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Book Review: The Mathematical Corporation by Josh Sullivan and Angela Zutavern

As a data scientist, I know first hand how today’s enterprise has some catching up to do with engaging the mathematical foundations for capitalizing on an ever-increasing volume of data assets. This is why a new title is so important: “The Mathematical Corporation – Where Machine Intelligence and Human Ingenuity Achieve the Impossible,” by Josh Sullivan and Angela Zutavern. Sullivan and Zutavern are, respectively, senior vice president and vice president of Booz Allen Hamilton.

Book Review: Monetizing Your Data by Andrew Roman Wells and Kathy Williams Chiang

For those of us firmly entrenched in the big data industry, we’re well familiar with the “collect data at all costs” mantra. So it’s no great surprise to see this new title that takes the story to its completion, specifically how to monetize all the data being stored away. My own personal recurring slogan is for enterprises to “maximize the value of their data assets” so “Monetizing Your Data: A Guide to Turning Data into Profit-Driving Strategies and Solutions,” by Andrew Roman Wells and Kathy Williams Chiang is a welcome resource.

Book Review: Julia for Data Science by Zacharias Voulgaris, Ph.D.

Here’s a useful new book for data scientists looking to approach the field from a unique perspective that doesn’t include language heavyweights like R and Python. “Julia for Data Science,” by Zacharias Voulgaris, Ph.D. from Technics Publications, allows you to master the Julia language to solve business critical data science challenges. But why look to a relatively new language when you already have other commonly-used languages at your disposal?

Book Review: Deep Learning by Goodfellow, Bengio, and Courville

I don’t usually get excited about a new book for the field in which I’ve been deeply involved for quite a long time, but a timely and useful new resource just came out that provided me much anticipation. “Deep Learning” by three experts in the field – Ian Goodfellow, Yoshua Bengio, and Aaron Courville is destined to considered the AI-bible moving forward.

Book Review: The Manga Guide to Regression Analysis

Last year, I wrote a review of a useful book that got students up to speed with a key mathematical ingredient of machine learning – linear algebra: The Manga Guide to Linear Algebra. No Starch Press (an excellent source of technical books) just came out with a follow-up title: The Manga Guide to Regression Analysis.

Book Review: The Book of R by Tilman Davies

A fantastic new book just landed on my desk, “The Book of R: A First Course in Programming and Statistics” by Tilman M. Davies from No Starch Press. I’ve been looking for a book like this for some time – to use with the introductory data science and machine learning course I teach.

Book Review: Introduction to Linear Algebra by Gilbert Strang

In this book review, I take a close look at the 5th edition of “Introduction to Linear Algebra” (Wellesley-Cambridge Press) by MIT mathematics professor Gilbert Strang. This book is a must-have for any serious data scientist.

Book Excerpt – Delivering on Digital: The Innovators and Technologies That Are Transforming Government

In his new book, William D. Eggers, one of the country’s best-known authorities on government reform, explores how a new generation of digital innovators is re-imagining virtually every facet of what government does and reducing their costs with the power of digital transformation.

Book Review: Data Lake Architecture

A new book “Data Lake Architecture – Designing the Data Lake and Avoiding the Garbage Dump” by the father of the data warehouse Bill Inmon is a simple, high-level introduction to this popular data organization. Written for enterprise thought-leaders and decision makers, the book offers a one-stop resource that explains how to build a useful data lake where data scientists and data analysts can solve business challenges and identify new business opportunities. Readers will learn how to structure data lakes as well as analog, application, and text-based data ponds to provide maximum business value.

Book Review: Why – A Guide to Finding and Using Causes

A new book, “ Why: A Guide to Finding and Using Causes ,” by Stevens Institute of Technology assistant professor of computer science Samantha Kleinberg is a necessary addition to any data scientist’s bookshelf as it helps bring focus to the dreaded “correlation does not imply causation” conundrum that affects our understanding of data-centric problems.