Syllabus for Computer-Intensive Statistics and Data Mining

Datorintensiv statistik och informationsutvinning


  • 10 credits
  • Course code: 1MS009
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Mathematics A1N
  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2007-03-15
  • Established by:
  • Revised: 2021-10-15
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2022
  • Entry requirements:

    120 credits. Analysis of Regression participation. Proficiency in English equivalent to the Swedish upper secondary course English 6.

  • Responsible department: Department of Mathematics

Learning outcomes

On completion of the course, the student should be able to:

  • give an account for the theoretical foundation of Markov Chain Monte Carlo-methods and to use such techniques to solve given statistical problems;
  • give an account for the principles behind random number generators;
  • use simulation methods such as Bootstrap and SIMEX;
  • use EM methods;
  • use non-parametric statistical models;
  • use statistical software, preferably R.


The purpose of the course is to give the student a good overview about statistical techniques that have been developed during the last years due to increasing computer capacity. Resampling techniques, Jack-knife, bootstrap. . EM algorithms. SIMEX methodology. Markov Chain Monte Carlo (MCMC) methods. Random number generators. Smoothing techniques. Kernel estimators, nearest neighbour estimators, orthogonal and local polynomial estimators, wavelet estimators. Splines. Choice of bandwidth and other smoothing parameters. Applications. Use of statistical software.


Lectures, problem solving sessions and computer-assisted laboratory work.


Written examination (8 credit points) at the end of the course as well as assignments (2 credit points) in accordance with instructions at course start.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Reading list

Reading list

Applies from: Autumn 2022

Some titles may be available electronically through the University library.

  • Zwanzig, Silvelyn; Mahjani, Behrang Computer intensive methods in statistics

    Boca Raton: CRC Press, [2020]

    Find in the library

Last modified: 2022-04-26