In this module, 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.
• Understand components of software applications (frontend, backend, APIs, and databases).
• Understand technical product development lifecycle.
• Understand native vs hybrid for mobile development.
• Understand templates vs full custom build.
• Understand cloud server architecture and how it supports scalability and reliability.

In this module, 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), backend frameworks (Python, Node.Js, Java, Golang) and their product implications.
• Design DevOps strategies that align with product roadmaps.
• Understand performance optimization techniques for scalability and efficiency.
• Understand API design principles (REST, gRPC).
• Understand database strategies (SQL, NoSQL, caching).
• Evaluate backend architecture decisions (microservices vs monolith trade-offs).
• Understand ETL processes and data warehouses.
• Understand the concept of CI/CD pipeline management.
• Understand infrastructure decisions (cloud vs on-premise).

This module will help you develop a strategic mindset for API products, Master API product management including performance optimization, integration strategies and creating technical requirements that bridge product vision with engineering execution.

Learning Objectives

• Analyze API types (Public, Private, Partner) and their business impact.
• Navigate API architecture and understand endpoints, methods, authentication, and error handling through practical testing.
• Identify API opportunities in your industry and develop user personas for developer audience.
• Design API strategies that support product scalability and user experience
• Optimize API performance through caching, payload management, and latency reduction.
• Understand third-party API integration strategies.

Master the art of developer-centric design by creating compelling documentation, intuitive interfaces, seamless onboarding experiences, and implementing data-driven API product management through analytics, performance monitoring, and security frameworks.

Learning Objectives

• Create developer-friendly documentation and portals that reduce time-to-first-success and encourage adoption.Design onboarding flows that quickly demonstrate API value and keep developers engaged.Define and track key API metrics (usage, performance, error rates) to guide product decisions.Apply core security and compliance practices (authentication, rate limiting, GDPR essentials) to build trust and protect users.

In this module, 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 core system design principles to solve real-world product scaling challenges
• Evaluate trade-offs between availability, consistency, and scalability (CAP theorem) in practical product scenarios.
• Design fault-tolerant systems using event-driven architecture patterns
• Select appropriate architecture and data strategies (monolith vs microservices, SQL vs NoSQL, caching) for product growth.
• Identify and optimize performance bottlenecks through monitoring, partitioning, and scaling approaches.

In this module, 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 opportunities using build-vs-buy frameworks, data readiness assessment, and cost-benefit analysis
• Design end-to-end AI integration workflows from problem definition to deployment, including testing, monitoring, and maintenance
• Leverage AI APIs and large models (e.g., GPT, Gemini, Hugging Face) to accelerate product development while balancing trade-offs in performance, scalability, and cost.
• Apply ethical and compliance principles (bias mitigation, privacy, PII handling, GDPR) to ensure responsible and trustworthy AI adoption.

In this module, 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