If you’re building or growing a data science team, the first reflex is to hire new talent. Before you do so, take a few moments to ask yourself the following questions.
Our friends over at DataCamp have produced the “Become a Data Scientist in 8 Steps” infographic providing a view of the eight steps that you need to to through to learn data science. Some of these eight steps will be easier for some than for others, depending on background and personal experience, among other factors.
In the TEDx video presentation below, Kevin Novak, Senior Data Scientist at Uber, provides a description and history of Uber and how Uber’s data hacking made their explosion possible.
With the continued upward trajectory of interest in getting on board with a data science career, our friends over at Simplilearn Solutions put together the compelling infographic below.
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.
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.”
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 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.
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.”
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.