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Advanced Data Mining 7.5 credits

Avancerad Data Mining
Second cycle, D7043E
Course syllabus valid: Autumn 2022 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
Second cycle
Grade scale
U G VG *
Computer Science and Engineering
Subject group (SCB)
Computer Technology
Main field of study
Information Systems Sciences

Entry requirements

In order to meet the general entry requirements for the course, you must have accomplished a minimum of 120 ECTS of university studies, out of which 60 ECTS in the areas of computer or system science. The studies shall have included Introductory Programming (for example D0009E Introduction to Programming or D0007N Objectoriented programming) and Fundamentals of Databases (for example D0004N Database Systems I or D0018E Database Technology) . The Advanced Data Mining Course also requires the completion of a basic data mining course such as D0025E Data Mining. Knowledge in English, equivalent to English 6.

More information about English language requirements


The selection is based on 20-285 credits

Course Aim

The objective of the course is for the student to develop their knowledge and skills in Advanced Data Mining. After passing the course, the student should be able to:

[1].  Use the advanced data mining concepts & techniques  

[2].  Explain how those concepts and techniques work

[3].  Explain how concepts and techniques are, or should be, used in organizations

[4].  Evaluate results of applying the concepts and techniques discussed above

[5].  Analyze and reflect on the relationship between the techniques, the dataset, the problem or opportunity in
       hand, and the tools and technology used


The course is an advanced course in data mining. The course provides knowledge to address various data science problems and datasets. Focus lies on advanced machine learning techniques for classification, regression, clustering, and anomaly detection, for example decision trees, random forests, neural networks, including Support Vector Machines and Deep Learning, Expectation Maximization (EM), Markov models, and Bayesian networks.

Each course occasion´s language and form is stated and appear on the course page on Luleå University of Technology's website.

Lectures, labs, assignments, case studies and project work. Laboratory work requires access to very high computational capacity. During the course, the students work in small groups. Some assignments or case studies in the course might contain work in contact with or about the industry. The student uses different methods and techniques, and it is important to choose the right method, technique or computer support for each task. Before and after the tasks are solved, there are lectures to present and discuss different solutions. 

If there is a decision on special educational support, in accordance with the Guideline Student's rights and obligations at Luleå University of Technology, an adapted or alternative form of examination can be provided.

Through individual tests and group/project assignment, different student abilities are examined. Those are: the ability to explain and use advanced data mining techniques and the ability to solve business problems using data mining individually and in groups.


Technical Requirements: access to PC with Windows XP, microphone, Web cam and permission to install software. Internet connection (minimum 0,5 Mbps).

Rajkumar Saini

Literature. Valid from Autumn 2019 Sp 1
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
0001Individual examU G VG4.00MandatoryA19
0002Individual tasksU G#1.50MandatoryA19
0003Group/Project workU G#2.00MandatoryA19

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 21 Nov 2018

Last revised
by Jonny Johansson, HUL SRT 17 Jun 2022