“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