Learning outcomes

Computing competence represents a central element in scientific problem solving, from basic education and research to essentially almost all advanced problems in modern societies. Computing competence is simply central to further progress. It enlarges the body of tools available to students and scientists beyond classical tools and allows for a more generic handling of problems. Focusing on algorithmic aspects results in deeper insights about scientific problems.

After completing a master's degree in computational science you will have achieved:

Knowledge

You have gained a deep knowledge of the scientific method and computational science at an advanced level, meaning that you:

  • have theoretical and practical knowledge of a wide range of computational methods and mathematical algorithms, including principles for developing and generalizing such methods and algorithms
  • understand how to apply computational methods to extract information from experimental data and solve scientific problems
  • understand the limitations of numerical methods, including approximation errors, round-off errors and the constraints on the applicability of specific algorithms

You understand the possibilities and limits of computational modeling, meaning that you:

  • can transform scientific problems into generic computational models and understand how various error sources influence the accuracy and reliability of the models and the computed results
  • have an overview of advanced algorithms for solving a wide range of problems and how they can be accessed in available software

Skills

You have developed a practical mastery of computing, including the interplay between scientific problems and data, mathematical models, generic algorithms and reusable software, meaning that you:

  • are able to analyze and visualize computed results and evaluate their relevance with respect to the underlying problems and/or hypotheses
  • have a working understanding of high-performance computing elements including memory usage, vectorization and parallel algorithms, and related software tools like debuggers, test frameworks, scripts, and version control systems
  • can program in high-level and compiled languages and make efficient use of a computer algebra system
  • understand how to increase the efficiency of numerical algorithms and pertinent software
  • and you are familiar with techniques for collaborative software development

General competence

You have developed a fundamental understanding and knowledge of scientific work and the scientific method, including ethical and societal limitations and possibilities. This means among other things that you:

  • can develop hypotheses and suggest ways to test these using relevant analytical, experimental and numerical tools
  • can reflect on and develop strategies and tools to make science reproducible and have a sound ethical approach to scientific problems
  • you can communicate in a professional way scientific problems, results and uncertainties, orally and in written form
  • you have developed a sound, scientific intuition and can reflect over and develop efficient and personal learning strategies
  • you can work independently but also in close collaboration with others to complete a research project on time

By completing a Master's degree in Computational Science, you will have developed a critical understanding of the scientific methods which have been studied, have a better understanding of the scientific process per se as well as having developed perspectives for future work and how to verify and validate scientific results.

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Published Dec. 22, 2016 1:02 PM - Last modified Jan. 4, 2018 8:47 AM