MATH203 Introduction to Probability and Statistics
MATH203 Introduction to Probability and Statistics
Syllabus | International University of Sarajevo - Last Update on Sep 09, 2025
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Faculty of Engineering and Natural Sciences
Leila Miller
Course Lecturer
Course Objectives
This course is designed to promote understanding and knowledge of statistical methods and concepts used in engineering and natural sciences. Students will be introduced to a wide range of statistical techniques for analyzing data. Students will learn how, when and why statistics are used and why it is necessary to understand them. The topics to be studied are conceptualization, operationalization, and measurement of phenomena from their applied area of studies. Students will learn how to summarize data with graphs and numbers, make generalizations about populations based on samples of the population, and describe the relationships between variables. Students are not expected to become expert statisticians, but they are expected to gain an understanding of how statistics can be used to contribute to their scientific argumentation and for other more general types of questions. Students will become knowledgeable and critical consumers of statistical information that appears in the media, in the workplace, and elsewhere. Students will also gain basic familiarity with the statistical software package R.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Probability, Random Variables and Stochastic Processes, Papoulis, Mc Graw Hill
Additional Literature
“Elementary Statistics A Step By Step Approach”, 9th ed, by Allan G. Bluman (freely available on the web). Gristead and Snell's "Introduction to Probability"(freely available on the web), Jim Pitman's "Probability".Teaching Methods
Class discussions with examples
Active tutorial sessions for engaged learning and continuous feedback on progress
Tutorials that involve problems involving concepts covered in lectures, checks through computer simulations, interpretation of the results
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Course description and presentation (Objectives, requirements, rules, students rights and responsibilities) | |
| 2 | Introduction to Random variables and axioms of probability | |
| 3 | Probability density function; Experimental design; | |
| 4 | Random Processes | |
| 5 | Defining dirac delta function. | |
| 6 | Understanding the importance of measuring variability (range, interquartile range, the variance, and the standard deviation) | |
| 7 | Sample spaces and probability; Addition and Multiplication rules; | |
| 8 | Midterm | |
| 9 | Continuous Probability Distributions | |
| 10 | Probability distributions; Discrete Prob. Distributions (mean, variance, standard deviation and expectation) | |
| 11 | Joint and Conditional Distributions | |
| 12 | Joint and Conditional Distributions | |
| 13 | Project Presentations | |
| 14 | Project Presentations | |
| 15 | Project Presentations |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| MATH203.1 | Course | Friday 09:00 - 10:50 | B F1.23 - Amphitheater I | Tuesday 12:00 - 12:50 | B F1.23 - Amphitheater I |
| MATH203.2 | Course | Thursday 09:00 - 10:50 | B F1.23 - Amphitheater I | Wednesday 12:00 - 12:50 | B F1.23 - Amphitheater I |
| MATH203.1 | Tutorial | Monday 18:00 - 19:50 | B F1.9 | - | - |
| MATH203.2 | Tutorial | Monday 14:00 - 15:50 | B F1.17 | - | - |
| MATH203.3 | Tutorial | Thursday 12:00 - 13:50 | B F2.16 | - | - |
| MATH203.4 | Tutorial | Friday 10:00 - 11:50 | B F2.17 | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Monday | 12:00 - 14:00 | A F1.32 | |
| Tuesday | 11:00 - 12:00 | A F1.32 | |
| Wednesday | 13:00 - 14:00 | A F1.32 | |
| Thursday | 10:00 - 11:00 | A F1.32 |
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 3 5
Midterm Exam
AI: Not AllowedAlignment with Learning Outcomes : 2
Project
AI: Not AllowedAlignment with Learning Outcomes : 1 4
HW
AI: Not AllowedAlignment with Learning Outcomes : 2
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)
Active tutorials
24 hours ⏳ (12 week × 2 h)
Home study
56 hours ⏳ (14 week × 4 h)
In-term exam study
12 hours ⏳ (1 week × 12 h)
Final Exam study
13 hours ⏳ (1 week × 13 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 [MATH203] 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 | |||||||||
| MATH203 | Introduction to Probability and Statistics | 3 | 2 | 6 | ||||||
| Prerequisite | MATH101 | It is a prerequisite to | IE301, IE306, MATH306 | |||||||
| Lecturer | Leila Miller | Office Hours / Room / Phone | Monday: 12:00-14:00 Tuesday: 11:00-12:00 Wednesday: 13:00-14:00 Thursday: 10:00-11:00 |
|||||||
| lmiller@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | This course is designed to promote understanding and knowledge of statistical methods and concepts used in engineering and natural sciences. Students will be introduced to a wide range of statistical techniques for analyzing data. Students will learn how, when and why statistics are used and why it is necessary to understand them. The topics to be studied are conceptualization, operationalization, and measurement of phenomena from their applied area of studies. Students will learn how to summarize data with graphs and numbers, make generalizations about populations based on samples of the population, and describe the relationships between variables. Students are not expected to become expert statisticians, but they are expected to gain an understanding of how statistics can be used to contribute to their scientific argumentation and for other more general types of questions. Students will become knowledgeable and critical consumers of statistical information that appears in the media, in the workplace, and elsewhere. Students will also gain basic familiarity with the statistical software package R. | |||||||||
| Textbook | Probability, Random Variables and Stochastic Processes, Papoulis, Mc Graw Hill | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | Class discussions with examples. Active tutorial sessions for engaged learning and continuous feedback on progress. Tutorials that involve problems involving concepts covered in lectures, checks through computer simulations, interpretation of the results. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Course description and presentation (Objectives, requirements, rules, students rights and responsibilities) | |||||||||
| Week 2 | Introduction to Random variables and axioms of probability | |||||||||
| Week 3 | Probability density function; Experimental design; | |||||||||
| Week 4 | Random Processes | |||||||||
| Week 5 | Defining dirac delta function. | |||||||||
| Week 6 | Understanding the importance of measuring variability (range, interquartile range, the variance, and the standard deviation) | |||||||||
| Week 7 | Sample spaces and probability; Addition and Multiplication rules; | |||||||||
| Week 8 | Midterm | |||||||||
| Week 9 | Continuous Probability Distributions | |||||||||
| Week 10 | Probability distributions; Discrete Prob. Distributions (mean, variance, standard deviation and expectation) | |||||||||
| Week 11 | Joint and Conditional Distributions | |||||||||
| Week 12 | Joint and Conditional Distributions | |||||||||
| Week 13 | Project Presentations | |||||||||
| Week 14 | Project Presentations | |||||||||
| Week 15 | Project Presentations | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 30 | 3,5 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm Exam | 1 | 30 | 2 | Not Allowed | |
| Project | 1 | 25 | 1,4 | Not Allowed | |
| HW | 1 | 15 | 2 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Active tutorials | 2 | 12 | 24 | |||
| Home study | 4 | 14 | 56 | In-term exam study | 12 | 1 | 12 | |||
| Final Exam study | 13 | 1 | 13 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 19/09/2025 | |||||||||