Technical Acumen for Group Product Managers: A Beginner's Guide
Empower Group Product Managers with essential technical knowledge to bridge the gap between product vision and engineering execution.
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Software Development Fundamentals and Methodologies
Unit 1: Software Development Lifecycle (SDLC) Basics
What is SDLC?
SDLC Stages: Deep Dive
SDLC Models Overview
Choosing the Right Model
Your Role in the SDLC
Unit 2: Agile, Waterfall, and Scrum Methodologies
Waterfall: A Linear Approach
Agile: Embrace Change
Scrum: Agile in Action
Agile vs. Waterfall
Kanban: Visual Workflow
Unit 3: Collaboration with Engineering Teams
Speaking the Same Language
User Stories: The Basics
Acceptance Criteria
Participating in Scrum
Giving Constructive Feedback
System Architecture, Data Flow, and Infrastructure Basics
Unit 1: System Architecture Fundamentals
What is System Architecture?
Architecture Diagram Basics
Monolithic Architecture
Microservices Architecture
Choosing an Architecture
Unit 2: Data Flow Diagrams
What is Data Flow?
DFD Symbols and Notation
Tracing Data: A Simple Flow
Complex Data Flows
Data Flow and Product Design
Unit 3: Database Fundamentals
What is a Database?
SQL vs. NoSQL: The Basics
SQL: Relational Databases
NoSQL: Non-Relational DBs
DB Choice & Product Impact
Unit 4: Cloud Infrastructure Basics
What is Cloud Computing?
AWS, Azure, and GCP
Scalability in the Cloud
Cloud Cost Optimization
Cloud & Product Feasibility
APIs, Integrations, and Security Principles
Unit 1: API Fundamentals
What are APIs?
REST vs. GraphQL
API Request Methods
API Status Codes
API Keys & Authentication
Unit 2: Integrations
Integration Strategies
Data Mapping
Webhooks
Error Handling
Integration Testing
Unit 3: Security Principles
Security Basics
Authentication
Authorization
Encryption
Secure Coding Practices
Product Analytics, KPIs, and AI/ML Introduction
Unit 1: Product Analytics and KPIs
Intro to Product Analytics
Defining Your KPIs
Acquisition KPIs
Engagement KPIs
Retention KPIs
Unit 2: AI/ML Fundamentals for Product Managers
AI/ML: The Basics
AI/ML Use Cases
Data Requirements
Model Evaluation
AI/ML Limitations
Unit 3: Ethical Considerations in AI/ML
Bias in AI/ML
Privacy Concerns
Transparency & Explainability
Ethical Frameworks
Responsible AI