I attended this year’s Strata + Hadoop World Conference in NYC at the Javit’s Center late last week. There were “boat loads” of speakers, tutorials, and vendors pitching their latest, greatest software, solutions, and hardware to attack the “big data” opportunity.
One of the more entertaining sessions was on Thursday
Hilary Mason (Accel Partners), Lucian Lita (Intuit), Vs. Scott Nicholson (Poynt), Joseph Adler (Interana, Inc.),
Many popular tools for analyzing data require programming (R, Hadoop, Pandas, Spark, etc.) and many data scientists know how to write code. But many data scientists also use graphical tools (like Excel or Tableau) to explore data. Is it possible to be a great data scientist if you can’t code? If it’s not possible today, are there tools that could make it possible?
In this Oxford Style debate, two teams of the world’s best data scientists debated the following proposition: “If you can’t code, you can’t be a data scientist.”
List of debaters:
RESULT: Hilary Mason and Lucian Lita’s Team won quite easily (according to the audience show of hands) and I believe it came down to one major fact. You “can do” data science without being able to code, BUT you won’t be able to do it very well at all (right now) and/or very efficiently and thus NO ONE will hire you. So learn to code!
I decided to celebrate with the winner. Nice Job Hilary!
In general the conference is still dominated by Big Data infrastructure solutions (NoSQL/RMDBS Databases, New Hadoop Layers, Faster Processing, Analytics Platforms, Visualization Tools, etc.).
It will be interesting to see if by next year we don’t see more “plug and play” big data apps and tools for non-techie end users. The venture capitalist there seemed to think that is where things are headed.
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