Tunkelang: The O’Reilly Strata Conference brings together an incredible community of people working on big data. This year, I decided to do something different for my presentation. Rather than talk about science or technology, I addressed the practical problem of interviewing the candidates to build teams of data scientists.
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Hal Varian: “Data availability is going to continue to grow. To make that data useful is a challenge. It’s generally going to require human beings to do it.”
Source: Carl Bialik, “Data Crunchers Now the Cool Kids on Campus,” The Wall Street Journal, March 1, 2013
See my list of graduate programs in data science and big data analytics
To see the other 56 questions assessing “the technical horizontal knowledge of a senior candidate at a high level” go here
Lotsa data in this Infographic about data growth
[youtube http://www.youtube.com/watch?v=oGcZ7WVx6EI]
[youtube http://www.youtube.com/watch?v=J_CYKk8q1Ao]
Summary of the presentation by Ben Rooney here
Update: Ben Rooney interviews DJ Patil
[youtube http://www.youtube.com/watch?v=0LtzMhr0ZCM]
Past Courses
in Big Data Analytics and Data Science
Content Online
Analyzing Big Data with Twitter (UC Berkeley, School of Information) (Fall 2012)
Introduction to Data Science (Columbia University, Statistics Department) (Fall 2012
Introduction to Data Science (UC Berkeley, Computer Science) (Spring 2011)
“Let us cultivate the mathematical sciences with ardor, without wanting to extend them beyond their domain; and let us not imagine that one can attack history with formulas, nor give sanction to morality through theories of algebra or the integral calculus”–Augustin-Louis Cauchy, 1821, quoted by Matthew Jones, Columbia University
“…the common language of business is not going to be Chinese or Spanish. It’s going to be math”–Michael Rhodin, IBM
“The future is going to be owned by people who are comfortable in the quant world but have deep business knowledge”–Christine Poon, Max M. Fisher College of Business, Ohio State
“[One false promise that some proponents of Big Data hold out is that somehow vast oceans of digital data can be sifted for nuggets of pure enterprise gold.] It is not going to happen magically. The software only finds correlations, not causations. In order to find causal relationships you have to do work. If you take any sufficiently large data sets, you are going to find correlations. You need a human in the loop to work out which are important”–Stephen Sorkin, Splunk
Continue reading“Data is everywhere. It exists. We’re just pulling it into one place and our goal is to make it consumable for teachers”–Fahad Hassan, Always Prepped
“A lot of people are changing their title, but they’re not really data scientists, and there’s a lot of talk about the skills shortage. There just aren’t enough of them”–Amit Bendov, SiSense
“Engineering, I think you can pick up. [A data scientist’s] curiosity is built-in”–Scott Nicholson, Accretive Health
“The thought process is the most important ingredient in data science”–Catalin Ciobanu, Carlson Wagonlit Travel.
“We run the company by questions, not by answers. So in the strategy process we’ve so far formulated 30 questions that we have to answer […] You ask it as a question, rather than a pithy answer, and that stimulates conversation. Out of the conversation comes innovation”–Eric Schmidt, Google
“We’re seeing the beginnings of bringing the collaboration models that have been vastly successful in open-source communities to data science… The future looks like this: The entire workflow from data to analysis to result to visualization will be social and collaborative“–Donnie Berkholz
“it’s not hard to imagine a day where [baseball] managers… have their locker room data scientist run real-time, in-game analytics using technologies like Cassandra, Hbase, Drill, and Impala”–Barry Eggers, Lightspeed Venture Partners
“Measuring influence is hard, especially in the context of an online social network. We may not be able to explicitly model the process of persuading others to change their behavior, especially when we do not have all of the necessary data in one place. But it is crucial test of an influence measure’s realism that it recognize human attention as a scarce commodity, and that it be resistant to manipulation. In any case, influence matters too much for us not to try to measure it. Influence is ultimately about the battle for the scarce space in people’s minds–our most precious natural resource”–Daniel Tunkelang, LinkedIn
Steve Jobs had an insanely great ride on the waves of digitization that have transformed the way we work and play over the last few decades. But taking a cursory look at the hundreds of tributes published to commemorate the anniversary of his passing, I was surprised to find lots of trees but not a single forest. The pig picture view of Jobs’ life is sorely missing.
We hear about a lot of specific things that he did or stimulated: He was “a genius toymaker,” a “genuine human being,” a “patent warrior.” He invented this, pushed for that, and denounced the other thing. All true. But wasn’t there something bigger that connected all the dots besides his creativity and drive?
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