Build Your Technical Depth as a Product Manager

In this topic, you will learn how to build essential technical literacy by understanding core components of modern software applications, from web and mobile interfaces to cloud infrastructure and security fundamentals.

Learning Objectives

• Analyze the architecture of web applications, mobile apps, and cloud systems
• Evaluate security protocols (HTTPS, encryption) and their impact on product decisions
• Demonstrate understanding of APIs and their role in product ecosystems

In this topic, you will deep dive into software architecture patterns, technology stacks, and infrastructure decisions that directly impact product performance, scalability, and development velocity.

Learning Objectives

• Compare frontend frameworks (React, Angular, Vue) and their product implications
• Evaluate backend architecture decisions (microservices vs monolith, database choices)
• Design DevOps strategies that align with product roadmaps

In this topic, you will learn about API product management including performance optimization, integration strategies, and creating technical requirements that bridge product vision with engineering execution.

Learning Objectives

• Design API strategies that support product scalability and user experience
• Optimize API performance through caching, payload management, and latency reduction
• Create comprehensive technical requirement documents for complex integrations

In this topic, you will learn how to develop system design thinking to make informed architectural decisions, understand trade-offs between scalability and consistency, and design resilient systems that support product growth.

Learning Objectives

• Apply system design principles to solve real-world product scaling challenges
• Evaluate trade-offs between availability, consistency, and partition tolerance (CAP theorem)
• Design fault-tolerant systems using event-driven architecture patterns

In this topic, you will learn how to navigate the AI product landscape by understanding machine learning fundamentals, making build-vs-buy decisions, and managing operational challenges while ensuring ethical AI implementation.

Learning Objectives

• Evaluate AI/ML solutions for product features using cost-benefit analysis
• Implement AI integration workflows from problem definition to production deployment
• Address ethical considerations, bias mitigation, and compliance requirements in AI products

In this topic, you will learn the art of collaborating with diverse technical teams, facilitate effective communication between stakeholders, and lead technical decision-making processes that align with product strategy.

Learning Objectives

• Facilitate productive collaboration between frontend, backend, DevOps, data science, and AI/ML teams
• Translate business requirements into technical specifications across different engineering disciplines
• Lead technical decision-making processes that balance business needs with technical constraints