A data scientist shares his passions

\We asked our staff data scientist what motivates him, and here's what he said:

My passions:

  1. Data Science research but not in an academic or corporate environment.
  2. Developing new, synthetic metrics (to measure yield or for data reduction), and robust, simple, scalable techniques to handle big, unstructured, messy, flowing data -- avoiding the curse of big data.
  3. Offering awards to winners in our competitions.
  4. Delivering state-of-the-art, business-oriented knowledge, as open-source intellectual property, for free.
  5. Unifying all analytic fields (currently acting as independent silos): machine learning, business analytics, predictive modeling, data mining, operations research, quant, computer science, econometrics, statistics, and so on.
  6. Developing solid automated data science solutions for the non-expert, or for black-box computations and predictions.
  7. Growth hacking. Developing the largest, most useful, and most successful community, for analytic practitioners, with strong, diversified revenue streams and viral growth (powered by computational marketing), using a lean start-up approach (involving massive vendor outsourcing and self-funding).
  8. Delivering true data science training (and certification with our partners) using a new paradigm: free apprenticeship, 6-month long, online, for self-learners. Our current intern (nuclear physicist, post-doc from Columbia University and EPFL - Lausanne, Switzerland) is one of our former candidates. Emphasis is on stuff that you can’t learn in traditional (often outdated) university curricula.
  9. Debunking myths about the lack of analytic talent, being evangelist and the voice of data science, promoting horizontal over vertical knowledge, and warning about fake data science and other would-be data scientists.
  10. Defining data science, big data, and helping companies identify real talent.
  11. Selling data offered via API services, for instance stock price forecasts.
  12. Writing books, especially with a self-publishing platform such as Lulu.com.

We invite you to share your passions with us, in the comment section below.

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Tags: predictive modeling


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Comment by Elías De La Rosa on March 19, 2016 at 1:04pm
Laetitia: even though I am not even close to be a data scientist, I find something as my passion: closing the gap between the data scientist silo and the layman; this reconciliation will be beneficial for both, as the layman will benefit from (and ask for more of) the data scientist's work, and thus there will be more room and demand for people like you and me.
Comment by Vincent Granville on December 17, 2015 at 6:36pm

Sione, my mentor (when I was working on my PhD) always said that I should stay away from reading articles published by other scientists, as it would hurt my creativity or potentially influence my research in undesirable ways. I actually do read what other people write, but that does not stop me from doing unbiased research, free of conflict of interests or external influences. And what I write gets heavily commented; it probably gets more peer-reviewing than traditional scientific articles, mostly by impartial readers (at least on DSC).

Comment by Kalyanaraman K on December 17, 2015 at 4:13pm
There are many untapped methods in Statistics, including early tools that have gone away from regular inclusion in academic work. For example, correllogram in time series, periodogram (available now in a new format) in time series, confluence analysis and the like. They could be used under the current environment in a useful way.
Comment by Sione Palu on December 15, 2015 at 9:26am

Quote : "Data Science research but not in an academic or corporate environment."

Research can't be all done in academic or corporate,, but it make sense for an enthusiast data scientist to keep tap on academic research for new ideas.

Comment by Peter J Jamack on December 13, 2015 at 8:59pm

The biggest issue I have with #4, #7 and #11 is it's kind of bogus.   I believe in open source, but I also believe in Open Data.  How you figure out revenue streams beyond that is a challenge, but you can't really claim to be all about open sourcing the technology and then locking down the data and charging for it. It's the same thing, just a different model.   But it's not open, not even close.  

Most of these data competitions and cool sources wouldn't exist if there were no open data initiatives.  And there still are tons of silos and locked down or very expensive data sources out there.   If everybody went back to locking down the data and charging access for it or giving out APIs for fees,  we aren't moving forward.
Nothing changes if instead of charging for software licenses, you just charge for data licenses.  Same thing, just a different model.  But it's not open.

 You really can't complain about governments and hospitals and businesses needing to push and join open data practices if you're just locking up your own data in your own silos for a fee.  Especially if you start shouting for everybody to open source their technology.   It's just bogus.

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