Syllabus for Large Datasets for Scientific Applications

Stora datamängder inom vetenskapliga tillämpningar


  • 5 credits
  • Course code: 1TD267
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Computer Science A1F, Technology A1F, Computational Science A1F
  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2014-03-13
  • Established by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2014
  • Entry requirements:

    120 credits including Computer Programming II or the equivalent (programming in Java or Python). Database Design II and Scientific Computing I or the equivalent. 

  • Responsible department: Department of Information Technology
  • This course has been discontinued.

Learning outcomes

To pass, the student should be able to

  • use state-of-the art software platforms for management and processing of massive scientific datasets.
  • analyse the characteristics of a data-intensive scientific application and propose suitable strategies to handle the data analytics aspects of the application.
  • implement software to address an application’s data analysis needs using the technology presented in the course.
  • critically analyse, discuss and present solutions and implementations in writing and orally.


How to develop scientific applications utilizing methods and key concepts of large scale data processing platforms. Distributed file systems and cloud containers such as OpenStack Swift. Batch data processing with MapReduce based infrastructures such as Hadoop. Effective use of query languages such as Hive for scientific applications. Effective use of indexing. Array databases such as SciDB, data stream processing platforms such as Storm. Overview of techniques, concepts and tools for analysing massive data, such as NoSQL, NoDB, flat namespaces and ontology based data.


Lectures, guest lectures, laboratory work and group supervision. Assignments with oral and written presentation.


Mandatory assignments and the completion of a software project. Active participation in seminars. Written and oral discussion of assignments and research papers.

Reading list

Reading list

Applies from: Autumn 2014

Some titles may be available electronically through the University library.

Research papers.

Last modified: 2022-04-26