CS206 Data Structures
CS206 Data Structures
Syllabus | International University of Sarajevo - Last Update on Sep 09, 2025
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Computer Sciences and Engineering
Emine Yaman
Course Lecturer
Course Objectives
The objective of the course is to introduce and train students in the design and implementation of numerous basic and complex data structures in the program implementation It also intends to introduce different methods of representing data in real-world scenarios.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
- Data Structures & Algorithms in Java, 2nd Edition, Robert Lafore, SAMS - Discrete and Combinatorial Mathematics: An Applied Introduction by Ralph P. Grimaldi, 5th Edition
Additional Literature
Data Structures and Algorithms Made Easy in Java, Narasimha Karumanchi, CareerMonk PublicationsTeaching Methods
Lectures
Tutorials
Class discussions with examples
Homeworks
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Course Logistics, Introduction | |
| 2 | Arrays | |
| 3 | Time Complexity | |
| 4 | Stacks and Queues | |
| 5 | Linked Lists | |
| 6 | Simple Sorting | |
| 7 | Recursion | |
| 8 | Midterm Exam | |
| 9 | Advanced Sorting | |
| 10 | Binary Trees | |
| 11 | Binary Search Trees and AVL Trees | |
| 12 | Hash Tables | |
| 13 | Heaps | |
| 14 | Graphs | |
| 15 | Final Review |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| CS206.1 | Course | Tuesday 14:00 - 16:50 | A F2.14 - Amphitheater II | - | - |
| CS206.1 | Tutorial | Wednesday 13:00 - 14:50 | A F1.24 - Amphitheater I | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Wednesday | 10:00 - 12:00 | A F1.34 | |
| Thursday | 10:00 - 12:00 | A F1.34 | |
| Friday | 10:00 - 12:00 | A F1.34 |
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Midterm
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Quizzes
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
Assignments
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5
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
45 hours ⏳ (15 week × 3 h)
Tutorials
24 hours ⏳ (12 week × 2 h)
Midterm Study
12 hours ⏳ (1 week × 12 h)
Final Exam Study
12 hours ⏳ (1 week × 12 h)
Home Study
45 hours ⏳ (15 week × 3 h)
Homework Study
12 hours ⏳ (2 week × 6 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 [CS206] 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
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Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| CS206 | Data Structures | 3 | 2 | 6 | Tuesday 14:00-16:50 | |||||
| Prerequisite | CS105 | It is a prerequisite to | CS207, CS305, CS417 | |||||||
| Lecturer | Emine Yaman | Office Hours / Room / Phone | Wednesday: 10:00-12:00 Thursday: 10:00-12:00 Friday: 10:00-12:00 |
|||||||
| eyaman@ius.edu.ba | ||||||||||
| Assistant | Harun Hadzo | Assistant E-mail | hhadzo@ius.edu.ba | |||||||
| Course Objectives | The objective of the course is to introduce and train students in the design and implementation of numerous basic and complex data structures in the program implementation It also intends to introduce different methods of representing data in real-world scenarios. | |||||||||
| Textbook | - Data Structures & Algorithms in Java, 2nd Edition, Robert Lafore, SAMS - Discrete and Combinatorial Mathematics: An Applied Introduction by Ralph P. Grimaldi, 5th Edition | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | Lectures, tutorials, class discussions with examples, homeworks | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Course Logistics, Introduction | |||||||||
| Week 2 | Arrays | |||||||||
| Week 3 | Time Complexity | |||||||||
| Week 4 | Stacks and Queues | |||||||||
| Week 5 | Linked Lists | |||||||||
| Week 6 | Simple Sorting | |||||||||
| Week 7 | Recursion | |||||||||
| Week 8 | Midterm Exam | |||||||||
| Week 9 | Advanced Sorting | |||||||||
| Week 10 | Binary Trees | |||||||||
| Week 11 | Binary Search Trees and AVL Trees | |||||||||
| Week 12 | Hash Tables | |||||||||
| Week 13 | Heaps | |||||||||
| Week 14 | Graphs | |||||||||
| Week 15 | Final Review | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 35 | 1,2,3,4,5 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm | 1 | 30 | 1,2,3,4,5 | Not Allowed | |
| Quizzes | 2 | 15 | 1,2,3,4,5 | Not Allowed | |
| Assignments | 1 | 20 | 1,2,3,4,5 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Tutorials | 2 | 12 | 24 | |||
| Midterm Study | 12 | 1 | 12 | Final Exam Study | 12 | 1 | 12 | |||
| Home Study | 3 | 15 | 45 | Homework Study | 6 | 2 | 12 | |||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 23/09/2025 | |||||||||