Fundamentals of Data Science

Catalog description

Introduction to the knowledge acquisition and discovery process. Cleaning and analyzing data, building machine learning models, model validation and testing, and visualization. A number of machine learning algorithms are introduced such as regression, naive Bayes, decision trees, association rules, and clustering. Feature selection and transformation. Programming languages, popular in data science, such as Python are covered at an accelerated pace, as the course assumes as prerequisites two semesters of programming. Emphasis is on the use of such languages for data analysis and modeling.