Course title

Decision-making support systems

Lecture type

Elective

Course code

22-00-525

Semester

5

ECTS

6

Lecturers and associates

• Associate Professor PhD Goran Klepac
• Mateo Martinović, Instructor
• Zlatan Morić, Senior Lecturer

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