CS105 Advanced Programming
CS105 Advanced Programming
Syllabus | International University of Sarajevo - Last Update on Feb 02, 2026
Syllabus Quick Jump
Search and navigate to any syllabus instantly
Computer Sciences and Engineering
Kanita Karađuzović-Hadžiabdić
Course Lecturer
Course Objectives
The aims of this course are to: teach students main object-oriented concepts and practices, introduce students to one object-oriented programming language, teach students some of the fundamental data structures and algorithms.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Walter Savitch, Absolute Java, 6th Edition Pearson; Carrano, Data Structures and Abstractions with Java, 4th Edition
Additional Literature
-Teaching Methods
Lectures
Class discussion
Practical work
Lab exercises
Homework exercises
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to the course, Intro to Java and Classes; UNGRADED LAB | Chapter 1,2, 3 (Savitch) |
| 2 | Intro to Classes, Methods, Encapsulation, Overloading Constructors; UNGRADED LAB | Chapter 4 (Savitch) |
| 3 | Static keyword, Wrapper Classes, References, Copy Constructors, Package; UNGRADED LAB | Chapter 5 (Savitch) |
| 4 | Copy Constructors (cont.) Inheritance, PART I; UNGRADED LAB | Chapter 5,7 (Savitch) |
| 5 | Inheritance, PART II; GRADED LAB | Chapter 7 (Savitch) |
| 6 | Polymorphism and Abstract Classes; UNGRADED LAB | Chapter 8 (Savitch) |
| 7 | ExceptionHandling GRADED LAB | Chapter 9, 10 (Savitch) |
| 8 | MIDTERM WEEK; NO LAB | |
| 9 | Intro to UML (Basics of class idagram), Debugging, Swing part I: UNGRADED LAB | Chapter 12, 17 (Savitch) |
| 10 | Swing part II, Interfaces (short Intro), UNGRADED LAB | Chapter 17 |
| 11 | Data Strucures (Bags, Array implementation); GRADED LAB | Chapter1, 2 (Carrano) |
| 12 | Data Strucures (Bags, Link Implementation, Intro to Stacks); GRADED LAB | Chapter 3,5, (Carrano) |
| 13 | Data Structures (Stack implementations); QUIZ; UNGRADED LAB | Chapter 6, (Carrano) |
| 14 | Data Structures (Intro to Queues, Deques, Priority Queues) UNGRADED LAB | Chapter 10 (Carrano) |
| 15 | Data Structures Continuation: Intro to Queues, Deques, Priority Queues), Exam Prep.; UNGRADED LAB | Chapter 10 (Carrano) |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| CS105.1 | Course | Monday 15:00 - 17:50 | B F1.23 - Amphitheater I | - | - |
| CS105.2 | Course | Wednesday 09:00 - 11:50 | B F1.23 - Amphitheater I | - | - |
| CS105.1 | Tutorial | Thursday 16:00 - 17:50 | A F1.3 - Computer Lab | - | - |
| CS105.2 | Tutorial | Thursday 10:00 - 11:50 | RC1.4 - Computer Laboratory | - | - |
| CS105.3 | Tutorial | Friday 14:00 - 15:50 | A F1.3 - Computer Lab | - | - |
| CS105.4 | Tutorial | Friday 10:00 - 11:50 | A F1.3 - Computer Lab | - | - |
| CS105.5 | Tutorial | Thursday 14:00 - 15:50 | B F1.25 Computer Lab | - | - |
| CS105.6 | Tutorial | Thursday 12:00 - 13:50 | A F1.4 - Class/Laboratory | - | - |
| CS105.7 | Tutorial | Friday 09:00 - 10:50 | A F1.4 - Class/Laboratory | - | - |
| CS105.8 | Tutorial | Friday 14:00 - 15:50 | A F1.18 - Computer Lab | - | - |
| CS105.9 | Tutorial | Tuesday 12:00 - 13:50 | B F1.25 Computer Lab | - | - |
| CS105.10 | Tutorial | Friday 12:00 - 13:50 | A F1.3 - Computer Lab | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Tuesday | 10:00 - 12:00 | B F3.13 | |
| Wednesday | 14:00 - 17:00 | B F3.13 |
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
Midterm exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2
Lab
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
Quiz
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
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)
Labs
28 hours ⏳ (14 week × 2 h)
Home Study
45 hours ⏳ (15 week × 3 h)
Midterm exam
10 hours ⏳ (1 week × 10 h)
Final exam study
13 hours ⏳ (1 week × 13 h)
Quiz
9 hours ⏳ (1 week × 9 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 [CS105] 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 Feb 02, 2026 | International University of Sarajevo
Print Syllabus
Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| CS105 | Advanced Programming | 3 | 2 | 6 | Mon 15:00 - 17:50, Wed 9:00 -11:50 | |||||
| Prerequisite | CS103 | It is a prerequisite to | CS206, CS302, CS304, CS306, CS313, CS402, CS412, CS413, SE302, SE304, SE322, SE401, SE402, SE403, SE404, SE406, SE423 | |||||||
| Lecturer | Kanita Karađuzović-Hadžiabdić | Office Hours / Room / Phone | Tuesday: 10:00-12:00 Wednesday: 14:00-17:00 |
|||||||
| kanita@ius.edu.ba | ||||||||||
| Assistant | Ismar Aganovic, Nedim Redzovic, Amar Mulaosmanovic | Assistant E-mail | iaganovic@student.ius.edu.ba | |||||||
| Course Objectives | The aims of this course are to: teach students main object-oriented concepts and practices, introduce students to one object-oriented programming language, teach students some of the fundamental data structures and algorithms. |
|||||||||
| Textbook | Walter Savitch, Absolute Java, 6th Edition Pearson; Carrano, Data Structures and Abstractions with Java, 4th Edition | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | Lectures, Class discussion, Practical work, Lab exercises, Homework exercises | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction to the course, Intro to Java and Classes; UNGRADED LAB | Chapter 1,2, 3 (Savitch) | ||||||||
| Week 2 | Intro to Classes, Methods, Encapsulation, Overloading Constructors; UNGRADED LAB | Chapter 4 (Savitch) | ||||||||
| Week 3 | Static keyword, Wrapper Classes, References, Copy Constructors, Package; UNGRADED LAB | Chapter 5 (Savitch) | ||||||||
| Week 4 | Copy Constructors (cont.) Inheritance, PART I; UNGRADED LAB | Chapter 5,7 (Savitch) | ||||||||
| Week 5 | Inheritance, PART II; GRADED LAB | Chapter 7 (Savitch) | ||||||||
| Week 6 | Polymorphism and Abstract Classes; UNGRADED LAB | Chapter 8 (Savitch) | ||||||||
| Week 7 | ExceptionHandling GRADED LAB | Chapter 9, 10 (Savitch) | ||||||||
| Week 8 | MIDTERM WEEK; NO LAB | |||||||||
| Week 9 | Intro to UML (Basics of class idagram), Debugging, Swing part I: UNGRADED LAB | Chapter 12, 17 (Savitch) | ||||||||
| Week 10 | Swing part II, Interfaces (short Intro), UNGRADED LAB | Chapter 17 | ||||||||
| Week 11 | Data Strucures (Bags, Array implementation); GRADED LAB | Chapter1, 2 (Carrano) | ||||||||
| Week 12 | Data Strucures (Bags, Link Implementation, Intro to Stacks); GRADED LAB | Chapter 3,5, (Carrano) | ||||||||
| Week 13 | Data Structures (Stack implementations); QUIZ; UNGRADED LAB | Chapter 6, (Carrano) | ||||||||
| Week 14 | Data Structures (Intro to Queues, Deques, Priority Queues) UNGRADED LAB | Chapter 10 (Carrano) | ||||||||
| Week 15 | Data Structures Continuation: Intro to Queues, Deques, Priority Queues), Exam Prep.; UNGRADED LAB | Chapter 10 (Carrano) | ||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | 1,2,3,4 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm exam | 1 | 30 | 1,2 | Not Allowed | |
| Lab | 4 | 20 | 1,2,3,4 | Not Allowed | |
| Quiz | 1 | 10 | 1,2,3,4 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Labs | 2 | 14 | 28 | |||
| Home Study | 3 | 15 | 45 | Midterm exam | 10 | 1 | 10 | |||
| Final exam study | 13 | 1 | 13 | Quiz | 9 | 1 | 9 | |||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 22/02/2026 | |||||||||