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Month: April 2017

Academics and Data Science

I received the following comment on an article: Let’s Stop Using the Term Fake Data Scientist and thought it merited a response.  Usually the comments I receive are constructive even if they disagree with what I wrote, but this particular comment, demonstrated an arrogance which I believe is a huge problem in the data science world.

You can of course read the original article here, but the basic point was that data science is interdisciplinary field–consisting of a mixture of computer science, applied mathematics, and subject matter expertise, with a smattering of data visualization and communication skills.   I believe that it is inappropriate to label someone as a fake simply because their skillset is proportioned differently than many math-centric data scientists.  I’m also a believer in Dr. Carol Dweck’s thesis on having a growth-oriented mindset (as stated in her book Mindset) and that people who might be working in data science but whose skills need development in a certain area, should be given instruction and assistance rather than derogatory labels.

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A Proposal for Data Science Ethics

In the last few days I’ve been pleased to see that the issue of privacy which I wrote about last week about ISPs being able to sell your browsing history is getting a lot more coverage in the tech blogs such as Wired, Gizmodo, ArsTechnica, FOSSBytes and many others.   I read a lot of different news sources and I was hard pressed to find ANY writers supporting this government action.

This morning, DJ Patil, former US Chief Data Scientist, posted some comments about how much damage a data scientist could do with everyone’s browsing history.  (Complete thread here)  It occurred to me that data science is perhaps one of the most powerful professions of the 21st century and yet, there is no certifying body to determine who is and who is not a data scientists, nor is there any licensing, nor any professional society to establish rules of professional conduct.  Virtually every profession which can have an impact on the public has some sort of licensing or professional conduct code, and yet data science does not.  After all, even lawyers have a code of professional ethics.