You will learn k-means clustering implementation using scikit-learn to segment users based on RFM variables, enabling them to create data-driven user profiles that inform product strategy and targeted interventions.

You will analyze different retention calculation methodologies, understand their strategic implications, and create technical recommendations that guide data-driven retention strategy decisions in product analytics contexts.

You will systematically identify and assess bias sources that compromise A/B test validity, focusing on novelty effects and exposure inequality detection.

You will apply power analysis principles to calculate appropriate sample sizes and design experiments that reliably detect meaningful business impacts.

You will move beyond “vanity metrics” to master Cohort Analysis—the essential framework for measuring how effectively your product retains users over time. By grouping users based on shared characteristics, most commonly their acquisition date, you will construct and interpret Cohort Heatmaps to track behavior patterns and pinpoint exactly where users drop off in their lifecycle. This approach provides the mathematical clarity needed to separate temporary growth spikes from true product-market fit, enabling you to calculate precise Retention Rates and visualize the “long tail” of user stability through Retention Curves.

You will move beyond simple tracking to diagnose the "shape" of your user behavior and identify the underlying drivers of long-term loyalty. In this section, we analyze the specific geometry of your retention curves—distinguishing between the "Sinking Ship" of a declining curve and the "Growth Engine" of a flattened or "smiling" curve—to determine if your product has achieved true product-market fit. You will learn to perform behavioral layering to uncover the "Aha! Moment," that specific set of actions that separates your power users from those who churn, allowing you to optimize the user journey around the activities that mathematically correlate with the highest lifetime value.

You will apply Kaplan-Meier survival analysis to evaluate user retention patterns over time, create survival plots in R with statistical testing to compare groups, and integrate analytical findings into experiment readouts that mirror real-world data analyst deliverables for stakeholder communication.

You will conduct a comprehensive product analytics project that integrates user segmentation, experimentation design, and retention analysis to deliver actionable insights for optimizing product engagement and user retention strategies.