DIGITAL PLATFORM ANALYTICS
An introduction to data analysis procedures covering topics including: data collection, pre-processing, analysis, and presentation. Content covers important data analysis concepts, such as structured and unstructured data. Learn about supervised, unsupervised learning methods and advanced analytics topics, such as text mining, recommendation systems, and algorithm bias.
Prerequisites & course information
Requisites: None Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Recognize and apply fundamental data analysis procedures, including data collection, pre-processing, analysis, and presentation. Audience: Undergraduate 2. Analyze structured and unstructured data in a variety of contexts. Audience: Undergraduate 3. Develop supervised and unsupervised data mining skills, including linear regression, logistics regression, decision tree, KNN, K-means, and simple text mining techniques. Audience: Undergraduate 4. Evaluate data analysis frontiers, including recommendation systems, data ethics, privacy, and algorithm bias. Audience: Undergraduate Learning Outcomes: 1. Recognize and apply fundamental data analysis procedures, including data collection, pre-processing, analysis, and presentation. Audience: Undergraduate 2. Analyze structured and unstructured data in a variety of contexts. Audience: Undergraduate 3. Develop supervised and unsupervised data mining skills, including linear regression, logistics regression, decision tree, KNN, K-means, and simple text mining techniques. Audience: Undergraduate 4. Evaluate data analysis frontiers, including recommendation systems, data ethics, privacy, and algorithm bias. Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027