At the beginning of a course called “Data Science for Statisticians” it is useful to take a step back and spend some time thinking and reading about what a data scientist actually is. The best, simple summary seems to be that a data scientist is someone who is able to take a knowledge of statistics and a knowledge of computer science and combine the skills of both fields to process data and make predictions. Data scientists and statisticians use some different techniques and focuses, and they can complement each other and often need each other’s help in solving problems. A data scientist is typically dealing with very large datasets, so they need to have substantial knowledge in dealing with and cleansing massive amounts of data. Data scientists also tend to be more interested in the predictive ability of a model and less interested in quantifying uncertainty than a statistician may be. To be a good data scientist, a person needs to have strong coding abilities and a good applied statistics background. I view myself as a statistician who can gain a lot by having the knowledge of some of the tools and methods used by data scientists. I see developing an understanding of the way data scientists approach data and models as not just a way to add skills for if and/or when I need to deal with a big dataset, but as a way to expand my ability to think about problems in different ways.

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