CS540 Data Analytical Problem Solving
CS540 Data Analytical Problem Solving
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
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Computer Sciences and Engineering
Course Objectives
The aim of this course is to train students in data-oriented approach to problem solving from a business, health care, or some other domain where optimizing according to constraints is a way to solution. Data will serve as an insight into the context and the system within which problem lies. For example, dataset might come from insurance company and the question might be to identify clients for a new marketing strategy that the company wants to try out. The emphasis will be upon the understanding and use of statistical methodology, and the written communication of the results of such a data analysis. Students should gain practical experience in elementary data management and analysis techniques. Upon completion of the course students should be able to use data to make recommendations and make informed decisions regarding any process or phenomenon for which it is possible to collect data. In the first part of the course students will get to know the statistical software package R since that will be the tool with which data will be manipulated and analyzed.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
It is envisioned for the course to be very practical, hands-on, code-producing course. For the part of the course where students need to familiarize themselves with programming in R a good free reference is "An Introduction to R for Beginners" - S.D. Hafner readily available at https://www.researchgate.net/publication/325170649_An_Introduction_to_R_for_Beginners. We will make use of few chapters from “Introduction To Data Technologies" - Paul Murrel, again, available online for free.
Additional Literature
Teaching Methods
Lecture slides will be available throughout the introductory part where R programming will be the focus
We will right away start using a single real data set
Each student will get the same data set at the beginning of the class
At midterm mark this data set students practiced all the concepts on so far will be the object of the midterm exam
In the midterm students will have to put the taught concepts to use with familiar data
Lectures will therefore involve real data, computer analysis, summary, interpretation and reporting
After the midterm, all students will receive, each their own data set which will serve from that point on as the context for the final exam (report)
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to data concepts; Population and Sample; Random Sampling; Some important statistics; Data description and visualization techniques. | Slides |
| 2 | R essentials (import, export, manipulate, data); R data constructs (data.frames, variables, types, missing values ); | Omnipresent web-resources on R |
| 3 | Binding, merging, subsetting, saving data.frames permanently | Slides and online resources |
| 4 | Vizualization techniques revisited (scales, missing data, graph customization) | Slides and online resources |
| 5 | Higher level R functions from the "apply" - family. | Slides and online resources |
| 6 | Statistical methodology in R (correlation, reression, cluster-analysis, PCA) | Slides and online resources |
| 7 | Reporting on data and its context | Slides and online resources |
| 8 | Midterm | Slides and online resources |
| 9 | Data cleaning, identifying outliers and casting of variables in a ready-to-analyze format. | Slides and online resources |
| 10 | Creating an R script that imports, cleans and saves a data set. | Slides and online resources |
| 11 | Techniques for relating components in the data that might help answer the question or solve a particular problem that students will be given in week 8. (UML diagrams, RapidMiner tool, | Slides and online resources |
| 12 | Data analysis (supervised self study) | Slides and online resources |
| 13 | Data analysis (supervised self study) | Slides and online resources |
| 14 | Data analysis (supervised self study) | Slides and online resources |
| 15 | Final presentation of ones findings and recommendations. | Slides and online resources |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| CS540.1 | Course | - | - | - | - |
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes :
Final Presentation
AI: Not AllowedAlignment with Learning Outcomes : Demonstrate ability to decide on appropriateness and the type of descriptive statistical techniques tools and statistical visualization software; Apply regression and correlation analysis techniques correctly using R statistical software;
Midterm
AI: Not AllowedAlignment with Learning Outcomes : Demonstrate ability to decide on appropriateness and the type of descriptive statistical techniques tools and visualization methods for decision making purposes. Apply statistical methods like regression cluster analysis or PCA using R statistical softw
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:
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 [CS540] 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 Mar 03, 2026 | International University of Sarajevo
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Referencing Curricula Print this page
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | Identify the relationship between data at hand and an issue or a problem that needs a solution. | Not Allowed | |
| Semester Evaluation Components | |||||
| Final Presentation | 12 | 30 | Demonstrate ability to decide on appropriateness and the type of descriptive statistical techniques, tools and statistical visualization software; Apply regression and correlation analysis techniques correctly using R statistical software; | Not Allowed | |
| Midterm | 2 | 30 | Demonstrate ability to decide on appropriateness and the type of descriptive statistical techniques, tools and visualization methods for decision making purposes. Apply statistical methods like regression, cluster analysis or PCA using R statistical softw | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Total Workload Hours = | 0 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 27/03/2026 | |||||||||