Guest blog post by Laetitia Van Cauwenberge

Salary mostly depends on experience, education, location, industry, and unfortunately, factors such as gender. Also, most data scientists have all the three skills and more (R + Python + SQL), so it is hard to assess which one is the most valuable.

Source for picture: click here (numbers are from 2014)

You could do a survey, asking data scientists which skills they were hired for, and break the results down into 8 categories:

• no R, no Python, no SQL
• R, no Python, no SQL  
• no R, Python, no SQL  
• no R, no Python, SQL
• R, Python, no SQL
• R, no Python, SQL
• no R, Python, SQL
• R, Python, SQL

And then compute the average salary in each category, maybe broken down by education / experience / location. Or you can think logically, concluding that

• Python can do everything (web development, data science, and so on), in particular in production mode, has great data science libraries, and thus commands the highest salary boost
• R is somewhat specialized and limited to statistical analysis, thus commanding a lower salary boost
• SQL is extensively used and a very popular skill; in addition SQL queries are much easier to automate or outsource than Python coding, thus commanding the lowest salary boost. Though not knowing SQL might mean no job offer even if you are an expert in Python or R.

Then you can do some search using Indeed.com, and get salary per location for these three skills. Below are numbers for San Francisco, as of April 2016 (big contrast with the above picture corresponding to 2014, but the numbers below are for all job ads including architects and software engineers, not just data scientists). The number in parentheses represents the number of job openings.