Web Picks (week of 18 April 2016)

Every two weeks, we find the most interesting data science links from around the web and collect them in Data Science Briefings, the DataMiningApps newsletter. Subscribe now for free if you want to be the first to get up to speed on interesting resources.

  • How to approach machine learning as a non-technical person
    This post is not a primer on ML technology; this post won’t pretend to give you an explanation of deep learning or any specific technology, because these concepts change frequently and are largely irrelevant to much of the decision making. Instead, this post will address how to assess the technology and determine if it will yield pragmatic business value.
  • Building a High-Throughput Data Science Machine
    Scaling is hard. Scaling data science is extra hard. What does it take to run a sophisticated data science organization? What are some of the things that need to be on your mind as you scale to a repeatable, high-throughput data science machine
  • Doing Data Science Right — Your Most Common Questions Answered
    It’s hard to believe that “data scientist” only became a bona fide job title in 2008. Jeff Hammerbacher at Facebook and DJ Patil at LinkedIn coined the term to capture the emerging need for interdisciplinary skills across analytics, engineering, and product. Today, the demand for data scientists has blossomed, and with it the need to better understand how to grow these teams for success.