It's worth pointing out that Nate Silver is a rare individual. He's been wildly successful with data science despite not having a deep academic background, which is not the common case. His story is a useful data point though.<p>Do you need multiple degrees to master data science? Hell no. The practice of statistical inference requires only a few key skills. The important part is understanding how to think in terms of probability and algorithms, which doesn't require a rigorous understanding of proofs and formulae. An advanced academic degree is useful but not required. The world is looking for people who can consistently get the right answer, regardless of their background.<p>The other side to this is that a degree will be required to work for wall st, google, harvard, etc. Degrees still matter and social signalling and so on and so forth. We all can't be as well known and popular as Nate Silver and at some point institutions need a way of establishing that a particular candidate is truly qualified. So if you're not a messianic ml rock star, sweating the degree might be a good idea.<p>The beauty of a purely empirical craft is that results are the only arbiter of success. Anyone can get the right answer. It might be awhile before society catches up to and utilizes this powerful idea, but the hiring competitions on sites like kaggle are a great start [1]. Until then the best case is being the next Nate Silver.<p>[1] <a href="http://www.kaggle.com/c/facebook-recruiting-iii-keyword-extraction" rel="nofollow">http://www.kaggle.com/c/facebook-recruiting-iii-keyword-extr...</a>
I find the don't-go-to-school-just-do-it mentality that pervades tech blogs, article, discussions a bit short-sighted. I fully appreciate the importance of getting "hands-on experience" in addition to book learning, but what I don't get is the black-and-white perspective that formal education gives you NO EXPERIENCE WHATSOEVER.<p>As a PhD candidate, my perspective is quite definitely skewed, but my experience in school so far has not given me the impression that all I've been doing is book learning with no transferrable, real-world skills to work with data. I've had to work with plenty of data sets during my Masters and PhD research. I firmly believe that the experience I've gained from applying what I've learned in the classroom and from surveying existing literature on data mining and statistical methods stands me in good stead to tackle "real data" (whatever that means).<p>Maybe I've got the wrong end of the stick, but I think that something more than the "odd stats class" can be of value to a budding data scientist ...
I agree whole heartedly. When I first started playing around with data, despite my lite statistics background, I had no idea what I was doing and no amount of education would have prepared me for noisey, real life data sets. I had to become proficient with SQL and a certain type of problem solving and hacker mentality before I was able to do anything useful.<p>What's interesting now though is that since returning to college and taking more advanced courses in probability and statistics, I now have a "practical" background that allow me to see real life use cases for almost everything I'm learning (oh wonderful Bayes theorem!) that is a huge advantage.
most people don't really think logically or quantitatively. whatever math people learned in high school and college was forgotten when trying to make adult decisions. and they didn't even get to the good stuff.<p>data science is successful now because they are applying quantitative methods to the social sciences -- problems most people think about. the average educated person can understand by using the magic of "machine learning" we can solve problems in politics, health, social issues, etc.<p>Nate Silver's lack of a math background works to his advantage. he realizes the bar for quantitative understanding in the public sphere is very low. whatever little bit he does, he has to explain to a room full of drunk people in 30 seconds or less. he is very good at this.<p>neither employers nor the public want to admit that at the end of the day SOMEONE has to be responsible for these calculations. i have a master's degree in math -- which nobody gave a hoot about (see item 5 in this article <a href="http://gigaom.com/2013/04/16/how-to-hire-data-scientists-and-get-hired-as-one/" rel="nofollow">http://gigaom.com/2013/04/16/how-to-hire-data-scientists-and...</a> ) we weren't taught how to communicate our ideas and results to the public -- in fact we were discouraged from it. that is difficult to unlearn.<p>i've heard of attempts to "democratize" machine learning.
<a href="http://blog.bigml.com/2013/03/06/democratizing-machine-learning-with-c/" rel="nofollow">http://blog.bigml.com/2013/03/06/democratizing-machine-learn...</a>
articles like these are usually written by companies with a product or book to sell or distribute. what needs to be democratized is engineering based problem solving - using the powerful computers we hold in our pockets, our phones.
There is something that somewhat irks me about the whole domain of data science. I still can't quite put my finger on it, but I think it's a mixture of lack of rigor, handwaving arguments.<p>Maybe if it were called data journalism, or data storytelling rather than <i>science</i> I would somewhat lower my expectations and enjoy it more fully, but in my experience doing science and doing data science are not really close.
I feel like Data Science is not a real academic field, and it's not something you need a college degree in. The programs are so new and rudimentary, the universities don't really even know what to teach the students in an "Intro to Data Science" class. But it's a buzzword in the media, and there's employer demand now, so I think that universities saw the $$$ and just ran with it.<p>It's really more of a technical skillset and toolset for statisticians and other actual scientists who use statistics to test their hypotheses. Make Intro CS, Relational Algebra and Set Theory, and Computational Statistics electives for Bachelor of Science degrees. This really doesn't need its own degree program, and neither did "business intelligence" back when that was the buzzword for this. Universities aren't for "latest tools and fads" training.
<i>“… Getting your hands dirty with the data set is, I think, far and away better than spending too much time doing reading and so forth,” Silver said in a Q&A with HBR’s Walter Frick.</i><p>I'm in a data science class (hi carlob) right now. This is exactly how we've approached the subject. There were a few readings the first couple weeks to get people up to speed on Python (or scare them away if they didn't have any coding experience), then our homework problem sets have jumped straight into real-world data scraping and modeling.<p>It's been a fantastic way to start learning the field, because after just 3.5-ish weeks I already feel like I have the tools I need to start exploring and fitting rudimentary models to the information I'm collecting.<p>It's exciting and enabling, and I think that's definitely a strength of doing while learning.
Original source: <a href="http://blogs.hbr.org/2013/09/nate-silver-on-finding-a-mentor-teaching-yourself-statistics-and-not-settling-in-your-career/" rel="nofollow">http://blogs.hbr.org/2013/09/nate-silver-on-finding-a-mentor...</a>
Is there anyone around that "does data science" for a living that can talk about what it is they do exactly?<p>I'm under the impression that "data scientist" is as ambiguous a term as "designer". What's the minimum amount of math or statistics required to be labeled a data scientist? And what's the minimum amount of programming too?