Overview
Description
This course introduces data science with the R language and environment. The R language is taught through the pipeline for statistical analyses including importing, tidying, transforming, wrangling, visualizing, modeling, and communicating. Topics in statistical analyses will primarily include hypothesis testing and supervised and unsupervised regression and classification machine learning statistics. RStudio is used to introduce students to tools for creating reproducible and dynamic reports.
Units
Lecture3
Catalog Details
Offering
Offered: Every Spring - Odd Years
Terms
spring
Attributes
Standard
Learning Outcomes
- demonstrate fluency in the R environment and language.
- identify the appropriate statistical analyses depending on the data and research questions.
- conduct independent research and analysis.
- apply algorithms and report the expected accuracy of the models.
- defend the statistical veracity of their conclusions.