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IMO, as companies' data science capabilities mature, people with actual skills are needed. 5 years ago DS was hot and new. As a middle manager you would call u

IMO, as companies' data science capabilities mature, people with actual skills are needed. 5 years ago DS was hot and new. As a middle manager you would call up a consulting meat farm and ask for data science consultants: presto, you have a data science team. These people would, likely, be random BSc's with a SAS base certificate. In 2018 data science, and data driven decision making, is still hot, but hiring managers know they need people with a formal education. So, education reqs go up and scarcity increases.

compcoffee 8y ago
>but hiring managers know they need people with a formal education It's the exact opposite: as better tools are developed, there is less "formal education" required. In the same way that Web Devs are in high demand, and nobody needs a Computer Science degree to do that work. The reality is, "data driven decision making" is something businesses large and small are discovering they need. And often times that sort of analysis requires a regression, or maybe something slightly more sophisticated. What these businesses do not need is some cutting edge algorithm or complex AI pipeline. There is a ton of gate-keeping in this space, as the the PhDs learn that domain experience and business acumen are more important than a deep knowledge of the algorithms. A significant amount of the work is cleaning and processing the data. This is the way of the world. At the end of the day, most business problems boil down to inference and prediction, and most times you don't need to be incredibly precise; the easy 80% will do.
conjectures 8y ago
+1 because mooc style learning alone just isn't enough in most cases.
- albemuth 8y ago
  What would you say is missing from a mooc-based training?
  - conjectures 8y ago
    Having looked through the contents of a couple of data science moocs they often try to give intuition without math. When trying to put it into practice, if we go the slightest distance off piste we are lost without the math. So it's a fragile form of knowledge. If we take someone who's already strong at math from some other training and teach them more stats/ML concepts and intuition, then great. But the value add of the mooc learning is quite low in that scenario. The best alternatives (barring a formal course) imo, are text books that have exercises like Bayesian Data Analysis by Gelman.

compcoffee 8y ago

>but hiring managers know they need people with a formal education It's the exact opposite: as better tools are developed, there is less "formal education" required. In the same way that Web Devs are in high demand, and nobody needs a Computer Science degree to do that work. The reality is, "data driven decision making" is something businesses large and small are discovering they need. And often times that sort of analysis requires a regression, or maybe something slightly more sophisticated. What these businesses do not need is some cutting edge algorithm or complex AI pipeline. There is a ton of gate-keeping in this space, as the the PhDs learn that domain experience and business acumen are more important than a deep knowledge of the algorithms. A significant amount of the work is cleaning and processing the data. This is the way of the world. At the end of the day, most business problems boil down to inference and prediction, and most times you don't need to be incredibly precise; the easy 80% will do.

conjectures 8y ago

+1 because mooc style learning alone just isn't enough in most cases.

albemuth 8y ago
What would you say is missing from a mooc-based training?
- conjectures 8y ago
  Having looked through the contents of a couple of data science moocs they often try to give intuition without math. When trying to put it into practice, if we go the slightest distance off piste we are lost without the math. So it's a fragile form of knowledge. If we take someone who's already strong at math from some other training and teach them more stats/ML concepts and intuition, then great. But the value add of the mooc learning is quite low in that scenario. The best alternatives (barring a formal course) imo, are text books that have exercises like Bayesian Data Analysis by Gelman.