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What I learned applying data science to data science salaries

I recently did an analysis on what factors help increase data science salaries. Here is some of what I learned helps increase your data science salary: 

1) Learning cloud computing and big data tools. They are strongly correlated with positive salary outcomes.

2) Embracing a multitude of new technologies--aiming to learn a diversity of data science tools.

3) The ability to negotiate, and the willingness to always negotiate salary.

4) Geographically, when adjusted for cost of living, you'll want to live in San Jose. Failing that, San Francisco or Seattle will do just fine. 

5) Unfortunately, data science still has a bias when it comes to a gender gap in pay. 

Here are my findings in a graphical form :)

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Comment by Roger Huang on September 18, 2016 at 7:42pm

That's all fine and good, but this was a look at what factors increase data science salaries. If you want to maximize data science salaries, the research is quite clear: open source ecosystems beat proprietary ones. 

Comment by Sione Palu on September 13, 2016 at 8:26pm

Quote :  "Stay away from proprietary tools and go towards open-source"

WHY?

The user should use the tool that suits his/her analytical tasks at hand.

Matlab is my main tool which is commercial but the open source APIs & free-codes available in matlab is huge. Huge than any known tool. So, why sacrifice using a small subsets of available techniques todate when one can explore latest state of the art techniques. The algorithms currently available in Spark is equivalent to Excel on steroids. What I mean is that the techniques is limited, which means the analyst is only exposed to limited types of tasks he/she can model. I'm sure that available techniques in Spark will grow, but for now, it is limited.

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