Data Analysis Projects: Choice-Based Conjoint Analysis with Python

Understanding what customers truly value is the key to designing successful products and subscription models. Choice-based conjoint analysis is the gold-standard market research methodology used by top subscription platforms to analyze trade-offs and predict consumer behavior. This text-based course guides you through the process of setting up, executing, and interpreting a conjoint analysis project using Python. You will transition from grasping basic consumer preference theory to executing a complete, real-world analytical workflow. By working through a practical subscription-service case study, you will learn how to clean preference data, build discrete choice models, and simulate market share under different product configurations. What you'll learn: - Understand the foundational economic and statistical theories behind discrete choice modeling - Design conjoint survey attributes and levels to capture realistic consumer trade-offs - Clean and structure raw survey response data using modern Python data libraries - Estimate consumer part-worth utilities using logistic regression and choice models - Predict market share and simulate customer choices under various pricing and feature scenarios - Apply modern Python packaging and virtual environments to ensure reproducible data analysis We begin with essential terminology and the core mathematical concepts of utility theory. From there, you will progress step-by-step through data preparation, model estimation, and business-focused simulation techniques using clear, written explanations and structured code examples. This course is designed for beginner data analysts, product managers, and market researchers who have a basic familiarity with Python but are new to conjoint analysis. No advanced statistical background is required. Start reading today to master the analytical techniques that drive modern product strategy.