Master the analytical foundation that transforms data into product decisions. This Short Course equips product analysts with the systematic approach to hypothesis-driven investigation and the expertise to select optimal classification models for real-world scenarios. You'll learn to recall and apply the six-step hypothesis-driven analysis framework that guides investigations from question to conclusion, and evaluate critical trade-offs between decision trees and logistic regression based on interpretability, data characteristics, and preprocessing requirements. By completing this course, you'll confidently navigate model selection decisions, justify analytical approaches to stakeholders, and build reliable frameworks for product analytics that drive meaningful business outcomes.

By the end of this course, you will be able to: Recall the six steps of the hypothesis-driven analysis framework Evaluate model-selection trade-offs between decision trees and logistic regression This course is unique because it bridges theoretical frameworks with practical model selection, giving you both the analytical structure and technical decision-making skills essential for product analytics success. To be successful in this course, you should have a background in basic statistics, data analysis fundamentals, and exposure to classification modeling concepts.