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Guided learning journeys that build knowledge step by step.
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7812 Paths · page 409 / 782
This learning path equips environmentally conscious engineers with the knowledge and skills to design, develop, and measure software with a reduced carbon footprint. It covers energy-efficient coding practices, sustainable software architecture, green metrics, and the broader context of software's environmental impact.
This advanced learning path bridges Human-Computer Interaction (HCI) and Software Engineering (SE), focusing on user-centered design, usability evaluation, and iterative development. It guides engineers and designers through foundational concepts, practical techniques, and integration strategies to create software that is both functional and usable.
A comprehensive learning path for security and software engineers to design, implement, and maintain secure software systems. Covers secure SDLC, threat modeling, security testing, cryptography integration, and security patterns, with a foundation in software development and security basics.
A comprehensive learning path for engineers to master embedded systems software engineering, covering C/C++ fundamentals, RTOS integration, resource constraints, real-time design, and testing, grounded in software engineering and OS basics.
A focused learning path for engineers working on large-scale systems, covering scalability, performance, distribution, data management, availability, and operational excellence. The path builds from foundational concepts to advanced practices, emphasizing real-world trade-offs and design patterns.
This path equips developers and architects with the principles and practices for building cloud-native applications. It covers the 12-factor methodology, containerization, orchestration with Kubernetes, serverless computing, and CI/CD in the cloud, emphasizing DevOps culture and modern operational practices.
This learning path guides developers and architects through designing and implementing microservices-based systems. It covers service decomposition, inter-service communication with REST and gRPC, service discovery, resilience patterns, and API gateways, building from foundational concepts to advanced practices.
This advanced graduate-level path equips researchers and students with the knowledge and skills to design, conduct, analyze, and replicate empirical studies in software engineering. It covers research design, data collection methods (surveys, experiments), quantitative and qualitative analysis, and replication, with a strong foundation in statistics and research ethics.
This learning path equips researchers and DevOps professionals with the knowledge and skills to apply data analytics to software development. It covers data collection from software repositories, static and dynamic analysis techniques, visualization, and mining software repositories, culminating in a capstone project that integrates these skills.
This graduate-level path explores the human and social dimensions of software engineering, integrating social science foundations with advanced topics in team dynamics, communication, developer productivity, and ethics. Designed for researchers and managers, it builds from core concepts to advanced applications, emphasizing evidence-based understanding and practical implications.