Well written, but I believe you missed the point of the original article. No one ever claimed that taking one class made someone an expert "data scientist." In

Well written, but I believe you missed the point of the original article. No one ever claimed that taking one class made someone an expert "data scientist." Instead, that single class wetted Luis, Jure, and Xavier's (the three competition winners) appetites, and pushed them more to learn more about machine learning and natural language processing. They then went on to dive much deeper, and excelled specifically in one area of applied NLP. However, without that first class, there's a good chance none of them would have ever focused on (or heard of) machine learning. Their story is growing increasingly common. Like the Netflix Prize, Andrew Ng's first Coursera class did its part in shining a spotlight onto our dark little corner of the universe. I'd be very cautious about a long checklist of items that are necessary to be a successful data scientist (which is a pretty ill-defined and encompassing term at this point). That is a decent summary of many useful tools of the trade, but they are by no means useful for all problem domains. For example, I could spend years working on machine learning for EEG brain-computer interfaces without a good reason to use databases or "big data" NoSQL technologies. I especially enjoyed MSR's take on the matter in "Nobody ever got fired for using Hadoop on a cluster" http://research.microsoft.com/pubs/163083/hotcbp12%20final.pdf http://research.microsoft.com/pubs/163083/hotcbp12%20final.p... When we're hiring data scientists or seeking successful ones, we've found focusing on demonstrated excellence in one relevant area plus general quantitative competencies and the curiosity and tenacity to learn new tools and techniques works far better than a laundry list of skills and experiences.