Analysis

Introduction to Statistical Modeling for Online Behavior Data

Opal: Link here.

Time Thursdays, 5. DS

Location BAR/0I89

Department Computer Science

Modules INF-BAS3 (Software- und Web-Engineering)

Language English

Assessment oral exam

Description:

Online platforms generate vast amounts of behavioral data, including subjective ratings (e.g., likes, stars, reactions) and objective engagement metrics (e.g., views, clicks, watch time). While machine learning excels at predicting user behavior, statistical modeling—a cornerstone of empirical research—is critical for interpretable analysis. It helps uncover relationships between user behavior and various factors (e.g., how demographics influence engagement patterns) and enables causal inference in experiments (e.g., measuring the impact of different feed-ranking algorithms on user experience). These methods are widely used in UX research, product analytics, and A/B testing.