Product Analyst Course

Lesson 1 • Analytical Thinking Frameworks

Teaches structured problem decomposition and hypothesis-driven thinking. Provides a repeatable approach for any analytical question.

Lesson 2 • The Product Analyst Role Defined

Clarifies the scope, responsibilities, and career path of a product analyst. Establishes the professional context for all subsequent technical skills.

Lesson 3 • The Product Development Lifecycle

Maps how products move from discovery through launch and iteration. Anchors analytical work to each lifecycle stage.

Lesson 4 • Business Models and Value Creation

Introduces revenue models, cost structures, and how products generate value. Grounds metric selection in business outcomes.

Lesson 1 • AARRR and Engagement Metrics

Covers acquisition, activation, retention, referral, and revenue metrics for lifecycle analysis. Enables full-funnel performance measurement.

Lesson 2 • North Star Metric Framework

Teaches how to identify a single metric that best captures product value delivery. Connects the north star to supporting input metrics.

Lesson 3 • Goal Hierarchies and OKRs

Explains how company objectives cascade into product goals and individual metrics. Links strategic intent to measurable outcomes.

Lesson 4 • Metrics Documentation and Communication

Covers how to define, document, and socialize metrics across teams. Ensures consistent interpretation and reduces metric disputes.

Lesson 1 • Window Functions and CTEs

Introduces window functions and common table expressions for advanced analytical queries. Enables cohort, ranking, and running-total calculations.

Lesson 2 • Aggregations and Grouping

Covers GROUP BY, HAVING, and aggregate functions for summarizing product data. Enables metric calculation directly in SQL.

Lesson 3 • Core SQL Query Syntax

Teaches SELECT, WHERE, ORDER BY, and LIMIT clauses for basic data retrieval. Builds the query-writing foundation for all advanced SQL work.

Lesson 4 • Joins and Multi-Table Queries

Explains INNER, LEFT, RIGHT, and FULL joins for combining data across tables. Unlocks cross-domain analysis in product databases.

Lesson 5 • Query Optimization Basics

Covers indexing concepts, query execution plans, and common performance pitfalls. Ensures analysts write queries that run efficiently at scale.

Lesson 1 • Visualization Design Principles

Teaches perceptual principles, chart selection, and visual hierarchy for effective communication. Prevents common chart design mistakes.

Lesson 2 • Core Chart Types for Product Data

Covers line, bar, scatter, funnel, and cohort charts most used in product analysis. Matches each chart type to specific analytical questions.

Lesson 3 • Storytelling with Data

Teaches narrative structure for data presentations that lead to decisions. Connects visualization choices to the analytical story being told.

Lesson 4 • Dashboard Maintenance and Governance

Covers versioning, ownership, and data freshness standards for production dashboards. Ensures dashboards remain trustworthy over time.

Lesson 1 • Event Tracking and Data Collection

Explains how user actions are captured as events and structured for analysis. Establishes the data foundation for all behavioural analysis.

Lesson 2 • User Segmentation Techniques

Introduces behavioural, demographic, and RFM segmentation for targeted analysis. Enables personalised insights and product decisions.

Lesson 3 • Cohort Analysis for Retention

Covers cohort construction and retention curve interpretation for long-term behaviour. Distinguishes healthy retention from churn-prone patterns.

Lesson 4 • Path and Flow Analysis

Examines user navigation sequences to uncover unexpected paths and dead ends. Informs information architecture and feature placement decisions.

Lesson 1 • Causal Inference and Experiment Logic

Explains why randomised experiments establish causality and when alternatives are needed. Builds the conceptual foundation for all testing work.

Lesson 2 • Experiment Readout and Decision Making

Teaches how to present experiment results and translate them into ship or no-ship decisions. Closes the loop between analysis and product action.

Lesson 3 • Statistical Analysis of Results

Covers t-tests, chi-square tests, confidence intervals, and p-value interpretation. Enables correct, defensible conclusions from experiment data.

Lesson 4 • Hypothesis Formulation and Sample Size

Covers writing testable hypotheses and calculating required sample sizes before launch. Prevents underpowered tests and wasted experiment cycles.

Lesson 5 • Common Experiment Pitfalls

Identifies peeking, novelty effects, network interference, and survivorship bias. Builds the critical eye needed to run trustworthy experiments.

Lesson 1 • Decomposition and Drill-Down Analysis

Teaches breaking composite metrics into components to locate the source of change. Builds systematic diagnostic skills for complex products.

Lesson 2 • Qualitative Data Integration

Covers user interviews, surveys, and session recordings as complements to quantitative data. Adds the 'why' behind behavioural patterns.

Lesson 3 • Communicating Findings to Stakeholders

Covers structuring analytical reports and presenting findings to product and business leaders. Ensures insights lead to decisions rather than being ignored.

Lesson 4 • Metric Change Investigation Framework

Provides a structured process for diagnosing unexpected metric rises or drops. Prevents jumping to conclusions without systematic evidence.

Lesson 1 • Competitive and Market Analysis

Covers benchmarking, market sizing, and competitive positioning using available data. Contextualises internal metrics against external landscape.

Lesson 2 • Product Health Monitoring Systems

Covers alerting, anomaly detection, and operational dashboards for continuous product health. Shifts analytics from reactive to proactive.

Lesson 3 • Forecasting and Goal Setting

Introduces trend extrapolation, regression-based forecasting, and scenario planning. Enables analysts to set credible targets and model futures.

Lesson 4 • Building an Analytics Culture

Teaches how analysts influence team norms around data-driven decision making. Positions the analyst as a strategic enabler, not just a report generator.