Full course description
Course Overview
This is the sixth course in the Statistical Analysis and Data Visualization part of the Data Science and Business Analytics program. Building on your understanding of inferential statistics, this module focuses on confidence intervals and hypothesis testing—core techniques for making evidence-based decisions using data. You will learn how to assess uncertainty in estimates and test assumptions about populations using statistical methods and Python.
Why enroll in this course?
Whether you're analyzing customer behavior, evaluating product performance, or making strategic business decisions, the ability to draw conclusions from sample data is critical. This course provides the foundational tools to estimate population parameters and test hypotheses with confidence. You’ll gain practical skills in applying t-tests, proportion tests, and chi-square tests. These are essential techniques in any analyst’s toolkit.
What will you learn?
By the end of the module, you will have learned:
- How to construct and interpret confidence intervals
- The steps and logic of hypothesis testing
- How to perform one-sample t-tests and proportion tests
- How to compare two or more populations using statistical tests
- How to apply these methods in Python through guided demonstrations
Structure
The course is completely self paced. It will take you approximately 15 hours to complete all three modules. Activities include, video lessons, readings, and self reflection activities. Upon successful completion of this course, you will receive a certificate of completion.
Smith Executive Education Homepage | Contact us: rhsmith-execed@umd.edu