AID404 Business Intelligence
Syllabus | International University of Sarajevo - Last Update on Sep 09, 2025
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Artificial Intelligence and Data Engineering
Özge Büyükdağlı
Course Lecturer
Course Objectives
The course will examine the utilization of business information systems and business intelligence for empowering business professionals to analyze business operations and enhance decision-making. It will introduce students to the latest concepts, processes, and technologies in the fields of business intelligence and business analytics. By the end of the course, students will gain the skills to apply specific analytics tools, interpret solutions to business problems, and offer relevant business advice under different settings.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
1. Sharda, R., Delen, D., Turban, E. 2017, Business Intelligence: A Managerial Approach, 4th. ed, Pearson. 2. Sharda, R., Delen, D., Turban, E. 2018, Business Intelligence, Analytics, and Data Science - A Managerial Approach, 4th. ed, Pearson.
Additional Literature
1. Fortino, A. 2023, Data Mining and Predictive Analytics for Business Decisions – A Case Study Approach, Mercury Learning and Information.Teaching Methods
Combination of lectures (theory and explaining the background of the topic) and practical exercises (practical work by programming and practicing by using the learned algorithms to a real-world dataset)
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to Business Intelligence | |
| 2 | Data Warehousing Definitions and Concepts | |
| 3 | Business Performance Management: Performance Measurement, BPM Methodologies | |
| 4 | Business Performance Management: BPM Technologies and Applications | |
| 5 | Performance Dashboards and Scorecards: Data Visualization | |
| 6 | Data Mining for Business Intelligence: Data Mining Methods | |
| 7 | Data Mining for Business Intelligence: Data Mining Software Tools, Data Mining Applications | |
| 8 | MIDTERM | |
| 9 | Text and Web Mining: Natural Language Processing, Text Mining Applications | |
| 10 | Case Studies on Web Mining, | |
| 11 | Business Intelligence Implementation: Integration and Emerging Trends | |
| 12 | Connecting Bl Systems to Databases and Other Enterprise Systems | |
| 13 | Social Networks and Bl: Collaborative Decision Making | |
| 14 | Review/Project Presentations | |
| 15 | Big Data Concepts and Tools |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| AID404.1 | Course | Monday 14:00 - 16:50 | B F1.25 Computer Lab | - | - |
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes :
Midterm
AI: Not AllowedAlignment with Learning Outcomes :
Quizzes
AI: Not AllowedAlignment with Learning Outcomes :
Term project and presentation
AI: Not AllowedAlignment with Learning Outcomes :
Lab assignments
AI: Not AllowedAlignment with Learning Outcomes :
IUS Grading System
| Grading Scale | IUS Grading System | IUS Coeff. | Letter (B&H) | Numerical (B&H) |
|---|---|---|---|---|
| 0 - 44 | F | 0 | F | 5 |
| 45 - 54 | E | 1 | ||
| 55 - 64 | C | 2 | E | 6 |
| 65 - 69 | C+ | 2.3 | D | 7 |
| 70 -74 | B- | 2.7 | ||
| 75 - 79 | B | 3 | C | 8 |
| 80 - 84 | B+ | 3.3 | ||
| 85 - 94 | A- | 3.7 | B | 9 |
| 95 - 100 | A | 4 | A | 10 |
Late Work Policy
Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.
ECTS Credit Calculation
📚 Student Workload
This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:
Lecture hours
42 hours ⏳ (14 week × 3 h)
Assignments
21 hours ⏳ (7 week × 3 h)
Active labs
28 hours ⏳ (14 week × 2 h)
Home study
14 hours ⏳ (14 week × 1 h)
In-term exam study
10 hours ⏳ (1 week × 10 h)
Final exam study
11 hours ⏳ (1 week × 11 h)
Term project/presentation
24 hours ⏳ (12 week × 2 h)
150 Total Workload Hours
6 ECTS Credits
Course Policies
Academic Integrity
All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.
Attendance Policy
Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.
Technology & AI Policy
Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.
Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.
Communication Policy
All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [AID404] in the subject line.
Academic Quality Assurance Policy
Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
Learning Tips
Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.
Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.
Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.
Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.
Syllabus Last Updated on Sep 09, 2025 | International University of Sarajevo
Print Syllabus
Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| AID404 | Business Intelligence | 3 | 2 | 6 | ||||||
| Prerequisite | None | It is a prerequisite to | - | |||||||
| Lecturer | Özge Büyükdağlı | Office Hours / Room / Phone | ||||||||
| obuyukdagli@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | The course will examine the utilization of business information systems and business intelligence for empowering business professionals to analyze business operations and enhance decision-making. It will introduce students to the latest concepts, processes, and technologies in the fields of business intelligence and business analytics. By the end of the course, students will gain the skills to apply specific analytics tools, interpret solutions to business problems, and offer relevant business advice under different settings. | |||||||||
| Textbook | 1. Sharda, R., Delen, D., Turban, E. 2017, Business Intelligence: A Managerial Approach, 4th. ed, Pearson. 2. Sharda, R., Delen, D., Turban, E. 2018, Business Intelligence, Analytics, and Data Science - A Managerial Approach, 4th. ed, Pearson. | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | Combination of lectures (theory and explaining the background of the topic) and practical exercises (practical work by programming and practicing by using the learned algorithms to a real-world dataset) | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction to Business Intelligence | |||||||||
| Week 2 | Data Warehousing Definitions and Concepts | |||||||||
| Week 3 | Business Performance Management: Performance Measurement, BPM Methodologies | |||||||||
| Week 4 | Business Performance Management: BPM Technologies and Applications | |||||||||
| Week 5 | Performance Dashboards and Scorecards: Data Visualization | |||||||||
| Week 6 | Data Mining for Business Intelligence: Data Mining Methods | |||||||||
| Week 7 | Data Mining for Business Intelligence: Data Mining Software Tools, Data Mining Applications | |||||||||
| Week 8 | MIDTERM | |||||||||
| Week 9 | Text and Web Mining: Natural Language Processing, Text Mining Applications | |||||||||
| Week 10 | Case Studies on Web Mining, | |||||||||
| Week 11 | Business Intelligence Implementation: Integration and Emerging Trends | |||||||||
| Week 12 | Connecting Bl Systems to Databases and Other Enterprise Systems | |||||||||
| Week 13 | Social Networks and Bl: Collaborative Decision Making | |||||||||
| Week 14 | Review/Project Presentations | |||||||||
| Week 15 | Big Data Concepts and Tools | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 30 | Not Allowed | ||
| Semester Evaluation Components | |||||
| Midterm | 1 | 25 | Not Allowed | ||
| Quizzes | 3 | 15 | Not Allowed | ||
| Term project and presentation | 1 | 15 | Not Allowed | ||
| Lab assignments | 7 | 15 | Not Allowed | ||
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture hours | 3 | 14 | 42 | Assignments | 3 | 7 | 21 | |||
| Active labs | 2 | 14 | 28 | Home study | 1 | 14 | 14 | |||
| In-term exam study | 10 | 1 | 10 | Final exam study | 11 | 1 | 11 | |||
| Term project/presentation | 2 | 12 | 24 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 19/09/2025 | |||||||||