Full course description
Course Overview
This is the fifth course in the Statistical Analysis and Data Visualization part of the Data Science and Business Analytics program. After exploring data through visualization and exploratory data analysis (EDA), this module introduces inferential statistics—an essential approach for making data-driven decisions when analyzing samples rather than full datasets. You’ll learn how to use probability and sampling techniques to draw meaningful conclusions about larger populations from smaller data samples.
Why enroll in this course?
In many real-world scenarios, analyzing an entire dataset isn’t practical due to time or resource constraints. This course equips you with the statistical tools needed to work confidently with sample data and infer population-level insights. You’ll develop a foundational understanding of probability, random variables, and the central limit theorem—key concepts for anyone working with data.
What will you learn?
By the end of the module, you will have learned:
- The basics of probability and why it matters in inference
- The difference between discrete and continuous random variables
- How to calculate and interpret mean and variance for random variables
- How to apply the central limit theorem in data analysis
- Key sampling methods used to generate representative data subsets
Structure
The course is completely self paced. It will take you approximately 15 hours to complete all four 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