Statistical and Data Sciences 390 - TOPCS: ADV PROGRAMMING

Topics in statistics and data science. Statistical methods for analyzing data must be chosen appropriately based on the type and structure of the data being analyzed. The particular methods and types of data studied this in this course vary, but topics may include: categorical data analysis, time series analysis, survival analysis, structural equation modeling, survey methodology, Bayesian methods, resampling methods, spatial statistics, missing data methods, advanced linear models, statistical/machine learning, network science, relational databases, web scraping and text mining. This course may be repeated for credit with different topics. Prerequisites: MTH/SDS 290 or MTH/SDS 291 or MTH/SDS 292. : Advanced programming techniques for data science using R. This course is not about data analysis---rather, students will learn the R programming language at a deep level. Topics may include data structures, control flow, regular expressions, functions, environments, functional programming, object-oriented programming, debuggging, testing, version control, documentation, literate programming, code review, and package development. The major goal for the course is to contribute to a viable, collaborative, open-source, publishable R package. Prereqs: SDS 192 and CSC 111. (E)