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?
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.
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.
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.
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.
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.
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.
A new book, “Data-Driven Organization: Sustaining the competitive edge through organizational analytics,” by Rupert Morrison addresses one of today’s most pervasive business challenges – how to capitalize on the wealth data organizations possess to help employees to perform.
I’m frequently asked about educational resources for those making their entry into the data science and machine learning professions. There are plenty of good advanced books such as theoretical masterpieces. The book “R for Everyone: Advanced Analytics and Graphics” authored by Jared P. Lander covers the intermediate ground very well.