Introduction to Data Science

Catalog description

This course examines key components of the data science lifecycle including data collection and cleaning; exploratory data analysis and visualization; and extracting insight via statistical models and machine learning algorithms for regression, classification, and clustering. Algorithmic efficiency and scalability are emphasized, and techniques for working with big data are introduced. Students use a modern programming language (e.g., R, Python) with appropriate packages suitable for data analysis. This course is taught largely at an undergraduate level. Graduate students have additional course requirements/expectations. Prerequisite: CS 340 ; MTH 207 ; junior standing. Offered Occasionally.