Read this: http://www.forbes.com/sites/emc/2014/06/26/the-hottest-jobs-in-it-training-tomorrows-data-scientists/

Interesting subject areas: Statistics, Machine Learning, Algorithms.

I've had questions about data science from folks who (somehow) felt that
calculus and differential equations were important parts of data
science. I couldn't figure out how they decided that diffeq's were
important. Their weird focus on calculus didn't seem to involve using
any data. Odd: wanting to be a data scientist, but being unable to
collect actual data.

Folks involved in data science seem to think otherwise. Calculus appears to be a side-issue at best.

I can see that statistics are clearly important for data science.
Correlation and regression-based models appear to be really useful. I
think, perhaps, that these are the lynch-pins of much data science. Use a
sample to develop a model, confirm it over successive samples, then
apply it to the population as a whole.

Algorithms become important because doing dumb statistical processing on
large data sets can often prove to be intractable. Computing the median
of a very large set of data can be essentially impossible if the only
algorithm you know is to sort the data and find the middle-most item.

Machine learning and pattern detection may be relevant for deducing a
model that offers some predictive power. Personally, I've never worked
with this. I've only worked with actuaries and other quants who have a
model they want to confirm (or deny or improve.)

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