Subject archive for "data-science-leaders"
What if you wanted to do something really ambitious in data science–something like designing an innovative new search engine? Today, that would be a daunting task, and you’d probably need a big, highly qualified team of data scientists and programmers to bring your innovation to life. And you’d need months, if not years, to finish it.
By Lisa Stapleton3 min read
How do you build a data science capability into a powerful force for making decisions about virtually all facets of your business? And how do you recruit, train, organize, and retrain members of your team, in a field where competition for talent is intense and growing? These are just a couple of the questions that Glenn Hofmann, chief analytics officer at New York Life Insurance Company, has confronted and mastered in his tenure there.
By Lisa Stapleton4 min read
7 Top Innovators Share Insights, Trends and Career Advice in 'The Data Science Innovator’s Playbook'
Who’s doing the most innovative things in data science? Where is the profession going? And most importantly, what can you learn from some of the brightest in the business?
By Lisa Stapleton6 min read
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.
By Nikolay Manchev8 min read
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.
By David Bloch9 min read
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