.2
Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unl
Artificial Intelligence Basics: A Non-Technical Introduction book cover63

Artificial Intelligence Basics: A Non-Technical Introduction

Tom Taulli

4.2
Artificial intelligence touches nearly every part of your day. While you may initially assume that technology such as smart speakers and digital assistants are the extent of it, AI has in fact rapidly become a general-purpose technology, reverberating across industries including transportation, healthcare, financial services, and many more. In our modern era, an understanding of AI and its possibi
64

HBR Guide to Data Analytics Basics for Managers (HBR Guide Series)

Harvard Business Review

4.2
SQL Server 2019 Revealed: Including Big Data Clusters and Machine Learning book cover65

SQL Server 2019 Revealed: Including Big Data Clusters and Machine Learning

Bob Ward

4.2
Get up to speed on the game-changing developments in SQL Server 2019. No longer your grandparent's database engine, SQL Server 2019 is cutting edge with support for artificial intelligence (AI), machine learning (ML), big data performance and analysis, Java, and connections to Azure IOT Edge. This is not a book on traditional database administration and SQL. It focuses on all that is new and cutti
Python Data Analytics: With Pandas, Numpy, and Matplotlib book cover66

Python Data Analytics: With Pandas, Numpy, and Matplotlib

Fabio Nelli

4.2
Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn.
This revision is fully updated with new content on social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examp
Doing Data Science book cover67

Doing Data Science

Cathy O'Neil, Rachel Schutt

4.2
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-lon
Data Mining with Decision Trees: Theory and Applications (2nd Edition) book cover68

Data Mining with Decision Trees: Theory and Applications (2nd Edition)

Lior Rokach and Oded Z Maimon

4.2
Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.This 2nd Edition is dedicated entirely to the field of
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OCA Oracle Database SQL Exam Guide (Exam 1Z0-071) (Oracle Press)

Steve O'Hearn

4.1
Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning book cover70

Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning

Alan J. Izenman

4.1
Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened up the field of bioinformatics.

These exciting developments, which led to the introduction of many innovative statistical tools for high-dimensio
Data Analytics Made Accessible book cover71

Data Analytics Made Accessible

Anil Maheshwari

4.1
72

Learning R: A Step-By-Step Function Guide to Data Analysis

Richard Cotton

4.1
Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, you'll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts.

The second half of Learning R shows you real data analysis in action by covering everything from
Data Mining With R: Learning By Case Studies book cover73

Data Mining With R: Learning By Case Studies

Luis Torgo

4.1
The versatile capabilities and large set of add-on packages make R an excellent alternative to many existing and often expensive data mining tools. Exploring this area from the perspective of a practitioner, Data Mining with R: Learning with Case Studies uses practical examples to illustrate the power of R and data mining.



Assuming no prior knowledge of R or data mining/statistical techniques, the
Handbook of Statistical Analysis and Data Mining Applications book cover74

Handbook of Statistical Analysis and Data Mining Applications

Robert Nisbet, John Elder IV, Gary Miner

4.1
Python Machine Learning by Example book cover75

Python Machine Learning by Example

Yuxi (Hayden) Li

4.1
Take tiny steps to enter the big world of data science through this interesting guide About This Book - Learn the fundamentals of machine learning and build your own intelligent applications - Master the art of building your own machine learning systems with this example-based practical guide - Work with important classification and regression algorithms and other machine learning techniques Who T
Humanizing Big Data: Marketing at the Meeting of Data, Social Science & Consumer Insight book cover76

Humanizing Big Data: Marketing at the Meeting of Data, Social Science & Consumer Insight

Colin Strong

4.1
Between tweets, likes, comments, blogs, videos and images, today’s customer is estimated to generate 2.5 quintillion bytes of data per day. How can marketers utilize the ever-increasing amount of data to better understand and interact with their customers?

This book offers advice on how to interpret and incorporate data into an organization’s overall marketing strategy. It is designed to help marke
Practical Data Science with R Second Edition book cover77

Practical Data Science with R Second Edition

Nina Zumel

4.1
Practical Data Science with R, Second Edition is a task-based tutorial that leads readers through dozens of useful data analysis practices using the R language. By concentrating on the most important tasks you'll face on the job, this friendly guide is comfortable both for business analysts and data scientists. Because data is only useful if it can be understood, you'll also find fantastic tips fo
Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with Xlminer book cover78

Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with Xlminer

Galit Shmueli, Nitin R. Patel, et al.

4.1
Praise for the "First Edition"" full of vivid and thought-provoking anecdotes needs to be read by anyone with a serious interest in research and marketing."--"Research" magazine

"Shmueli et al. have done a wonderful job in presenting the field of data mining a welcome addition to the literature."--computingreviews.com

Incorporating a new focus on data visualization and time series forecasting, "Data
Probability and Statistics for Data Science: Math + R + Data book cover79

Probability and Statistics for Data Science: Math + R + Data

Norman Matloff

4.1
Probability and Statistics for Data Science: Math + R + Data covers "math stat"--distributions, expected value, estimation etc.--but takes the phrase "Data Science" in the title quite seriously:



* Real datasets are used extensively.



* All data analysis is supported by R coding.



* Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, li
Data Jujitsu: The Art of Turning Data into Product book cover80

Data Jujitsu: The Art of Turning Data into Product

DJ Pati

4.1
Acclaimed data scientist DJ Patil details a new approach to solving problems in Data Jujitsu.

Learn how to use a problem's "weight" against itself to:

Break down seemingly complex data problems into simplified parts
Use alternative data analysis techniques to examine them
Use human input, such as Mechanical Turk, and design tricks that enlist the help of your users to take short cuts around tough prob
Data Analytics: 3 Books in 1 - The New Ultimate Bible for Understanding & Using Data Analytics, Big Data + Data Science For Business + Data Mining book cover81

Data Analytics: 3 Books in 1 - The New Ultimate Bible for Understanding & Using Data Analytics, Big Data + Data Science For Business + Data Mining

John Harper

4.1
Pandas for Everyone: Python Data Analysis book cover82

Pandas for Everyone: Python Data Analysis

Daniel Y. Chen

4.1
The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effec
Hadoop: The Definitive Guide book cover83

Hadoop: The Definitive Guide

Tom White

4.1
Hadoop: The Definitive Guide helps you harness the power of your data. Ideal for processing large datasets, the Apache Hadoop framework is an open source implementation of the MapReduce algorithm on which Google built its empire. This comprehensive resource demonstrates how to use Hadoop to build reliable, scalable, distributed systems: programmers will find details for analyzing large datasets, a
A Programmer’s Guide to Data Mining: The Ancient Art of the Numerati book cover84

A Programmer’s Guide to Data Mining: The Ancient Art of the Numerati

Frances Buontempo

4.1
The ancient art of the Numerati provides insight into basic datamining techniques for beginners wishing to immerse themselves in the field using practical examples.
Unti Nonfiction book cover85

Unti Nonfiction

Brittany Kaiser

4.1
In this explosive memoir, a political consultant and technology whistleblower reveals the disturbing truth about the multi-billion-dollar data industry, revealing to the public how companies are getting richer using our personal information and exposing how Cambridge Analytica exploited weaknesses in privacy laws to help elect Donald Trump--and how this could easily happen again in the 2020 presid
Introduction to Statistical Relational Learning book cover86

Introduction to Statistical Relational Learning

Lise Getoor, Ben Taskar, et al.

4.1
Pragmatic AI: An Introduction to Cloud-Based Machine Learning book cover87

Pragmatic AI: An Introduction to Cloud-Based Machine Learning

Noah Gift

4.1
Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning
Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results--even if you don't have a strong background in math or data science. Gift illuminates powerful off-the-shelf
The Practitioner's Guide to Graph Data book cover88

The Practitioner's Guide to Graph Data

Denise Gosnell, Matthias Broecheler

4.1
How do you apply graph thinking to solve complex problems? With this practical guide, data scientists will learn how to think about data as a graph and determine if graph technology is right for your company. You'll learn techniques for building scalable, real-time, and multimodel architectures that solve complex problems with graph data.

Authors Denise Koessler Gosnell and Matthias Broecheler show
Data Mining Techniques in Crm: Inside Customer Segmentation book cover89

Data Mining Techniques in Crm: Inside Customer Segmentation

Konstantinos K. Tsiptsis, Antonios Chorianopoulos

4.1
This is an applied handbook for the application of data mining techniques in the CRM framework. It combines a technical and a business perspective to cover the needs of business users who are looking for a practical guide on data mining. It focuses on Customer Segmentation and presents guidelines for the development of actionable segmentation schemes. By using non-technical language it guides read
Real-World Machine Learning book cover90

Real-World Machine Learning

Henrik Brink, Joseph Richards, Mark Fetherolf

4.1
Summary

Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats
Text Mining: A Guidebook for the Social Sciences book cover91

Text Mining: A Guidebook for the Social Sciences

Gabe Ignatow and Rada F. Mihalcea

4.1
Online communities generate massive volumes of natural language data and the social sciences continue to learn how to best make use of this new information and the technology available for analyzing it. Text Mining: A Guidebook for the Social Sciences brings together a broad range of contemporary qualitative and quantitative methods to provide strategic and practical guidance on analyzing large te
Statistical Methods for Speech Recognition book cover92

Statistical Methods for Speech Recognition

Frederick Jelinek

4.0
This book reflects decades of important research on the mathematical foundations of speech recognition. It focuses on underlying statistical techniques such as hidden Markov models, decision trees, the expectation-maximization algorithm, information theoretic goodness criteria, maximum entropy probability estimation, parameter and data clustering, and smoothing of probability distributions. The au
The Numerati book cover93

The Numerati

Stephen Baker

4.0
An urgent look at how a global math elite is predicting and altering our behavior -- at work, at the mall, and in bed

Every day we produce loads of data about ourselves simply by living in the modern world: we click web pages, flip channels, drive through automatic toll booths, shop with credit cards, and make cell phone calls. Now, in one of the greatest undertakings of the twenty-first century, a
Practical Time Series Analysis: Prediction with Statistics and Machine Learning book cover94

Practical Time Series Analysis: Prediction with Statistics and Machine Learning

Aileen Nielsen

4.0
Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase.

Covering innovations in time ser
Data Mining: Concepts, Models And Techniques (Intelligent Systems Reference Library) book cover95

Data Mining: Concepts, Models And Techniques (Intelligent Systems Reference Library)

Florin Gorunescu

4.0
The knowledge discovery process is as old as Homo sapiens. Until some time ago this process was solely based on the 'natural personal' computer provided by Mother Nature. Fortunately, in recent decades the problem has begun to be solved based on the development of the Data mining technology, aided by the huge computational power of the 'artificial' computers. Digging intelligently in different lar
The Enterprise Big Data Lake: Delivering the Promise of Big Data and Data Science book cover96

The Enterprise Big Data Lake: Delivering the Promise of Big Data and Data Science

Alex Gorelik

4.0
The data lake is a daring new approach for harnessing the power of big data technology and providing convenient self-service capabilities. But is it right for your company? This book is based on discussions with practitioners and executives from more than a hundred organizations, ranging from data-driven companies such as Google, LinkedIn, and Facebook, to governments and traditional corporate ent
Interpretable Machine Learning book cover97

Interpretable Machine Learning

Christoph Molnar

4.0
This book is about making machine learning models and their decisions interpretable.

After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explain
High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark book cover98

High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark

Holden Karau, Rachel Warren

4.0
Apache Spark is amazing when everything clicks. But if you haven't seen the performance improvements you expected, or still don't feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources.

Ideal for softw
Natural Language Processing with Pytorch: Build Intelligent Language Applications Using Deep Learning book cover99

Natural Language Processing with Pytorch: Build Intelligent Language Applications Using Deep Learning

Delip Rao, Brian McMahan

4.0
Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you're a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library.

Authors Delip Rao and Brian McMahon
Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection book cover100

Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection

Bart Baesens

4.0
Detect fraud earlier to mitigate loss and prevent cascading damageFraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This i