Becoming a data scientist might be easier than you think

Meh. 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.<p>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.<p>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 <i>a lot</i> 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.

<i>Bad programming is easy. Idiots can learn it in 21 days</i>..<p>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.<p>The course itself is a brilliant work of a passionate top-of-the-field professional. No wonder coursera.com is such a huge success.

In an interesting turn of events, the article about machine learning was trolled by a bot.

If you are an actuary you are already a data scientist.

I 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.

Did anyone else notice the comments on this article?

This 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.

Which 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.