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Guided learning journeys that build knowledge step by step.
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This learning path introduces high school students to the systematic process of gathering, analyzing, and documenting software requirements. It covers requirement types, elicitation techniques, analysis, SRS documentation, and validation, building on foundational SDLC knowledge.
This learning path introduces the phases and models of the Software Development Life Cycle (SDLC). It covers the fundamental concepts, the main phases, and various process models such as Waterfall, V-Model, Iterative, and Agile. By the end, learners will be able to compare models and understand how they are applied in real projects.
A beginner-friendly path for high school students with basic programming knowledge. It covers the fundamentals of software engineering, the software development lifecycle, process models, roles, and ethics, culminating in a practical application.
A comprehensive learning path for aspiring researchers in database systems, covering research methodology, literature review, problem formulation, experimental design, benchmarking, reproducibility, data analysis, scientific writing, and the structure of top-tier papers like VLDB/SIGMOD.
This learning path guides graph database developers through the evolving landscape of graph query standards, focusing on the GQL standard and SQL/PGQ. It covers foundational concepts, pattern matching, integration with Cypher, and practical application.
This advanced graduate-level path explores how AI and ML techniques are transforming database systems. It covers foundational ML concepts, the integration of ML models into database components such as optimizers and indexes, and the management of ML workloads within databases. The path balances conceptual depth with practical considerations, preparing researchers and developers to engage with this emerging field.
This advanced path equips architects and developers with strategies for managing multiple data stores, focusing on data federation, federated queries, and data virtualization. It covers foundational database concepts, SQL proficiency, and architectural patterns, culminating in practical implementation and governance considerations.
This learning path takes you from foundational database and machine learning concepts to the advanced mechanisms behind autonomous databases. It covers self-driving architecture, workload learning, query optimization, self-tuning, and predictive maintenance, culminating in a research-oriented project. Designed for graduate-level researchers and developers, it provides a structured journey through the core technologies enabling database self-management.
This advanced graduate-level path explores the convergence of blockchain and database technologies. Learners will understand blockchain fundamentals, examine decentralized storage and immutable ledgers, and investigate how to query blockchain data and build smart contract databases. The path bridges traditional database concepts with blockchain innovations, culminating in a comparative analysis of integration approaches.
A professional learning path for data engineers to master databases optimized for real-time analytics. It covers columnar storage, vectorized execution, in-memory analytics, and popular engines like ClickHouse and Druid, culminating in building real-time dashboards.