To address the other poster regarding how to get into being a statistician in pharmaceutical industry, you would need to complete a PhD in statistics or biostatistics. To get in, you need two semesters of real analysis (which is proof-based calculus), linear algebra (at least one semester, preferably two), the calculus I-III, two semesters of calculus based probability. These are usually the minimum math requirements to get in, and you need to try to get almost all A's in those classes (with real analysis and calculus based probability looked at most closely). Then almost all schools require the general GRE (but math section is high school and more about making few mistakes); verbal is challenging but as long as you don't do too bad you are OK. Only two schools require the math subject GRE test (the PhD statistics programmes in Stanford and the University of Chicago). The PhD is usually a five year programme and tuition is waived almost all of the time with a living expense stipend. If you stop at masters degree in statistics, you can get a job as a SAS (or statistical) programmer. This does not initially pay that well but it can approach the pay package of a statistician with experience; however, career trajectory is slower. However, SAS programming is very different from software engineering. SAS is not an algorithmic language and is simply about knowing procedures and syntax, and how to write SAS macros, and manipulate and merge datasets (which can be quite tricky), in addition to knowing CDISC regulations for NDA submission to FDA. For someone who really wants to be a software engineer and likes the algorithmic aspects of coding, I would not recommend being a SAS programmer. For somone who is still an undergrad, there are six schools that give research programmes. Getting into one of these and completing the program would help one's graduate application, especially if you applied to the school where you completed the programme. Summer Institute in Biostatistics and Data Science | NHLBI, NIH. Finally with PhD in statistics you could also go to the tech industry as a data scientist, but this requires a different type of PhD thesis in machine learning and also learning SQL thoroughly to pass the interviews. And then of course there are the academia options.
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