Course · Product & Management

Product Management from Scratch

A practical product management course in English: telling the PM role apart from project manager and product owner, problem discovery and non-leading interviews, testable hypotheses and MVPs, prioritization (RICE, ICE, Kano, MoSCoW), metrics (AARRR, North Star) and unit economics (LTV, CAC), and data-informed decisions.

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Unit 1 — What a Product Manager Actually Does

Lesson 1.1 — The PM's job: "what & why", not "how & when"

6 exercises · Explain in your own words what a product manager is accountable for — delivering value to BOTH the user and the business — and recognize that the PM owns the 'what and why' rather than only the 'how and when'.

Lesson 1.2 — PM vs Project Manager vs Scrum Product Owner

5 exercises · Clearly distinguish the product manager, the project manager, and the Scrum product owner, and assign realistic responsibilities to the correct role.

Lesson 1.3 — Myths and beginner mistakes about the role

6 exercises · Recognize common myths about product management and the typical mistakes newcomers make, and replace each myth with the accurate practice.

Unit 2 — Discovery: Understand the Problem First

Lesson 2.1 — Discovery before delivery

6 exercises · Explain the difference between discovery (understanding the problem with interviews, observation, and data) and delivery (building the solution), and order the steps so problem-validation comes before building.

Lesson 2.2 — Problem interviews and customer development

6 exercises · Run a problem interview that asks about real past behaviour and pains, avoiding leading and hypothetical questions.

Lesson 2.3 — Jobs-to-be-Done and testable hypotheses

6 exercises · View a product through the Jobs-to-be-Done lens and write a hypothesis in testable form.

Unit 3 — Solution and MVP

Lesson 3.1 — What an MVP is and why it exists

6 exercises · Explain the MVP (per Eric Ries) as a tool for cheap learning that tests the key value hypothesis, not as a merely 'stripped-down product'.

Lesson 3.2 — Test the hypothesis cheaply: prototypes and experiments

5 exercises · Choose the cheapest experiment that can validate the key value hypothesis before committing to large development.

Lesson 3.3 — The featuritis trap: more features ≠ a better product

5 exercises · Recognize 'featuritis' and keep focus on validated value instead of accumulating features.

Unit 4 — Prioritization

Lesson 4.1 — Why prioritize, and an overview of the frameworks

6 exercises · Understand why prioritization is necessary and recognize the main frameworks — RICE, ICE, Kano, and MoSCoW — and what each is good for.

Lesson 4.2 — RICE scoring done right (correct arithmetic)

6 exercises · Correctly compute RICE scores for several features and rank them by priority.

Lesson 4.3 — Kano and MoSCoW applied to a case

6 exercises · Apply Kano and MoSCoW to a set of features and justify what to do now.

Unit 5 — Metrics and Unit Economics

Lesson 5.1 — The AARRR funnel, North Star, and vanity metrics

6 exercises · Read an AARRR funnel, understand the role of activation and retention, choose a North Star metric, and recognize vanity metrics.

Lesson 5.2 — Unit economics: LTV, CAC, and margin

6 exercises · Understand LTV, CAC, and contribution margin, and interpret the LTV/CAC ratio as a guideline rather than a law.

Lesson 5.3 — A/B-test basics and drawing correct conclusions

5 exercises · Understand what an A/B test is and avoid drawing conclusions without adequate sample size, statistical significance, or a causal basis.

Unit 6 — Roadmaps, Stakeholders, and Decisions

Lesson 6.1 — The roadmap: direction, not a list of dates

6 exercises · Build a simple roadmap that expresses priorities and direction rather than a guaranteed list of dates.

Lesson 6.2 — Stakeholders and data-informed decisions

6 exercises · Work with stakeholders and make decisions by combining quantitative data with qualitative signals.

Lesson 6.3 — Capstone: analyze a real product situation

5 exercises · Apply the whole course to a realistic product situation: from problem and hypothesis through MVP, prioritization, metrics, and a roadmap recommendation.

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