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Becoming a data scientist might be easier than you think
- betawolf33 14y agoDid anyone else notice the comments on this article?
- ahi 14y agoIf you are an actuary you are already a data scientist.
- EzGraphs 14y agoYeah - particularly the mathematical/statistical side of things. But a big part of the profession is software development / computer science. An actuary might construct useful models and choose the proper techniques to analyze data, but choose inferior technologies or suboptimal implementations that won't work for large data sets. A lot of actuaries spend most of their life inside of Excel. Excel won't cut it with Big Data.
- hessenwolf 14y agoA big part of the profession, and zero part of the training in the profession. Actuaries write worse code than electrical engineers.
- nesu 14y agoData Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician. source: http://blog.kaggle.com/2012/10/04/engineering-practices-in-data-science/ http://blog.kaggle.com/2012/10/04/engineering-practices-in-d... Software development is important, but it's not everything. People choose what works and use it. Though I agree that spreadsheets won't always work on Big Data, there's no point in doing more if you can achieve it with less.
- pitiburi 14y agoIn an interesting turn of events, the article about machine learning was trolled by a bot.
- dschiptsov 14y agoBad programming is easy. Idiots can learn it in 21 days.. I'm one of those who actually completed this course (with a score of 73.10/780) but it doesn't make you a data scientist. It is only the very beginning. The course itself is a brilliant work of a passionate top-of-the-field professional. No wonder coursera.com is such a huge success.
- Evbn 14y agoIs 73 out of 780 a good score?
- dschiptsov 14y ago73 out of 80 and 780 out of 800.)
- amalag 14y agoI guess the professor isn't kidding when he says in the videos. "After you finish this course, you will know as much or more than the silicon valley programmers doing machine learning" The material he presents is quite distilled. He gives a lot of real examples, but the programming exercises are sort of fill in the blanks. A lot of the hard work is done. You can still learn a lot though.
- waterlesscloud 14y agoThis is a crucial step for the mooc's: students who have completed their classes and go on to real world achievements. It's precisely the way that a school builds a reputation, by the success of its students.
- marshallp 14y agoWhich brings up the question of what all these academic machine learningists (especially the theorists) are up to? Why aren't they winning kaggle? Why aren't Andrew Ng's own grad students collecting the prizes? Some self-reflection needs to go on.
- imgabe 14y agoMaybe they are spending their time conducting research instead of entering contests? I'm not trying to be snarky, but do you know that they're even entering the contests?
- marshallp 14y agoWhy are they not entering contests? What is the point of their research if it isn't to engineer better algorithms for the task of machine learning? How do they prove that their research led to better algorithms?
- imgabe 14y agoI would imagine they publish papers that are reviewed by other academics. It is possible to use their algorithm on a dataset without entering a contest to do so. It's a little like asking why Electrical Engineering PhDs are not designing the new iPhone (probably some are involved somewhere, I know). Research and applications are two separate endeavors.
- marshallp 14y agoThe researchers actually do set up competitions to see who is best. However, there are few entrants. In kaggle there are 1000s of competitors, so it's a better judge of whether they are legitimately improving the state of the art (rather than simply schmoozing their local funding agencies).
- Evbn 14y agoOh, just noticed the username. I hope someone hires Marshall soon, or he finishes the book he is working on , or whatever, so this long form trolling can come to conclusion.
- rm999 14y agoMeh. I've said it before, and I'll say it again: those contests aren't necessarily a good indicator of who will be a good data scientist, in the same way programming contests tell you little about who will be a good software engineer. Being a good data scientist requires a lot more than machine learning, including a solid understanding of the business side (deep domain knowledge), the ability to write production-grade software and tools, scripting/hacking/data munging, math/statistics, and common sense. Running a sanitized dataset through machine learning algorithms is maybe 5-10% of it. I'm not trying to discourage people, I'm thrilled so many people are taking an interest in data sciences and I want to push interested people in a direction where they can excel at it. But this article is dangerous - becoming a data scientist requires a lot of hard work. I've seen a large sample of people (through interviews) who think a single online class is enough to get into the field. It's a great start, but if you want to be valuable you need a wider set of skills.
- marshallp 14y agoAll the evidence from kaggle indicates that deep domain knowledge is not required. Jeremy Howard has some youtube videos discussing this. Pretty much all the skills you outlined (except for production grade code - which is a software engineer problem, not a data scientist problem) are covered by the contest.
- rm999 14y ago> All the evidence from kaggle indicates that deep domain knowledge is not required That's irrelevant, data scientists don't do data mining contests for a living. In my experience finding the right question to answer is a large chunk of data science, and that is never spelled out for you like it is in a contest.
- marshallp 14y agoYou're saying kaggle is fundamentally different to what data scientists do? I don't understand that. The right question is usually how do I increase profits (or score this essay/image etc). So simply set up your problems that way.