About this degree
Data science is applicable to a myriad of professions, and analyzing large amounts of data is a common application of computer science. This course empowers students to analyze data, and produce data-driven insights. It covers all areas needed to solve problems involving data, including preparation (collection and integration), presentation (information visualization), analysis (machine learning), and products (applications).
This course is a hybrid of a computing course focused on Python programming and algorithms, and a statistics course focusing on estimation and inference. It begins with acquiring and cleaning data from various sources including the web, APIs, and databases. Students then learn techniques for summarizing and exploring data with spreadsheets, SQL, R, and Python. They also learn to create data visualizations, and practice communication and storytelling with data. Finally, students are introduced to machine learning techniques of prediction and classification, which will prepare them for advanced study of data science.
Throughout the course, students will work with real datasets (e.g., economic data) and attempt to answer questions relevant to their lives. They will also probe the ethical questions surrounding privacy, data sharing, and algorithmic decision making. The course culminates in a project where students build and share a data application to answer a real-world question.
