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The Big Data Zone is presented by Exaptive.  Learn how rapid data application development can address the data science shortage.

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.)

The Big Data Zone is presented by Exaptive.  Learn about how to rapidly iterate data applications, while reusing existing code and leveraging open source technologies.

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Published at DZone with permission of Steven Lott , DZone MVB .

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