Statistics plays an important role in our lives. Successful statistical data analysis relies increasingly on using computers. As datasets and dimensionality increase in size the computational element of data analysis takes on a critical role. In these cases, statistical inference requires carefully crafted solutions that are computationally efficient and numerically accurate.
This is an introductory course in computational statistics. As such, it will cover basic concepts from both computer science as well as statistics. The course has several broad aims:
47822 STAT 59800 - SK1 (598G Lecture)
47998 STAT 59800 - SK2 (598G1 Lab)
Monday, Wednesdays, and Fridays, 12:30-1:20pm, B286 Beering Hall
Monday 4:30-5:20pm, B282 Beering Hall
An introductory course on probability and inference (e.g., STAT 516 and STAT 517) is required; basic course on linear algebra (e.g., MATH 511) is required; some prior programming and debugging experience is required (not necessarily in C or R). Please see the instructor early if unsure of your preparation.
For grading, policies, and what to expect, please read the syllabus. Some of the additional questions are answered in FAQ.