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DataGroup

9

AI Product Management

789 000 ₸
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Allocated 3 Quotas

About course

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The program's goal is to train product leaders capable of independently managing an AI product throughout its entire lifecycle, from customer discovery to scaling. During the course, students fully master the product management cycle: Discovery → Development → Strategy → Analytics → Growth. They learn to conduct high-quality in-depth interviews, identify user pain points, and validate hypotheses. In their practical work, participants build working MVPs and interactive prototypes to test their AI solutions. The course teaches how to digitalize business: students master product analytics, unit economics, and financial modeling. To manage development, they implement Agile/Scrum methodologies and prioritization frameworks such as RICE, Kano, and MoSCoW. Team and strategy are aligned using OKR and North Star Metric metrics. The main outcome of the course is the development of their own AI-focused pet product from scratch. The program culminates with a final defense of the completed case before an expert jury to enhance the professional portfolio.

Special condition

Selection criteria: selection using automated digital screening;

Course details

level

For beginner

Study format

Online

Entrance exams

No

Duration, in weeks

26

Education language

Russian

Qualifications

junior

Skills


Upon completion of the course, students will acquire the following skills: Customer Discovery: In-depth interviews (CustDev), Affinity Diagram, surveys (NPS, CES, Likert) User Research: User Persona, JTBD, Empathy Map, Design Thinking, usability testing Product Documentation: PRD, User Stories, Acceptance Criteria, Story Mapping Market and Competitors: TAM/SAM/SOM, Feature Matrix, Positioning Map, Blue Ocean MVP and Prototyping: Landing Page (Tilda), Wizard of Oz, Figma (clickable prototype) Agile: Scrum, Kanban, Jira (backlog, sprint planning, epics) Strategy: Lean Canvas, Business Model Canvas, monetization models Finance: Unit Economics (CAC, LTV, Payback), Financial Modeling, P&L, sensitivity analysis Prioritization and Roadmap: RICE, ICE, Kano, MoSCoW, Outcome Roadmap Metrics: OKR, North Star Metric, metric tree, AARRR funnel, cohort analysis Product Analytics: Amplitude, Microsoft Clarity, SQL (SELECT/WHERE/GROUP BY/JOIN) A/B testing: hypothesis framework, sample calculation, p-value, guardrail metrics Growth: Growth loops, habit loops, Aha-moment, RFM analysis B2B PM: enterprise discovery, RFP/TOR, pilot, SLA, specifics of banks and quasi-government agencies in the Republic of Kazakhstan Portfolio: Notion, Loom (video pitch), STAR method

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