Statistical Inference and Hypothesis Testing in Data Science Applications
This course is part of Data Science Foundations: Statistical Inference Specialization
Instructor: Jem Corcoran
What you'll learn
Skills you'll gain
There are 6 modules in this course
This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
Fundamental Concepts of Hypothesis Testing
Composite Tests, Power Functions, and P-Values
t-Tests and Two-Sample Tests
Beyond Normality
Likelihood Ratio Tests and Chi-Squared Tests
Explore more from Probability and Statistics
©2025 ementorhub.com. All rights reserved