Information Governance

Your Big Data Analytics Summer Reading List

Big data books to keep you up to date

Big data books to keep you up to date

Interested in learning more about Big Data analytics? Here’s your required reading list:

Data Analytics Made Accessible: 2017 Edition
Anil Maheshwari

This title may look familiar if you’ve ever searched online for a book recommendation on data analytics before, and for good reason. After recently reading the 2017 edition, I wish this had been my stepping stone into the often times confusing world of big data. This book covers a wide range of topics from pattern recognition to social network analysis, serving as a “catch-all” introduction so readers can pick and choose chapters relevant to their interests. (If you’re not interested in the algorithms and models that are doing the behind-the-scenes work, and are strictly interested in concepts and implications, skip straight to Chapter 16.)

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

Another great introduction to big data analytics, Davenport’s book focuses more specifically on how analytics can play a huge role for businesses. In a very easily digestible way, the book walks through what big data is, how to incorporate it into a business strategy and which technologies and human skill sets to focus on to get its fullest benefit. The last couple of chapters offer an interesting comparison of big data use cases in small startups and large enterprises, making the book relevant for your firm no matter its size.

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
Foster Provost

If you’ve somewhat mastered the concepts of data analytics, you may want to skip straight to this one. Data Science for Business goes into much greater depth on how you can apply analytics to your business needs. Beware: at 350+ pages of textbook-like information, this is not a light read, but it is full of information and models on understanding which frameworks are helpful for parsing through analytics results and useful techniques to use data science findings for a competitive advantage.

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

This book was recently recommended to me as the “Freakonomics of analytics.” Each chapter poses intriguing questions like “Why is human behavior the wrong thing to predict?” and “What kind of risk has the perfect disguise?” If the topic of big data tends to bore you, the countless anecdotes and case studies here are bound to peak your interest.

Born and raised in the Middle East, I made my way to Silicon Valley via Philly. At ZL, I work on building customer relationships and solving customer problems. My passions include information governance and mozzarella sticks.