Data Analytics for Product Managers Course

Lesson 1 • Data Sources in a Product Ecosystem

Maps common data sources—product events, CRM, support, surveys—to their analytical uses. Helps PMs know where to look before requesting data.

Lesson 2 • Types of Product Data

Distinguishes quantitative, qualitative, behavioural, and operational data types. Grounds each type in real product scenarios to build recognition skills.

Lesson 3 • Why Data Matters in Product Work

Establishes the business case for analytics in product management. Connects data literacy to better prioritisation, stakeholder trust, and product outcomes.

Lesson 4 • Defining Questions Before Pulling Data

Teaches question-first thinking to prevent data overload. Frames analytics as hypothesis-driven inquiry rather than exploratory browsing.

Lesson 1 • Choosing the North Star Metric

Guides selection of a single metric that best captures product value delivery. Links north star choice to long-term retention and revenue alignment.

Lesson 2 • Metric Taxonomy for Products

Categorises metrics into acquisition, activation, retention, revenue, and referral layers. Provides a shared vocabulary for cross-functional metric discussions.

Lesson 3 • Designing a KPI Tree

Teaches decomposition of top-level goals into measurable sub-metrics. Enables PMs to identify which levers drive the north star metric.

Lesson 4 • Communicating Metrics to Stakeholders

Covers how to present metric frameworks to executives, engineers, and designers. Aligns metric storytelling with audience goals and decision authority.

Lesson 1 • Privacy and Data Governance Basics

Covers consent management, data minimisation, and user rights in product analytics. Frames compliance as a design constraint that shapes instrumentation decisions.

Lesson 2 • Event Tracking Fundamentals

Explains event-based data models including events, properties, and user identity. Connects tracking design to downstream analysis quality.

Lesson 3 • Data Quality and Validation

Teaches methods for detecting missing, duplicate, or incorrect event data. Establishes quality checks as a PM responsibility, not just an engineering task.

Lesson 4 • Data Collection Methods and Tools

Surveys client-side, server-side, and third-party data collection approaches. Helps PMs choose the right method based on accuracy and privacy needs.

Lesson 1 • Visualising Data for Insight

Covers chart selection, axis design, and annotation for exploratory analysis. Trains PMs to distinguish charts that reveal insight from those that obscure it.

Lesson 2 • Descriptive Statistics Essentials

Covers mean, median, mode, variance, and percentiles as tools for summarising product data. Connects each statistic to a specific product interpretation use case.

Lesson 3 • Segmentation and Cohort Analysis

Teaches slicing data by user attributes and time-based cohorts to reveal hidden patterns. Directly supports retention analysis and feature adoption tracking.

Lesson 4 • Understanding Data Structure and Shape

Introduces rows, columns, data types, and distributions as the starting point for any analysis. Builds confidence in reading raw data before applying any technique.

Lesson 1 • Statistical Concepts for PMs

Explains p-values, confidence intervals, statistical power, and sample size in plain language. Equips PMs to evaluate test results without needing a statistics degree.

Lesson 2 • Designing a Valid Experiment

Covers hypothesis formulation, unit of randomisation, and metric selection for experiments. Ensures tests are designed to answer the right question before any code is written.

Lesson 3 • Foundations of Causal Inference

Distinguishes correlation from causation and explains why controlled experiments are the gold standard. Prepares PMs to challenge spurious insights from observational data.

Lesson 4 • Interpreting and Acting on Results

Teaches how to read experiment results, handle inconclusive tests, and make ship decisions. Connects statistical outcomes to product strategy and roadmap prioritisation.

Lesson 1 • Engagement Metrics and Depth of Use

Defines DAU, WAU, MAU, stickiness, and feature adoption rates as engagement signals. Links engagement depth to long-term retention and monetisation potential.

Lesson 2 • Behavioural Segmentation for Retention

Groups users by behavioural patterns to tailor retention strategies to distinct user types. Connects segmentation outputs directly to product and marketing interventions.

Lesson 3 • Churn Analysis and Prediction

Covers methods for identifying at-risk users before they churn using behavioural signals. Enables PMs to design proactive retention interventions grounded in data.

Lesson 4 • User Journey and Path Analysis

Maps common user paths through a product to identify friction, detours, and dead ends. Supports UX prioritisation and onboarding optimisation decisions.

Lesson 1 • Communicating Data to Drive Roadmap Buy-In

Structures data narratives that persuade executives and stakeholders to approve roadmap decisions. Combines analytical rigour with storytelling to reduce decision friction.

Lesson 2 • Measuring Feature Success Post-Launch

Defines success criteria before launch and tracks adoption, engagement, and impact after. Closes the feedback loop between shipping and learning.

Lesson 3 • Data-Driven Prioritisation Frameworks

Applies RICE, ICE, and opportunity scoring using real metric inputs. Replaces gut-feel ranking with a repeatable, data-backed prioritisation process.

Lesson 4 • Identifying Opportunities from Data

Covers techniques for mining analytics to surface unmet needs and underserved segments. Connects data patterns to opportunity hypotheses ready for validation.

Lesson 1 • Multi-Touch Attribution Models

Explains how to assign credit across marketing and product touchpoints in a user journey. Enables more accurate investment decisions across acquisition and growth channels.

Lesson 2 • Building an Analytics Strategy

Guides PMs in designing a company-wide analytics roadmap aligned to product and business goals. Synthesises all prior course concepts into a strategic measurement plan.

Lesson 3 • Network Effects and Viral Metrics

Quantifies viral growth loops and network density as strategic product levers. Connects viral coefficient and network metrics to long-term competitive advantage.

Lesson 4 • Customer Lifetime Value Modelling

Covers LTV calculation methods and their application to acquisition, retention, and pricing strategy. Connects LTV to sustainable unit economics and investment decisions.