Computer Engineering MA, Data Mining, 6 credits


Computer Engineering MA, Data Mining, 6 credits

General data

  • Code: DT047A
  • Subject/Main field: Computer Engineering
  • Cycle: Second cycle
  • Credits: 6
  • Progressive specialization: A1N - Second cycle, has only first-cycle course/s as entry requirements
  • Answerable department: Information Systems and Technology
  • Answerable faculty: Faculty of Science, Technology and Media
  • Established: 10/1/2018
  • Date of change: 12/1/2020
  • Version valid from: 1/1/2020


The student should develop a basic understanding of current machine learning techniques for mining large quantities of data. The student should develop skills in finding interesting patterns and building prediction models by explorative data analysis using data analysis tools such as R, Weka or Orange, and preparing input, interpreting output and critically evaluating results. The student should show an ability to apply the skills in a small project in an real-world business or engineering application area such as big data visualization, business intelligence analysis, decision support systems, text/web/sensor/geo data mining, context aware applications, intelligent agents or cognitive radio.

Course objectives

The student should be able to:
• Discuss what real-world applications of data mining that are realistic and ethical
• Mine data using a tool such as the R script language, the Python Orange library, the Weka Java based tool or own implementations of algorithms
• Prepare input, interpret output and evaluate results
• Identify influential variables in a multivariate data set
• Discover patterns by association rule mining and evalute their reliability
• Develop and validate prediction models
• Follow a standard methological process reliable problem analysis, modelling and evaluation
• Apply data mining techniques on a small real-world problem


• Application areas of data mining
• Data and knowledge representation (relations, attributes, sparse data, tables, decision trees, rules)
• Bayesian statistics
• Associative and sequential patterns
• Basic algorithms
• Data clustering
• Data categorization
• Data cleaning
• Data visualization
• Association rules
• Data prediction
• Laboratory exercise on the R, Orange or the Weka data analysis tool
• Project

Entry requirements

120 credits completed courses including the following:

Computer Engineering BA (AB) including Databases, modeling and implementation, 6 credits and Java, 6 credits. Mathematics BA (A), 30 credits, including Mathematical Statistics, 6 credits.

Selection rules and procedures

The selection process is in accordance with the Higher Education Ordinance and the local order of admission.

Teaching form

The course may be offered as a campus course or as a web-based distance course. The student time commitment is estimated to about 160hours.

Examination form

I101: Choice of project, 0.0 Credits
Grade scale: Fail (U) or Pass (G)

L101: Laboratory exercise, 0.5 Credits
Grade scale: Fail (U) or Pass (G)

P101: Project presentation, 2.0 Credits
Grade scale: Fail (U) or Pass (G)

T101: Exam, 3.5 Credits
Grade scale: Seven-grade scale, A, B, C, D, E, Fx and F. Fx and F represent fail levels.

The final grade is based on combined exam and project assessment.

Grading criteria for the subject can be found at

The examiner has the right to offer alternative examination arrangements to students who have been granted the right to special support by Mid Sweden University’s disabilities adviser.

If examination on campus cannot be conducted according to decision by the vice-chancellor, or whom he delegated the right to, the following applies: [Written Exam T101], will be replaced with two parts, online examination and follow-up. Within three weeks of the online examination, a selection of students will be contacted and asked questions regarding the examination. The follow-up consists of questions concerning the execution of the on-line exam and the answers that the student have submitted.

Grading system

Seven-grade scale, A, B, C, D, E, Fx and F. Fx and F represent fail levels.

Course reading

Reference literature

  • Author: Ganguly et al
  • Title: Knowledge discovery from sensor data
  • Edition: 2009 or later

Required literature

  • Author: Witten, Frank, Hall
  • Title: Datamining - Pratical Machine Learning Tolls and Techinques
  • Edition: Third edition 2011 or later
  • Publisher: Elsivier

The page was updated 9/2/2014