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Decision-making support systems

  • Class 30
  • Practice 30
  • Independent work 120
Total 180

Course title

Decision-making support systems

Lecture type

Elective

Course code

22-00-525

Semester

5

ECTS

6

Lecturers and associates

Course overview

This module is designed to enable students to learn the knowledge, and understanding to apply data mining techniques to solve business problems.

Students learn toidentify and understand basic algorithms for automatic data processing. Data mining results in a predictive model, but applications are far wider than the prediction itself, so it is used for any input and output mapping that is too hard to manually input or for which there are no clearly defined rules to be entered, or these rules change too often.

The objectives of this module are to enable students to:
• Evaluate decision support systems.
• Calculate and interpret results from attribute relevance analysis.
• Prepare data for modelling and make descriptive statistical analysis.
• Implement data mining model to solve business problem.

It is important for students to take this module to adopt basics of decision support systems, methods and tools, which include applying advanced analytical techniques, attribute relevance analysis with aim of constructing decision support system.

Literature

Essential reading:
1. Sharda, R., Delen, D. and Turban, E., (2020). Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support, London: Pearson

Recommended reading:
1. Tan, P., Steinbach, M. and Karpatne, A. (2019). Introduction to Data Mining, London: Pearson

Further reading:
1. Klepac, G., Kopal, R., and Mršić, L. (2015). Developing Churn Models Using Data Mining Techniques and Social Network Analysis (pp. 1-361). Hershey, PA: IGI

Download student guide

Minimal learning outcomes

  • Classify decision support system elements.
  • Attribute relevance analysis calculation.
  • Evaluate usage of specific quantitative method within decision support system.
  • Select adequate quantitative method for solving problems in domain of decision support Systems.
  • Analyse data and create a holistic solution.

Preferred learning outcomes

  • Classify complex decision support system elements.
  • Attribute relevance analysis calculation and explanation.
  • Evaluate usage of specific quantitative method within decision support system and justify usage of chosen method.
  • Select of adequate quantitative method for solving problems in domain of decision support systems and solution proposal.
  • Analyse data and create a holistic solution with an explanation of causalities.
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