Sign up for our newsletter and get the latest big data news and analysis.

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.

Deep Learning at Google with Jeff Dean

In the Google TechTalk video presentation below, luminary Jeff Dean discusses the use of Deep Learning at Google – “Large-Scale Deep Learning for Intelligent Computer Systems.”

Data Science at Ticketmaster

In the video presentation below, Jenn Webb, Managing Editor at Radar, interviews John Carnahan who serves as Executive Vice President of Data Science at Ticketmaster.

The Science of Data-Driven Storytelling

The video presentation below captures the highlights of the all-day data-driven storytelling event produced by DataScience, Inc. and the National Science Foundation (NSF)’s West Big Data Innovation Hub.

Bridging the Gap Between Data Science and Data Engineering

In the compelling keynote address below, Josh Wills, Director of Data Engineering at Slack, discusses an all-too-common theme these days: “Data Engineering and Data Science: Bridging the Gap.”

Data Science 101: GPU Programming for Beginners

The presentation below is an educational resource that sets the stage for parallel programming with GPUs (graphics processing units) and was sponsored by the Center for Astrophysics and Supercomputing at Swinburne University of Technology. GPUs are becoming quite popular for the implementation of deep learning solutions.

Data Science 101: General Learning Algorithms

In the presentation below, Dr. Demis Hassabis from Google DeepMind delivered a talk on “General Learning Algorithms” to the Royal Society in London on May 22, 2015. Hassabis was the co-founder and CEO of DeepMind, a neuroscience-inspired AI company, bought by Google in Jan 2014.

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.

Data Science and Statistics: Different Worlds?

The video presentation below, courtesy of the Royal Statistical Society, includes a panel of distinguished practitioners to bring their own perspectives on important issues surrounding the growing field of data science.

Coursera Announces First MOOC-Based Master’s Degree in Data Science

Coursera, a leading online education company known for massive open online courses (MOOCs), today announced a professional data science master’s degree from the University of Illinois at Urbana-Champaign.