New online master's degree to train the data scientists of tomorrow • Hacker News Board

Re: New online master's degree to train the data scientists of tomorrow

#51

Post by mathattack » Thu, Jul 18, 2013, 2:43 PM UTC

$60K seems like an awful lot for an on-line program. I think they are getting a lot of internal flack over the pricing. It seems to me that Berkeley should be jumping into on-line learning, as their state funding dries up. The best way to do this consistent with their mission is a mass market approach. By putting in Tiffany pricing, they're going to fail both their mission (training data science, educating the public, etc) and they won't bring in much money to fill any revenue holes.

Harvard's online masters degrees are closer to $20K all in.

Re: New online master's degree to train the data scientists of tomorrow

#52

Post by achompas » Thu, Jul 18, 2013, 3:28 PM UTC

xiaoma wrote: post #50

achompas wrote: post #36

Earlier quoted context omitted.

This is a recipe to get very good at basic analysis. It won't prepare you for the day-to-day responsibilities of a data scientist, though.

What do you feel would needed to be added to the mix in order to prepare a person for the day-to-day responsibilities of a data scientist? Also which of those responsibilities do you see as most challenging?

Yeah, sorry for the snarky one-liners. I wrote a bit more here:

https://news.ycombinator.com/item?id=6060821

There are two pieces Kaggle can't help you with: working through the full research cycle and developing performant models. It also emphasizes the wrong goals (for example error minimization is almost never your primary goal), but I need to work at some point and have spent enough time in this thread, so I'll skip that. :P Email me if you want to discuss, though.

Anyway Kaggle can't help with the full research cycle, since you're not identifying a relevant question yourself (this is surprisingly hard) or presenting your answer to others. The latter is hard for any route, since you really only encounter that type of volume in industry.

Re: New online master's degree to train the data scientists of tomorrow

#53

Post by fixxer » Thu, Jul 18, 2013, 4:04 PM UTC

bernardom wrote: post #49

fixxer wrote: post #22

Earlier quoted context omitted.

Cornell? What year? EDIT: I did mine at CU back in 2003 in applied, not FE. I had many friends in FE, most all went into credit. I worked in trading for six years before quitting for a PhD. I do not place any value on an FE degree; looking back at the curriculum they offered, it is obvious that they were thinking the wrong way (the credit models they were teaching were complete shit and they had no concept of micro-s…

B.S.'08, M.Eng '09 Henderson is one of my favorites; I just went back for my 5-year and he was just so wonderful to talk to. The FE curriculum never really interested me; I took OR methods in FE as an undergrad and the professor (a surfer-dude postdoc from UCLA, Will Anderson) convinced me that the efficient market hypothesis was mostly right. After that, FE seemed a little... well, in the words of my classmate Ryan,…

Mostly right is a mostly correct statement. Heard of Renaissance Technologies?

IMO, continuous time finance is a flawed model. Academics always rattle off theorems based on assumptions that do not hold across all time scales. I've worked with traders lacking any formal education that have a better understanding of the market than someone like Protter will ever have. Trading isn't about investing; it is about capital flows.

That said, I don't miss the job (only the $).