To study computer science methodology that contributes to both theoretical and applied statistics.
* This is an undergraduate course that can be taken for graduate credit.
Review of key notions from probability and statistics.
Introduction to exploratory data analysis: the art of interrogating data to generate hypotheses; displaying, summarizing, and comparing sets of data.
Using data analysis to motivate statistical procedures (estimation and hypothesis testing, regression, analysis of variance, time series)
A study of various computer implementations as found in libraries of statistical programs (BMD, SAS, SPSS).
Some applied multivariate techniques (cluster analysis, multi-dimensional scaling, principal components).
Computer methods in theoretical statistics: random number generation; Monte-Carlo methods; generation of plausible statements for possible theorems.
Frequent, small computer assignments, a mini-project, and a project, either applied or theoretical. Applied projects perform significant analyses on interesting data sets, either supplied by the instructor or brought in by the student. Theoretical projects use computer techniques to investigate statistical questions of a theoretical nature.
- will be prepared to contribute to a rapidly changing field by acquiring a thorough grounding in the core principles and foundations of computer science (e.g., techniques of program design, creation, and testing; key aspects of computer hardware; algorithmic principles).
- will acquire a deeper understanding on (elective) topics of more specialized interest, and be able to critically review, assess, and communicate current developments in the field.
- will be prepared for the next step in their careers, for example, by having done a research project (for those headed to graduate school), a programming project (for those going into the software industry), or some sort of business plan (for those going into startups).