My dream job using this masters degree in statistics is to work in analytics for a sports team (preferably basketball or football). That being said, though, the number of professional and high-level NCAA teams is not growing whereas the demand for data scientists does seem to be consistently growing. Therefore, I see myself very likely getting into data science because that is where the jobs are likely to be. I would then try to do some volunteer or consulting work for a sports team to perhaps eventually break into that area. I just do not think it is realistic for me to think I could go get a job as a Director of Analytics or even an analyst for some NBA team right off the bat. I like the description of data scientists that is offered in the assigned article “Data Scientists vs Statisticians,” which is that they are practicioners that follow a particular process pretty closely. Specifically this is the “data science process,” which consists of several steps that are pretty straightforward to understand. These steps are, again per the same article: “data ingest, data transformation, exploratory data analysis, model selection, model evaluation, and data storytelling. I interpret this process as meaning that data scientists take in data into their chosen analytical framework, transform it if neccessary in order to do some initial analysis of the data before selecting what appears to be the most appropriate model based on that initial analysis. evaluating the predictive performance of that model and then finally wrapping the analysis into a narrative that can be presented to key business leaders in a way that is understandable, even for those who do not have a strong technical background. One obvoius different between data science and statistics is that data science tends to deal with much larger data sets that are imported for analysis whereas a statistician might aquare data from an experiment or survey. Another difference is that statisticians are much more focused on quantifying the uncertainty associated with their calculations/estimations. Data scientists are, rather, primarily foccused on prediction. I think the biggest area of similarity between data science and statistics is simply that the “science” in data science is in fact statistics - statistics is in the background of virtually everything a data scientist does. I think this fact is what leads critics such as Nate Silver to cliaim that data science is nothing but a fancy word for applied statistics. I do not buy this argument though - I think that the usefulness and impact of data science is sufficient to warrant its own term.