Re:How to Become a Data Scientist, Part 2
by moseandre on Sunday August 07, 2016 @07:14PM
(#12244287)
It was a statistics class where I learned a linear time median algorithm. But that was pretty lucky, I admit.
95% of undergraduate statistics education is focused on formal inference. Data science, in my experience, involves a lot more exploratory data analysis [1] than formal inference (frequentist or Bayesian).
The extreme focus on inference and the hypothesis testing step in the scientific method is something people with a formal statistics education have to overcome to be productive data scientists. Or applied statisticians, really! It is more important to understand the data, organize it creatively, and find unexpected structure.
See, here's the deal. Numerical literacy (numeracy) comes about through many processes, one of which is obtaining a statistics degree.
However, not many fresh statisticians could tell you or me the expected runtime of a taking a numerical field's median on a large database. Why? Because the backgrounds are different. Yet, for a solid data science team, you'd like at least someone who can give insight as to whether your project will take minutes or decades to complete. This (contrived but important) example suggests that while a BS in statistics is an important and necessary component of a data science team, it not sufficient.