Syllabus for Data Mining I

Informationsutvinning I


  • 5 credits
  • Course code: 1DL360
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Computer Science A1N, Technology A1N
  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2010-03-18
  • Established by:
  • Revised: 2019-02-19
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: week 27, 2019
  • Entry requirements: 120 credits with 30 credits in mathematics, including mathematical statistics, 45 credits in computer science and/or engineering, including Database Design I and a second course in computer programming. Algorithms and Data Structures I is recommended.
    English language proficiency that corresponds to English studies at upper secondary (high school) level in Sweden ("English 6").
  • Responsible department: Department of Information Technology

Learning outcomes

On completion of the course the student shall be able to:

  • explain different methods to extract processed information from large amounts of data, both in theory and in practical application
  • use these methods with appropriate tools
  • evaluate and compare the suitability of different methods.


Introduction to data mining, its terminology and overview over various types of data and its properties, an overview of different methods to explore and visualise large amounts of data, introduction to classification methods, introduction to clustering methods, introduction to association analysis, handling of personal integrity in the area of data mining.

The subjects are treated both theoretically and practically through laboratory sessions where selected methods are implemented and tested on typical amounts of data.


Lectures, seminars, laboratory sessions and project. Guest lecture.


Written examination and a project that is presented orally and in writing. 
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: week 28, 2019

  • Introduction to data mining Tan, Pang-Ning; Steinbach, Michael; Karpatne, Anuj; Kumar, Vipin

    Second edition.: Harlow: Pearson Education, 2020

    Find in the library


Reading list revisions