As the number of our digital traces continues to grow, so does the opportunity for discovering meaningful patterns in these traces. In this course, students will initially learn how to collect, clean, format, and store data from digital platforms. By adopting a computational approach to statistical analysis, students will then implement in code different statistical metrics and simulation scenarios for hypothesis testing and estimation. Finally, students will generate meaningful visualizations for data exploration and communicating results. Additionally, we will discuss the ethics of data collection and think critically about current practices of experimenting with online users. Students will work in groups to create their own datasets, ask an interesting question, perform statistical analyses and visualizations, and report the results.

Units: 1

Max Enrollment: 24

Prerequisites: CS 230 or permission of the instructor.

Instructor: Mustafaraj

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Degree Requirements: DL - Data Literacy (Formerly QRDL); DL - Data Literacy (Formerly QRF)

Typical Periods Offered: Fall

Semesters Offered this Academic Year: Not Offered

Notes: