Full course description
Course Overview
This course explores linear regression within an inferential framework, focusing on how statistical models can be used to understand relationships in data and support evidence-based decision-making. You will begin by examining how data is explored, prepared, and analyzed, and how these steps inform both explanation and prediction.
Building on these foundations, the course introduces key statistical learning methods used in business contexts, including linear and logistic regression, classification techniques, and model evaluation approaches. You will also explore methods such as decision trees, k-nearest neighbors, and clustering to broaden your understanding of both supervised and unsupervised learning. Throughout the course, you will apply these techniques to real-world scenarios, developing the skills needed to interpret model outputs and assess their reliability in practical settings.
Why enroll in this course?
Strengthen your ability to not just build models, but understand and explain them. This course emphasizes the inferential side of machine learning, helping you draw meaningful conclusions from data and communicate insights with confidence.
Whether you are a data analyst, business professional, or aspiring data scientist, this course will deepen your statistical intuition and equip you with practical tools to support decision-making in real-world applications.
What will you learn?
By the end of this course, you will be able to:
- Apply linear regression in both inferential and predictive settings
- Interpret relationships between variables using statistical models
- Build and evaluate classification models, including logistic regression
- Assess model performance using appropriate evaluation metrics
- Apply machine learning techniques such as decision trees and k-nearest neighbors
- Understand the fundamentals of unsupervised learning using clustering
Structure
The course is completely self paced. It will take you approximately 20 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