AID101 Fundamentals of Data Science
AID101 Fundamentals of Data Science
Syllabus | International University of Sarajevo - Last Update on Feb 02, 2026
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Artificial Intelligence and Data Engineering
Emine Yaman
Course Lecturer
Course Objectives
The purpose of this course is to provide students with the basic statistical and logical knowledge needed to understand data science. The course forms the basis for many other courses needed for data science and allows students to understand and develop many of the methods they use in data mining. It will introduce students to the latest concepts, principles and tools of data science including data types, data structures, data manipulation and visualization techniques etc. The course emphasizes a hands-on approach to learning data skills, offering a number of interactive exercises by using real-life datasets from a variety of disciplines with the aim of applying many techniques and concepts. By the end of the course, students will improve their theoretical and practical knowledge. Students will gain the skills to apply specific analytics tools and interpret solutions to many problems.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
OpenIntro Statistics - Fourth Edition, David M. Diez, Christopher D. Barr, Mine Cetinkaya-Rundel, Copyright Year: 2015, Last Update: 2019 Publisher: OpenIntro
Additional Literature
Pang Ning Tan, Michael Steinbach, Vipin Kumar, 2005, Second Edition, Introduction to Data Mining, Pearson, 2005.Teaching Methods
Combination of lectures (theory and explaining the background of the topic) and practical exercises (practical work by using software tools and practicing by using the learned algorithms to a real-world dataset)
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to Data Science | Chapter 1 (Main book) |
| 2 | Data | Chapter 2 (Supplementary book) |
| 3 | Mathematical Preliminaries | Chapter 2 (Supplementary book) |
| 4 | Summarizing Data | Chapter 2 (Main book) |
| 5 | Probability | Chapter 3 (Main book) |
| 6 | Statistical Distributions | Chapter 4 (Main book) |
| 7 | Review, Preparation for Midterm Exam | |
| 8 | MIDTERM EXAM | |
| 9 | Foundations for Inference | Chapter 5 (Main book) |
| 10 | Inference for Categorical Data | Chapter 6 (Main book) |
| 11 | Inference for Numerical Data | Chapter 7 (Main book) |
| 12 | Introduction to Linear Regression | Chapter 8 (Main book) |
| 13 | Multiple and Logistic Regression | Chapter 9 (Main book) |
| 14 | Data Science Ethics and Social Issues | External Sources |
| 15 | Review, Preparation for Final Exam |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| AID101.1 | Course | Monday 11:00 - 13:50 | A F2.13 | - | - |
| AID101.1 | Tutorial | Thursday 14:00 - 15:50 | A F1.4 - Class/Laboratory | - | - |
| AID101.2 | Tutorial | Friday 12:00 - 13:50 | A F1.18 - Computer Lab | - | - |
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
Midterm
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3
Quizzes
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
Homeworks
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
Lab assignments
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)
Home Study
30 hours ⏳ (15 week × 2 h)
Active labs
20 hours ⏳ (10 week × 2 h)
Final Exam Preparation
15 hours ⏳ (1 week × 15 h)
Midterm exam Preparation
11 hours ⏳ (1 week × 11 h)
Quizzes Preparation
14 hours ⏳ (2 week × 7 h)
Homeworks
15 hours ⏳ (5 week × 3 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 [AID101] 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 | |||||||||
| AID101 | Fundamentals of Data Science | 3 | 2 | 6 | Monday 11:00-13:50 | |||||
| Prerequisite | None | It is a prerequisite to | - | |||||||
| 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 Hadžo | Assistant E-mail | hhadzo@ius.edu.ba | |||||||
| Course Objectives | The purpose of this course is to provide students with the basic statistical and logical knowledge needed to understand data science. The course forms the basis for many other courses needed for data science and allows students to understand and develop many of the methods they use in data mining. It will introduce students to the latest concepts, principles and tools of data science including data types, data structures, data manipulation and visualization techniques etc. The course emphasizes a hands-on approach to learning data skills, offering a number of interactive exercises by using real-life datasets from a variety of disciplines with the aim of applying many techniques and concepts. By the end of the course, students will improve their theoretical and practical knowledge. Students will gain the skills to apply specific analytics tools and interpret solutions to many problems. |
|||||||||
| Textbook | OpenIntro Statistics - Fourth Edition, David M. Diez, Christopher D. Barr, Mine Cetinkaya-Rundel, Copyright Year: 2015, Last Update: 2019 Publisher: OpenIntro | |||||||||
| 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 using software tools 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 Data Science | Chapter 1 (Main book) | ||||||||
| Week 2 | Data | Chapter 2 (Supplementary book) | ||||||||
| Week 3 | Mathematical Preliminaries | Chapter 2 (Supplementary book) | ||||||||
| Week 4 | Summarizing Data | Chapter 2 (Main book) | ||||||||
| Week 5 | Probability | Chapter 3 (Main book) | ||||||||
| Week 6 | Statistical Distributions | Chapter 4 (Main book) | ||||||||
| Week 7 | Review, Preparation for Midterm Exam | |||||||||
| Week 8 | MIDTERM EXAM | |||||||||
| Week 9 | Foundations for Inference | Chapter 5 (Main book) | ||||||||
| Week 10 | Inference for Categorical Data | Chapter 6 (Main book) | ||||||||
| Week 11 | Inference for Numerical Data | Chapter 7 (Main book) | ||||||||
| Week 12 | Introduction to Linear Regression | Chapter 8 (Main book) | ||||||||
| Week 13 | Multiple and Logistic Regression | Chapter 9 (Main book) | ||||||||
| Week 14 | Data Science Ethics and Social Issues | External Sources | ||||||||
| Week 15 | Review, Preparation for Final Exam | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 30 | 1,2,3,4 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm | 1 | 25 | 1,2,3 | Not Allowed | |
| Quizzes | 2 | 20 | 1,2,3,4 | Not Allowed | |
| Homeworks | 5 | 10 | 1,2,3,4 | Not Allowed | |
| Lab assignments | 5 | 15 | 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 | Home Study | 2 | 15 | 30 | |||
| Active labs | 2 | 10 | 20 | Final Exam Preparation | 15 | 1 | 15 | |||
| Midterm exam Preparation | 11 | 1 | 11 | Quizzes Preparation | 7 | 2 | 14 | |||
| Homeworks | 3 | 5 | 15 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 23/02/2026 | |||||||||