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Book Review: Python Data Science Handbook

I recently had a need for a Python language resource to supplement a series of courses on Deep Learning I was evaluating that depended on this widely used language. As a long-time data science practitioner, my language of choice has been R, so I relished the opportunity to dig into Python to see first hand how the other side of the data science world did machine learning. The book I settled on was “Python Data Science Handbook: Essential Tools for Working with Data” by Jake VanderPlas.

From the Editor’s Bookshelf: My Favorite Titles for Data Science and Machine Learning

As a practicing data scientist, I’ve spent years building up my library of academic and practical resources that I routinely draw upon for helping me do my work. Although my library is vast, I have a select group of books that occupy a prominent position on my desk. I’ve been asked enough times about my “favorite titles” list, I thought I’d write this article for my readers.

Book Review: The Future of IoT by Don DeLoach, Emil Berthelsen, and Wael Elrifai

“The Future of IoT – Leveraging the Shift to a Data Centric World,” by Don DeLoach, Emil Berthelsen, and Wael Elrifai is a self-published gem for anyone wondering about IoT. The overarching theme of the book is consistently – data, information, and knowledge – with a wrapper of use case examples to make it real. The book will assist you in kick-starting your evaluation of IoT technology in terms of all that data and how best to capitalize on it.

Book Review: Statistical Learning with Sparsity – The Lasso and Generalizations

As a data scientist, I have a handful of books that serve as important resources for my work in the field – “Statistical Learning with Sparsity – The Lasso and Generalizations” by Trevor Hastie, Robert Tibshirani, and Martin Wainwright is one of them. This book earned a prominent position on my desk for a number of reasons.

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.