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 Excerpt – Delivering on Digital: The Innovators and Technologies That Are Transforming Government
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.
I’ve been waiting for good book that introduces the concepts of data science and machine learning for a lay audience. Then I read an announcement of a new book that seemed to fill this need. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Basic Books; September 22, 2015) by University of Washington professor Pedro Domingos. My hopes were high and for the most part I think this book represents a good introduction to the area.
A book just came across my desk, “Storytelling with Data – A Data Visualization Guide for Business Professionals,” by author Cole Nussbaumer Knaflic, that is a handy resource for data scientists of all levels of experience. It is an important job of data scientists to engage in “data storytelling” to communicate the results of their machine learning projects.
When one of my favorite independent tech book publishers, No Starch Press, notified me about their new title “Doing Math with Python,” I was energized to review what potentially could be a good new resource for budding data scientists.
I was happy to receive a review copy of book employing a very unique approach for teaching mathematics, “The Manga Guide to Linear Algebra,” published by No Starch Press. This is a comic book, perfect for new data scientists! The book is great for newbies because it clearly spells out each minute step in performing calculations involving vectors, matrices, determinants, linear transformations, kernels, eigenvalues and eigenvectors.
Text analytics recently has become a useful adjunct to other machine learning methods, of great interest to both data scientists and big data engineers. With “Practical Text Analytics: Interpreting text and unstructured data for business intelligence,” Dr. Steven Struhl provides timely and lucid discussion of the topic.