Syllabus for Computer-Intensive Statistics and Data Mining DS

Datorintensiv statistik och informationsutvinning DS


  • 7.5 credits
  • Course code: 1MS043
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Mathematics A1N, Data Science A1N, Computer Science A1N
  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2021-03-04
  • Established by:
  • Revised: 2023-02-09
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2023
  • Entry requirements:

    120 hp. Regression analysis or participation in Introduction to data science. Proficiency in English equivalent to the Swedish upper secondary course English 6.

  • Responsible department: Department of Mathematics

Learning outcomes

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.

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

  • give an account for the principles behind random number generators;
  • give an account for the principle for simulation using Monte Carlo methods;
  • apply methods for computer-intensive estimation methods, such as bootstrap;
  • use Expectation Maximization (EM)-algorithms;-s
  • apply the Monte Carlo methodology to solve real-world problems;


Random number generators and simulation of random objects. Monte Carlo methods, including Markov Chain Monte Carlo and quasi-Monte Carlo methods. Estimation techniques such as for example Kernel estimators. Expectation Maximization-algorithms. Applications. The course contains an applied project part with specialization within a defined area within data analysis.



Lectures and computer-assisted laboratory work.


Written assignments (5 credits) and project work which is presented orally (2.5 credits).

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..

Other directives

This course cannot be included in the same degree as the course ​Computer-Intensive Statistics and Data Mining 10 credits or Computer-Intensive statistics with applications 10 credits.

Reading list

Reading list

Applies from: Autumn 2023

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