Data Science

Not that guy, but I just want to say that a bio/math double major with stats will land you any cushy bioinformatics job you could ever want. If you know how to code or some computer science type stuff, you're especially in the gold because that's one of the hottest fields to be going into right now.

NYU has a Center for Data Science lead by Yaun Lecun. I think arguing against the term is hipster.

What statistical concepts have you found most useful in your line of work? What do you do on a daily basis?

>focus on analyzing tho
sorry for the hijack, but is this common for individuals hired with msc in stats to do more analysis than ground work?

>applies for job
>"Hi Mr user, have you got 5 years experience in the field X?"
>"No? Hum, you are not exactly who we are looking for. Good luck"

I'm in an introductory course, we have covered PCA and association rules so far. I think we are going cover decission trees now

If you have to present results to your superior, there is not a lot of stuff you can do. I usually just put some graphs up cause they barely know what a t-test is (yes this is common). Also very important are weighting techniques, linear and multiple regression. And you better fucking understand sampling theory and how to manage complex sample designs if you want to do a good job.

It's my first job so I don't really know but I guess not.

I poke around the TensorFlow library for machine learning. This is a good intro: libgen.io/book/index.php?md5=7C4333E23A6BF64508F7C848E13CE5FB it also gives you a mathematical crash course in machine learning.

If you like ML, try this:
cs.cmu.edu/~15150/lect.html (SML, as semester goes on this will fill with lectures)

cs.cmu.edu/~rjsimmon/15411-f15/schedule.html (Compiler Design in ML)

Learning about compilers translates into interesting jobs, specifically in security research which hires data scientists all the time too.

Wait, you meant ML as in machine learning not standard ML disregard.