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Careers and Graduate School in Statistics, Biostatistics, Data Science, and other data-analytical paths

Statistics, biostatistics, and data science are closely related fields. Broadly, they all teach a set of mathematical and computational skills for analysis and modeling of data, as well as interpretation and visualization of results. It is difficult to summarize differences among these fields without over-simplifying a complex domain of knowledge. Indeed, the differences between programs in the same field are likely as large as the differences among the fields themselves. Importantly, however, the skills learned through the study of any of these fields will prepare students for a similar set of exciting, well remunerated, and impactful careers in industry and prepare students for further pursuits in academics.

In the table below, we summarize a minimum set of pre-requisite courses that will help students be competitive in admission to top graduate programs in these fields. We believe that the courses that prepare students for masters level study will also serve to make students competitive for jobs in industry.

This summary was created from a survey of a dozen programs in statistics and biostatistics, and more than 15 programs in data science. We recommended a course (with a check ✓) if the programs we surveyed nearly unanimously listed the course as a prerequisite. However, these requirements should be taken as a minimum starting point. Broadly, programs in statistics, biostatistics, and data science want to (1) primarily see a high level of mastery in upper-level math courses, and (2) secondarily see a comfort with programming and comp. sci. topics.

Five column table displaying recommended completed courses prior to a Masters or PhD for a dozen programs in statistics and biostatistics, and more than 15 programs in data science.
Course Example W&M Course Masters in Data Science Masters in Statistics or Biostatistics PhD in Statistics or Biostatistics
Calc I, II, and III MATH 111/131, 112/132, 212/213
Intro. Linear Algebra MATH 211
A Second Linear Course MATH 309 or 408
Intro. Analysis MATH 311
Probability (Calc III-based) MATH 451
Mathematical Statistics MATH 452
Applied Data Analysis MATH 352, MATH 459
Intro to Computing CSCI 140/141
More Comp. Sci. courses  e.g. Data Structures, Databases, Algorithms CSCI 241 or CSCI 303 or CSCI 421