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Data Mining 7.5 credits

Data Mining
First cycle, D0025E
Course syllabus valid: Autumn 2021 Sp 1 - Present
The version indicates the term and period for which this course syllabus is valid. The most recent version of the course syllabus is shown first.

Education level
First cycle
Grade scale
U G VG *
Systems Science
Subject group (SCB)
Informatics/Computer and Systems Sciences
Main field of study
Information Systems Sciences

Entry requirements

In order to meet the general entry requirements for first cycle studies you must have successfully completed upper secondary education and documented skills in English language and at least 60 credits in completed courses in which the following knowledge must be included, knowledge in Programming (for example D0019N Software Development with Java or D0007N Objectoriented programming) and Fundamentals of Databases (for example D0004N Database Systems I or D0018E Database technology). Knowledge in English, equivalent to English 6.

More information about English language requirements


The selection is based on 1-165 credits.

Course Aim

Data mining is the discovery of patterns and hidden information in large datasets. This course aims at the understanding of the data mining concepts and techniques. The course provides students with the detail about most aspects of data mining and knowledge discovery, focusing on techniques and algorithms in respect to how they are used to solve business problems.

Upon completion of the course, the student will be able to:

  1. Understand what data mining is;
  2. Differentiate between knowledge discovery in databases and data mining;
  3. Describe data mining as a process;
  4. Explain the CRISP-DM process;
  5. Describe the different applications where data mining is used;
  6. Understand the different data mining techniques and algorithms;
  7. Analyse the performance of data mining techniques and algorithms;
  8. Evaluate mining outcomes;
  9. Explain the relationship between data mining and big data [analytics];
  10. Understand how to formulate and solve business problems using data mining. 


    The data mining course will cover a number of topics, including evaluating data that is to be mined and data mining strategies. The techniques will be studied in association with the algorithms needed to implement them. The course will also rely on business cases. That is, each technique will be studied in association with a business scenario. This will enhance understanding of techniques and equip the learner with the necessary knowledge and skills required to formulate and solve mining problems. 


    During the course, students will work on individual tasks and a group task. For group work, students will collaborate with each other using a variety of collaboration tools. Also, students will be provided access to Rapid Miner, one of the world’s leading mining tools for solving business problems and cases.

    Teaching is in English and on the Internet for distance students or on campus for the students living here. IT support: Learning management system, e-mail and phone.

    A learning management system is used for delivering course material, information and submissions. Knowledge is shared and created within the course through virtual meetings with teachers and other students for discussions, supervision, teamwork and seminars. For students living here, there will be meetings on campus. 

    Individual and group tasks 2.5 hp, U G

    Written examination, 5 hp U G VG
    All students, both on distance and campus, write the individual exam online, webcam and microphone are required.

    Ahmed Elragal

    Transition terms
    The course D0025E is equal to D7040E

    Literature. Valid from Autumn 2017 Sp 1 (May change until 10 weeks before course start)
    Pang-Ning Tan, Michael Steinbach, and Vipin Kumar: Introduction to Data Mining, Addison Wesley, 2005

    Course offered by
    Department of Computer Science, Electrical and Space Engineering

    CodeDescriptionGrade scaleCrStatusFrom periodTitle
    0001Project work/individual assignmentsU G#2.50MandatoryA17
    0002Written examU G VG *5.00MandatoryA17

    Study guidance
    Study guidance for the course is to be found in our learning platform Canvas before the course starts. Students applying for single subject courses get more information in the Welcome letter. You will find the learning platform via My LTU.

    Syllabus established
    by Jonny Johansson, HUL SRT 15 Feb 2017

    Last revised
    by Jonny Johansson HUL, SRT 08 Dec 2020