Subject archive for "best-practices," page 3

This guest post was written by Arnu Pretorius, a Masters student in Mathematical Statistics at the MIH Media Lab, Stellenbosch University. Arnu's research interests include machine learning and statistical learning theory.

As more companies recognize the need for a [data science platform], more vendors are claiming they have one. Increasingly, we see companies describing their product as a “data science platform” without describing the features that make platforms so valuable. So we wanted to share our vision for the core capabilities a platform should have in order for it to be valuable to data science teams.

At last week's useR! R User conference, I spoke on digital provenance, the importance of reproducible research, and how Domino has solved many of the challenges faced by data scientists when attempting this best practice. More on the topic, and a recording of the talk, below.

This morning there was a great conversation on Twitter, kicked off by Hadley Wickham, about one of the ugly little bits of the data science process.

This talk took place at the Domino Data Science Pop-up in Austin, TX on April 13, 2016

For those wondering if data science platforms are really a thing, there’s a great article by Kevin Novak, the head of Uber’s Data Science Platform team. He describes the problems they saw with their data science team, and why they needed a platform.