Statistical inference and hypothesis testing in data science applications

This course will focus on the theory and implementation of hypothesis testing, particularly as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions about data. Particular attention will be given to the general logic of hypothesis testing, errors and error rates, power, modeling, and the proper calculation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, particularly p-values, and the ethical implications of such misuse. This course may be taken for academic credit toward CU Boulder’s Master of Science in Data Science (MS-DS) program, offered on Coursera. The MS-DS is an interdisciplinary degree that includes faculty from the Departments of Applied Mathematics, Computer Science, Information Science, and other CU Boulder departments. An outcomes-based program with no application requirement, the MS-DS program is ideal for individuals with broad educational and/or professional experience in computer science, computer science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.