Feeling optimistic after the Future of the Statistical Sciences Workshop

Last I week I participated in the Future of the Statistical Sciences Workshop. I arrived feeling somewhat pessimistic about the future of our discipline. My pessimism stemmed from the emergence of the term Data Science and the small role academic (bio)statistics department are playing in the excitement and initiatives surrounding it.  Data Science centers/departments/initiatives are propping up in universities without much interaction with (bio)statistics departments. Funding agencies, interested in supporting Data Science, are not always including academic statisticians in the decision making process.

About 100 participants, including many of our discipline’s leaders, attended the workshop. It was organized in sessions and about a dozen talks; some about the future, others featuring collaborations between statisticians and subject matter experts. The collaborative talks provided great examples of the best our field has to offer and the rest generated provocative discussions. In most of these discussions the disconnect between our discipline and  Data Science was raised as cause for concern.

Some participants thought Data Science is just another fad like Data Mining was 10-20 years ago. I actually disagree because I view the recent increase in  the number of fields that have suddenly become data-driven as a historical discontinuity. For example, we first posted about statistics versus data science back in 2011.

At the workshop, Mike Jordan explained that the term was coined up by industry for practical reasons: emerging companies needed a work force that could solve problems with data and statisticians were not fitting the bill. However, at the workshop there was consensus that our discipline needs a jolt to meet these new challenges. The take away messages were all in line with ideas we have been promoting here in Simply Statistics (here is a good summary post from Jeff):

  1. We need to engage in real present-day problems (problem first not solution backward)

  2. Computing should be a big part of our PhD curriculum (here are some suggestions)

  3. We need to deliver solutions (and stop whining about not being listened to); be more like engineers than mathematicians. (here is a related post by Roger, in statistical genomics this has been the de facto rule for a while.)

  4. We need to improve our communication skills (in talks or on Twitter)

The fact that there was consensus on these four points gave me reason to feel optimistic about our future.

 
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