What is the best book for big data analytics?

People with big data and data science skills are some of the most sought after professionals because demand is outstripping supply. Here are 10 books that can help you learn everything about the emerging field and the tools you will need to conquer it. 

Best Big Data Books To Boost Your Career

The Best Big Data & Data Analytics Books Of All Time

1) Data Analytics Made Accessible, by A. Maheshwari

Best for: the new intern who has no idea what data science even means

Example of a rave review:

“I would definitely recommend this book to everyone interested in learning about Data Analytics from scratch and would say it is the best resource available among all other Data Analytics books.” —reader’s review

If we had to pick one book for an absolute newbie to the field of Data Science to read, it would be this one. Updated for 2017, “Data Analytics Made Accessible” is one of the best books on data analytics, and does exactly what its name implies: it exaplains data analytics in an easy way, and makes them understandable and digestible for the uninitiated.

The book promotes easy understanding through:

• Concrete, real world examples at the beginning of each chapter
• An intuitively organized layout structured like a one semester college course
• Case studies throughout each chapter to tie the material together

Due to its scope of content and clear explanation, “Data Analytics Made Accessible” has been made a college textbook for many universities in the US and worldwide. The author, Anil Maheshwari, Ph.D., has both practical and intellectual knowledge of data analytics, as he worked in data science at IBM for 9 years before becoming a professor.

The book also has some “crowdsourced” material, as the 2017 edition had 4 chapters added based on feedback from reviewers and readers. At 156 pages on Kindle, this is a book you could finish in one (long) sitting if you were so inclined, that you can also use as an inspiration when you work with your business intelligence software.

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

 

Eric Siegel's breakout book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (Published by Wiley; foreword by Thomas H. Davenport) has been called "The Freakonomics of big data," and "the definitive book of this industry" that is "an operating manual for 21st century life."
At less than $15, this book and its Kindle version hold the #1 and #2 Best Seller positions in both "Business Planning & Forecasting" and "Econometrics" on Amazon.com.

 

3) Data Smart: Using Data Science to Transform Information into Insight, by J. 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.
Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype.
But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.
 Each chapter will cover a different technique in a spreadsheet so you can follow along:

• Mathematical optimization, including non-linear programming and genetic algorithms
• Clustering via k-means, spherical k-means, and graph modularity
• Data mining in graphs, such as outlier detection
• Supervised AI through logistic regression, ensemble models, and bag-of-words models
• Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation
• Moving from spreadsheets into the R programming language

You get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

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

 

Go ahead, be skeptical about big data. The author was--at first. 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. This book will help you understand: (1) Why big data is important to you and your organization, (2) What technology you need to manage it, (3) How big data could change your job, your company, and your industry, (4) How to hire, rent, or develop the kinds of people who make big data work, (5) The key success factors in implementing any big data project, and (6) How big data is leading to a new approach to managing analytics. With dozens of company examples, including UPS, GE, Amazon, United Healthcare, Citigroup, and many others, this book will help you seize all opportunities--from improving decisions, products, and services to strengthening customer relationships. It will show you how to put big data to work in your own organization so that you too can harness the power of this ever-evolving new resource.