Computational techniques for large-scale data

Education facts

Code: DIT066

The advent of big-data has led to the development of new programming paradigms, in particular for parallel systems allowing the computation with big data on redundant clusters of commodity computers. This course provides an introduction to different programming paradigms, e.g. MapReduce and extensions, which facilitate computations with Terabytes of data. It also demonstrates that for specific tasks algorithms and data structures can provide highly efficient alternatives.

Entry requirements

To be eligible to the course, the student should have a Bachelor's degree in any subject, or have successfully completed 90 credits of studies in computer science, software engineering, or equivalent. Specifically, at least 15 credits of successfully completed courses in programming, or equivalent are required. The student needs to have successfully completed a course in probability theory or statistics. Applicants must prove knowledge of English: English 6/English level 2 or the equivalent level of an internationally recognized test, for example TOEFL, IELTS.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Computational techniques for large-scale data

  University of Gothenburg

  GÖTEBORG

  Start date: 22 March 2027

  End date: 6 June 2027

  Pace of study: 50 %