Principles and Techniques of Data Science

Catalog description

Explores the data science lifecycle, including problem formulation, data collection and cleaning, exploratory data analysis and visualization, decision-making, and basic machine learning. Focuses on practical aspects of data pre-processing, exploration, and visualization to make inferences and predictions. Prerequisite(s): CS 311 or admission to CS graduate program. Prerequisite(s)/Corequisite(s): MATH 242 or MATH 440 or MATH 442 or admission to CS graduate program.