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What Does It Mean To Be a Data Scientist?
Nerval's Lobster writes What is a data scientist? "To be honest, I often don't tell people I am a data scientist," writes Simon Hughes, chief data scientist of the Dice Data Science Team. "It's not that I don't enjoy my job (I do!) nor that I'm not proud of what we've achieved (I am); it's just that most people don't really understand what you mean when you say you're a data scientist, or they assume it's some fancy jargon for something else." So how do Simon and his team define "data scientist"? In this blog posting, he breaks it down along several lines: solid programming skills, a scientific mindset, and the ability to use tools are just for starters. A data scientist also needs to be a polymath with strong math skills. "All good scientists are skeptics at heart; they require strong empirical evidence to be convinced about a theory," he writes. "Likewise, as a data scientist, I've learned to be suspicious of models that are too accurate, or individual variables that are too predictive." His points are good to keep in mind right now, with everybody throwing around buzzwords like "Big Data" without fully realizing what they mean.
Without sociology skills (my blog) on a data science team, hypothesis formation and ability to model clients will suffer. It would seem particularly important for a people-focused company like Dice.com.
Errr... You claim to be a scientist and yet you say "All good scientists are skeptics at heart; they require strong empirical evidence to be convinced about a theory," .
Circular definition, circular argument. Also, false. Many scientists (like Darwin for example) form a theory and then look for empirical evidence to test that theory. Next time start that sentence with "In my opinion" and you get away with it. You didn't and you don't.
Reading your article, it says nothing. I would not hire you on the basis of what you have written here.
Pardon me if that seems rude but it was in my opinion, too superficial to ignore.
Oh! By the way, what you do has had a title for a generation. You are an analyst doing what analysts do. Analyse data.
I'm sure there are some good data scientists but most of the papers I've seen lately that are based on statistics or various data sets are extremely lazy. You have someone that just combs through data and then tries to make a novel association. Nearly always they just show correlation and never causation.
I think that is one of the bigger problems. Because you're not collecting the data or structuring the experiments that collect the data, you can't isolate anything from the data. All you can do is say "well, this might be happening"... which is often completely useless. A more useful thing they could do is find that correlation and then see if they actually have causation by doing a follow up experiment or study that isolates for a specific variable under controlled conditions.
That is, I think data scientists would be more useful if they used the study as a jumping off point to doing an actual study. And I'm not especially interested in reading or even hearing about anything they've done until they've concluded that secondary study.
Absent that... it is lazy, boring, not interesting, and who cares.
I've decided to stop wasting my time responding to AC trolls/sockpuppets... so if you want a response from me... login.
But there's a big difference between a scientist and a statistician. A scientist pokes around and discovers new theories or mathematical models (often out of thin air). A statistician OTOH, like an engineer, simply applies the theories of scientists to accomplish real world usable things like pie charts and bar graphs.
So unless this guy is discovering or testing new theories, he's not a scientist. He's just a statistician.