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Concept Taxonomy

Course: From Product Manager to Technical Product Manager: A Practitioner's Guide Total Categories: 11 Generated: 2026-02-10

Taxonomy Categories

1. Product Management Foundations

TaxonomyID: PMFND

Description: Core product management concepts that form the foundation for the entire course, including PM roles, strategy, stakeholder management, and product metrics.

Includes:

  • Product management fundamentals and roles
  • Product strategy, vision, and roadmap
  • Stakeholder management and cross-functional teams
  • User needs, market research, and competitive analysis
  • Product metrics, KPIs, and OKRs

2. Software Development

TaxonomyID: SWDEV

Description: Foundational software development concepts that technical PMs need to understand, including programming basics, version control, and code collaboration workflows.

Includes:

  • Software development basics and source code
  • Programming languages overview
  • Frontend, backend, and full stack concepts
  • Version control, Git, and code repositories
  • Code review and pull request workflows

3. Technical Documentation

TaxonomyID: TCDOC

Description: Concepts related to writing and interpreting technical documents, specifications, requirements, and communicating technical information.

Includes:

  • Technical documentation practices
  • Engineering and technical specifications
  • Functional and non-functional requirements
  • Software bugs and debugging fundamentals
  • Technical jargon and vocabulary

4. System Architecture

TaxonomyID: SARCH

Description: System design concepts including architecture patterns, cloud computing, scaling strategies, and system reliability principles.

Includes:

  • System architecture fundamentals and design patterns
  • Monolithic vs. microservices architecture
  • Cloud computing (IaaS, PaaS, SaaS, serverless)
  • Containerization (Docker, Kubernetes)
  • Scaling, load balancing, caching, and CDN
  • System reliability, availability, and fault tolerance

5. APIs and Integrations

TaxonomyID: APINT

Description: API concepts including REST, GraphQL, authentication, documentation, and tools for working with APIs.

Includes:

  • API fundamentals and design patterns
  • REST API and GraphQL
  • HTTP methods, endpoints, and authentication
  • Data serialization (JSON, XML)
  • API testing, documentation, and tools (Postman, SDKs)
  • Webhooks and third-party integrations

6. Databases and Data

TaxonomyID: DBASE

Description: Database concepts including relational and NoSQL databases, SQL querying, data modeling, and data management practices.

Includes:

  • Database fundamentals (relational and NoSQL)
  • SQL basics, queries, and joins
  • Database schema, normalization, and indexing
  • Data modeling and entity relationships
  • Data warehouses and data lakes
  • Database transactions, ACID properties, and performance

7. SDLC and Agile

TaxonomyID: AGILE

Description: Software development lifecycle methodologies, Agile practices, Scrum ceremonies, and release management processes.

Includes:

  • SDLC and Waterfall methodology
  • Agile development and Scrum framework
  • Sprint ceremonies (planning, standups, reviews, retrospectives)
  • Product backlog, user stories, and story points
  • Kanban method and velocity tracking
  • CI/CD, release management, and feature flags
  • MVP and iterative development

8. Quality and Testing

TaxonomyID: QATST

Description: Code quality, technical debt management, testing methodologies, and quality assurance practices.

Includes:

  • Technical debt and code quality
  • Code refactoring and legacy systems
  • Testing fundamentals (unit, integration, end-to-end)
  • Performance and security testing
  • Quality assurance and code coverage
  • Automated testing and system migration

9. Analytics and Data Science

TaxonomyID: ANLYT

Description: Data-driven decision making, product analytics, experimentation, data visualization, and data governance.

Includes:

  • Data-driven decisions and product analytics
  • Web analytics and user behavior tracking
  • Funnel analysis, cohort analysis, and customer segmentation
  • A/B testing, experiment design, and statistical significance
  • Data visualization and dashboard design
  • Python for data analysis and ETL processes
  • Data privacy, GDPR, and data governance

10. AI Tools and Strategy

TaxonomyID: AITOL

Description: Generative AI concepts, AI tools for product managers, prompt engineering, and AI strategy and governance.

Includes:

  • Generative AI and large language models
  • AI tools for PMs (ChatGPT, Claude, GitHub Copilot)
  • AI prompt engineering and code understanding
  • AI for documentation, data analysis, and debugging
  • AI limitations, ethics, and governance
  • AI in product strategy and integration planning

11. Career and Leadership

TaxonomyID: CARER

Description: Career transition skills, technical communication, decision-making frameworks, and professional development for technical PM roles.

Includes:

  • Technical PM job market and interview preparation
  • Technical communication and engineering team dynamics
  • Build vs. buy analysis and technical decision making
  • Escalation frameworks and technical roadmapping
  • Personal learning plans and continuous technical learning

Category Distribution

Category TaxonomyID Count Percentage
System Architecture SARCH 25 12.5%
Databases and Data DBASE 25 12.5%
Analytics and Data Science ANLYT 25 12.5%
Product Management Foundations PMFND 20 10.0%
APIs and Integrations APINT 20 10.0%
SDLC and Agile AGILE 20 10.0%
AI Tools and Strategy AITOL 20 10.0%
Quality and Testing QATST 15 7.5%
Software Development SWDEV 11 5.5%
Career and Leadership CARER 10 5.0%
Technical Documentation TCDOC 9 4.5%

Distribution Guidelines

  • Target: ~18 concepts per category (200 concepts / 11 categories)
  • Maximum: 30% of total concepts (~60 concepts)
  • Minimum: No category below 3% (~6 concepts)
  • Spread: 8.0% (excellent balance)

Notes

This taxonomy provides a balanced organization across technical, analytical, and professional domains. Categories are designed to:

  1. Avoid excessive overlap between categories
  2. Maintain clear boundaries aligned with course topics
  3. Support logical learning progressions from PM foundations to technical depth
  4. Enable effective color-coded visualization in the learning graph
  5. Align with the course structure and Bloom's Taxonomy learning outcomes