Skip to content Skip to navigation

Computer Methods in Statistics*

01:198:425

To study computer science methodology that contributes to both theoretical and applied statistics.

Credits: 
4

This is an undergraduate course that can be taken for graduate credit.

Prerequisite: 

01:198:206; CALC2. Recommended: 01:198:323 or 01:640:373.

Please note that courses for which a student has received a grade of D cannot be used to satisfy prerequisite requirements.

Topics: 

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.

Expected Work: 

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.

Exams: 
None
Learning Goals: 
Computer Science majors ...
  • 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).
Course Type: 
Undergraduate & Graduate

Check the University Schedule of Classes to see if this course is open.

Request an Special Permission Number here if the class is full.