Subject archive for "data-science-leaders," page 2

One of the biggest challenges in data science today is finding the right tool to get the job done. The rapid change in best-in-class options makes this especially challenging - just look at how quickly R has fallen out of favor while new languages pop up. If data science is to advance as rapidly as possible in the enterprise, scientists need the tools to run multiple experiments quickly, discard approaches that aren’t working, and iterate on the best remaining options. Data scientists need a workspace where they can easily experiment, fail quickly, and determine the best data solution before they run a model through certification and deployment.

Danger of Big Data

Building models requires a lot of time and effort. Data scientists can spend weeks just trying to find, capture and transform data into decent features for models, not to mention many cycles of training, tuning, and tweaking models so they’re performant.

Today, we announced the latest release of Domino’s data science platform which represents a big step forward for enterprise data science teams. We’re introducing groundbreaking new features – including On-demand Spark clusters, enhanced project management, and the ability to export models – that give enterprises unprecedented power to scale their data science capabilities by addressing common struggles.

Paco Nathan's latest monthly article covers Sci Foo as well as why data science leaders should rethink hiring and training priorities for their data science teams.

Paco Nathan presented, "Data Science, Past & Future", at Rev. This blog post provides a concise session summary, a video, and a written transcript.