Data Science Principles with R

Catalog description

This course introduces the fundamental principles and techniques of data science, equipping students with the ability to extract insights, build models, and solve problems. Using the R programming language, students will explore data acquisition, wrangling, cleaning, manipulation, visualization, exploratory data analysis, data ethics, statistical modeling and inference, machine learning, and simulation. The course emphasizes the pipeline of data science and the interface between statistics and computing with real world applications. It does not provide introduction to individual statistical models and machine learning algorithms, which will be done in other courses. Additional topics if time permits include Database Querying, Text Mining, Geospatial Data, and Network Science. Prerequisite: A grade of C or better in Math 304 and either Math 327 or Math 447 (Math 447 may be taken before or in the same semester as Math 445). Offered regularly.