MK EN
ФИНКИ

Subject

Data-Driven Business Decision-Making Systems

1. Course Title Data-Driven Business Decision-Making Systems
Systems for data-based business decision-making
2. Code m23_w_033
3. Study Programme —
4. Organizer of the study programme (unit, institute, department or division) Faculty of Computer Science and Engineering
5. Degree level (first, second, third cycle) Second cycle
6. Academic year / semester 9 / Winter
7. Number of ECTS credits 6
8. Teacher Andrea Kulakov, Georgina Mircheva
9. Prerequisites for enrolling in the course —
10. Objectives of the course programme (competences) In this course, students will acquire knowledge of data-driven business decision-making. Students will become familiar with the methods and techniques for representing knowledge, decision-making, the design, development, and evaluation of data-driven business decision-making systems. Upon completion of this course, students will gain in-depth knowledge of techniques and methods for business decision-making, proper selection of methods and techniques, design and development of business decision-making systems, and their application in various applications. Students will gain practical knowledge through a case study analysis.
11. Course content Decision making, introduction, concepts, business decisions. Collection and representation of business data. Methods and techniques for modeling in business decision making. Decision support systems, introduction, concepts, categorization. Design and development of decision support systems. Software tools and environments for design and development. Analysis of business decisions. Evaluation of decision outcomes. Business decision making in organizational management. Business decision making in marketing, sales, and e-commerce. Business decision making for customer relationship management. Business decision making in manufacturing and innovation. Business decision making for optimization, scheduling, and planning.
12. Learning methods Lectures supported by slide presentations, interactive lectures, practical exercises
13. Total available time 6 ECTS x 30 hours = 180 hours
14. Distribution of available time 60 + 0 + 45 + 45 + 30 = 180 hours
15. Forms of teaching activities
15.1. Lectures - theoretical instruction 60 hours
15.2. Exercises (laboratory, auditory), seminars, teamwork 0 hours
16. Other forms of activities
16.1. Project assignments 45 hours
16.2. Independent assignments 45 hours
16.3. Home study 30 hours
17. Assessment method
17.1. Tests 15 points
17.2. Seminar paper / project (presentation: written and oral) 45 points
17.3. Activities and learning 15 points
17.4. Final exam 0 points
18. Grading criteria (points / grade)
up to 50 points5 (five) (F)
from 51 to 60 points6 (six) (E)
from 61 to 70 points7 (seven) (D)
from 71 to 80 points8 (eight) (C)
from 81 to 90 points9 (nine) (B)
from 91 to 100 points10 (ten) (A)
19. Requirement for obtaining a signature and taking the final exam —
20. Language of instruction Macedonian and English
21. Method for monitoring the quality of teaching internal evaluation and survey mechanism
22. Literature
22.1. Required literature
1. Efraim Turban, Jay E. Aronson, Ting-Peng Liang, and Ramesh Sharda | Decision Support and Business Intelligence Systems | Prentice Hall | 2011
2. Daniel J. Power | Decision Support Systems: Concepts and Resources for Managers | Greenwood Publishing Group | 2002
3. Vicki L. Sauter | Decision Support Systems for Business Intelligence | John Wiley & Sons | 2011
4. George M. Marakas | Decision Support Systems | Prentice Hall | 2002
5. Sharda, R., Delen, D., Turban, E. | Business Intelligence, A Managerial Perspective on Analytics | Pearson | 2013
6. Gerardus Blokdyk | Decision Support System: A Complete Guide - 2020 Edition | 5STARCooks | 2021
22.2. Additional literature
No. Author Title Publisher Year
Деканат Dean’s Office Студии Studies Акти Acts Проекти Projects