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Guided learning journeys that build knowledge step by step.
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This learning path equips computational science researchers with essential software engineering practices to develop reliable, reproducible, and maintainable scientific software. It covers version control, testing, documentation, code review, and continuous integration, tailored to the needs of scientific computing workflows.
This learning path introduces advanced computational science students to the core concepts and technologies of high-performance computing. Starting with computer architecture and parallel computing fundamentals, it progresses through distributed memory programming with MPI, shared memory with OpenMP, GPU programming with CUDA, and performance optimization techniques. The path emphasizes practical skills and the underlying principles that drive modern HPC systems.
This learning path guides computational science students from basic Julia syntax to high-performance scientific computing, covering multiple dispatch, arrays, linear algebra, differential equations, and parallel computing. It emphasizes practical applications and hands-on practice.
This learning path equips science and engineering students with advanced Python skills for scientific computing. Starting from basic Python, it covers essential libraries (NumPy, SciPy, Matplotlib, Pandas) and techniques for performance optimization and vectorization, culminating in a capstone project that integrates these skills.
A comprehensive learning path for advanced undergraduates to understand and implement numerical methods for PDEs. It covers the mathematical foundations, finite difference, finite element, and finite volume methods, along with stability, convergence, and applications to elliptic, parabolic, and hyperbolic PDEs.
This learning path guides undergraduate science students from the fundamentals of calculus and programming to practical numerical methods for differentiation, integration, and ordinary differential equations. It covers finite difference approximations, Newton-Cotes integration, and both single-step and multistep methods for ODEs, emphasizing error analysis and stability.
This learning path guides undergraduate STEM students through the essential numerical methods for solving linear algebra problems in computational science. Starting from foundational matrix operations and floating-point arithmetic, it progresses through LU and QR decompositions, eigenvalue methods, SVD, and iterative techniques, with an emphasis on practical implementation and understanding of numerical behavior.
This learning path guides high school students interested in data science through the fundamentals of representing and visualizing scientific data. Starting with data types and basic plotting, it progresses to creating effective charts and graphs, and concludes with interactive visualizations using Matplotlib and Plotly.
This learning path introduces programming fundamentals with a focus on scientific computing. Learners will gain hands-on experience with variables, data types, loops, conditionals, functions, arrays, and basic I/O, using Python or Julia. The path emphasizes practical problem-solving and prepares learners for more advanced computational science topics.
This learning path equips high school students with the essential mathematical concepts needed for computational science. Starting from basic algebra and trigonometry, it progresses through linear algebra, calculus, differential equations, probability, and statistics, emphasizing their applications in computational contexts.