Top 10 Books on Predictive Analytics and Data Modeling

There are a wide variety of books available on predictive analytics and data modeling around the web. Selecting the one that is right for you or your data-driven organization can be a tough, even overwhelming task. Solutions Review has done the research for you. After reviewing a multitude of books on the subject matter, we’ve carefully selected the following 10 books, based on relevance, popularity, online ratings, and their ability to add value to your business.

Data modeling is typically the first step in database design and is used to create a conceptual model of how data relates to each other. Coupled with predictive analytics, which can help your company extract information from existing data models in order to recognize patterns and predict future trends, the two can create business insights. Those discoveries can lead to better profits, happier customers, faster reaction times, and more.

Note: these titles are not industry specific; they should have applications in a variety of fields.

Applied Predictive Modeling by Max Kuhn and Kjell Johnson

“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 statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.”

Predictive Analytics For Dummies by Anasse Bari, Mohamed Chaouchi and Tommy Jung

“Combine business sense, statistics, and computers in a new and intuitive way, thanks to Big Data Predictive analytics is a branch of data mining that helps predict probabilities and trends. Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in fields such as advertising, fraud detection, politics, and others. This practical book does not bog you down with loads of mathematical or scientific theory, but instead helps you quickly see how to use the right algorithms and tools to collect and analyze data and apply it to make predictions. Topics include using structured and unstructured data, building models, creating a predictive analysis roadmap, setting realistic goals, budgeting, and much more.”

Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science (FT Press Analytics) by Thomas W. Miller

“Start with strategy and management. Master methods and build models. Transform your models into highly-effective code—in both Python and R.

This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value.”

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

“This book is easily understood by all readers. Rather than a “how to” for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques.

You have been predicted — by companies, governments, law enforcement, hospitals, and universities. Their computers say, “I knew you were going to do that!” These institutions are seizing upon the power to predict whether you’re going to click, buy, lie, or die.”

Data Science for Business: What you need to know about data mining and data-analytic thinking by Foster Provost and Tom Fawcett

“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.”

Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition by Bruce Ratner

“The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of creative and useful machine-learning data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature.”

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

“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.”

Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst by Dean Abbott

“Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included. * The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today * This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions * Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish * Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios * A companion website provides all the data sets used to generate the examples as well as a free trial version of software.”

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) by Trevor Hastie, Robert Tibshirani and Jerome Friedman

“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. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas 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 color 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.”

R for Everyone: Advanced Analytics and Graphics (Addison-Wesley Data & Analytics Series) by Jared P. Lander

“Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks.”

Timothy King
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Timothy King

Editor, Data and Analytics at Solutions Review
Timothy leads Solutions Review's Business Intelligence, Data Integration and Data Management areas of focus. He is recognized as one of the top authories in Big Data, and the number-one authority in enterprise middleware. Timothy has also been named one of the world's top-75 most influential business journalists by Richtopia.
Timothy King
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