Introduction to Data Science

Catalog description

Explores the fundamentals of data science using modern programming tools and software. Applies the data science workflow including importing, cleaning, transforming, and visualizing data, to uncover insights and communicate findings effectively. Topics include data wrangling, exploratory data analysis, ethical considerations in handling data, and basic statistical inference. Emphasizes hands-on learning through real-world datasets and projects designed for students with no prior programming experience. Builds foundational skills for working with data in a variety of fields. Prerequisite: C or better in MATH 116, 16B, or 118, or successful completion of the QL/Stats transitional math course (TM-002) or the STEM transitional math course (TM-001) at an approved high school, or placement by exam. (PCS 1.1, 3 credit hours: 3 hours lecture, 0 hours lab)