Data Analysis

Graduate / Professional

Selected topics in statistics explored through analysis of data sets using the R statistical computing language. Topics include linear and nonlinear models, maximum likelihood, resampling methods, curve estimation, model selection, classification, and clustering. Extensive use of the R programming language. Experience with R programming (from e.g. S&DS 1060 , S&DS 2200 , S&DS 230, S&DS 2420 ), probability and statistics (e.g. S&DS 1060 , S&DS 2200 , S&DS 2380 , S&DS 2410 , or concurrently with S&DS 2420 ), linear algebra (e.g. MATH 2220 , MATH 2250 , MATH 1180 ), and calculus is required. This course is a prerequisite for S&DS 4250 and may not be taken after S&DS 4250 . QR TTh 2:35pm-3:50pm