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.
Many times we data scientists, not being statisticians in the strictest sense, hold the fear we may commit some kind of statistical faux paux. Fear no more! With the release of a probing new book “Statistics Done Wrong,” by Alex Reinhart, we have a curious road map for avoiding statistical fallacies.
Tweet32 Share7 Share2 +11 EmailShares 42Download your FREE copy of “Software Defined Storage for Dummies” today, compliments of IBM Platform Computing! This new learning resource can help enterprise thought leaders better understand the new area of software define storage in support of big data initiatives. Software defined storage is a relatively new concept in the […]