This course is a follow-up course to an introductory course in probability and statistics. Officially called "Strategies for Process Investigations", I took this course as a senior in my chemical engineering program. The course description is as follows (chemical engineering things italicized):
This course is designed to give you a more comprehensive understanding of how models are estimated from data, and how experimental programs can be designed to make the resulting data as informative as possible. The focus of the course is largely on empirical models, i.e., models that are estimated from data. However, the techniques for estimating parameters, making decisions about parameters, and planning experiments also apply equally to fundamental or first-principles models.
The objectives of the course are:
- To provide you with a strong background for developing empirical models between process variables through model building, including multiple linear regression with emphasis on evaluation and interpretation of the resulting model.
- To provide you with basic techniques for the initial screening of process variables including 2-level, complete and fractional, factorial designs, and higher-order experimental designs.
How Is It Useful?
Statistics is one of the pillars of data science. Check out this Quora page for a great answer to the question "How do data scientists use statistics?"