By Sebastian Gutierrez
Info Scientists at paintings is a suite of interviews with 16 of the world's such a lot influential and cutting edge facts scientists from around the spectrum of this sizzling new occupation. "Data scientist is the sexiest activity within the twenty first century," in response to the Harvard enterprise assessment. via 2018, the U.S. will adventure a scarcity of 190,000 expert facts scientists, in accordance with a McKinsey document. every one of those facts scientists stocks how she or he tailors the torrent-taming thoughts of massive info, information visualization, seek, and statistics to express jobs via dint of ingenuity, mind's eye, endurance, and keenness. information Scientists at paintings components the curtain at the interviewees' earliest facts initiatives, how they grew to become info scientists, their discoveries and surprises in operating with facts, their options at the earlier, current, and way forward for the career, their reviews of crew collaboration inside of their corporations, and the insights they've got received as they get their fingers soiled refining mountains of uncooked info into gadgets of industrial, medical, and academic worth for his or her corporations and consumers.
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Extra resources for Data Scientists at Work
Then I became a department head at AT&T Labs, which was the name of AT&T’s research lab after the company split up in 1996. I joined NYU in 2003, so I’ve been here a little over 11 years. I joined Facebook at the end of 2013. Gutierrez: What excited you about the opportunity at Facebook? LeCun: The main thing is that I was given the opportunity to create a worldclass research lab from scratch. Facebook did not have a tradition of being active in research, so this was a bit of a new experiment for Facebook.
Basically anything in the non-numerical data world will grow very fast. Specifically to text, I find what Twitter is doing to be really interesting, especially the skills around taking that super-fuzzy text data and being able to identify important patterns. I think this is an area that will really explode. Gutierrez: What is driving the growth in text analytics? Smallwood: More and more mechanisms are dealing with text data because we’re able to handle larger volumes. You used to have a multiple-choice question where you had to answer a, b, c, or d, and then that data would get encoded.
Experimentation and getting your statistical results is the easy part. The hard part is interpreting those results when you know it’s still a world of uncertainty and you might have metrics that are telling different stories about the same test. How do you interpret those results and try to translate what the test was testing, what the change in the product was, and imagining all the reasons why you might be getting these inconsistent metrics? Gutierrez: How do you help new team members develop this way of thinking about interpreting outputs and results?
Data Scientists at Work by Sebastian Gutierrez