AID304 Big Data Analytics
Syllabus | International University of Sarajevo - Last Update on Apr 04, 2026
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Artificial Intelligence and Data Engineering
Babatunde Kazeem Oladejo
Course Lecturer
Course Objectives
• Provide an overview of key platforms like Hadoop, Spark, and other relevant tools. • Discuss various methods of storing data and explain the processes of uploading, distributing, and processing data. • Explore diverse approaches for implementing analytics algorithms on different platforms. • Delve into the challenges related to visualization and mobile integration in the context of Big Data Analytics.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale, 1st edition, Ofer Mendelevitch Casey Stella Douglas Eadline, 2019, ISBN 9780134024141
Additional Literature
Raj Kamal and Preeti Saxena, “Big Data Analytics Introduction to Hadoop, Spark, and Machine Learning”, McGraw Hill Education, 2018 ISBN: 9789353164966, 9353164966Teaching Methods
The course will commence with a one-hour session dedicated to theoretical concepts and providing a comprehensive understanding of the topic's background
Subsequently, we will transition to hands-on programming and practical exercises
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction of Big Data Analytics | Chapter 1 |
| 2 | Big Data Project Lifecycle and Use Cases | Chapters 1 and 2 |
| 3 | Hadoop Architecture | Chapter 3 |
| 4 | Spark Architecture and pySpark Implementation, No Lab | |
| 5 | Data Preparation | |
| 6 | Data Preparation cont.; In-Term Quiz | |
| 7 | Feature Engineering; Graded Lab | |
| 8 | MIDTERM Exam | |
| 9 | Predictive Machine Learning Models | |
| 10 | Clustering-based Analysis, No Lab (BiH holiday) | |
| 11 | Big Data Visualization and Graph Databases | |
| 12 | Stream Data Analysis | |
| 13 | Anomaly Detection and Handling | |
| 14 | No Lecture, No Lab (Holidays) | |
| 15 | FINAL Exam Preparation |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| AID304.1 | Course | Thursday 15:00 - 17:50 | A F1.10 | - | - |
| AID304.1 | Tutorial | Friday 16:00 - 17:50 | B F1.25 Computer Lab | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Wednesday | 14:00 - 17:00 | A F1.16 | |
| Thursday | 11:00 - 13:00 | A F1.16 |
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3
Midterm
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3
In-Term Quiz
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3
Term project and presentation
AI: Consult InstructorAlignment with Learning Outcomes : 1 2 3
Lab assignments
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3
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
42 hours ⏳ (14 week × 3 h)
Assignments
21 hours ⏳ (7 week × 3 h)
Active labs
28 hours ⏳ (14 week × 2 h)
Home study
14 hours ⏳ (14 week × 1 h)
In-term exam study
10 hours ⏳ (1 week × 10 h)
Final exam study
11 hours ⏳ (1 week × 11 h)
Term project/presentation
24 hours ⏳ (12 week × 2 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 [AID304] 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 Apr 04, 2026 | 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 | |||||||||
| AID304 | Big Data Analytics | 3 | 2 | 6 | ||||||
| Prerequisite | None | It is a prerequisite to | - | |||||||
| Lecturer | Babatunde Kazeem Oladejo | Office Hours / Room / Phone | Wednesday: 14:00-17:00 Thursday: 11:00-13:00 |
|||||||
| koladejo@ius.edu.ba | ||||||||||
| Assistant | Ismar Aganovic | Assistant E-mail | iaganovic@ius.edu.ba | |||||||
| Course Objectives | • Provide an overview of key platforms like Hadoop, Spark, and other relevant tools. • Discuss various methods of storing data and explain the processes of uploading, distributing, and processing data. • Explore diverse approaches for implementing analytics algorithms on different platforms. • Delve into the challenges related to visualization and mobile integration in the context of Big Data Analytics. |
|||||||||
| Textbook | Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale, 1st edition, Ofer Mendelevitch Casey Stella Douglas Eadline, 2019, ISBN 9780134024141 | |||||||||
| Additional Literature |
|
|||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | The course will commence with a one-hour session dedicated to theoretical concepts and providing a comprehensive understanding of the topic's background. Subsequently, we will transition to hands-on programming and practical exercises. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction of Big Data Analytics | Chapter 1 | ||||||||
| Week 2 | Big Data Project Lifecycle and Use Cases | Chapters 1 and 2 | ||||||||
| Week 3 | Hadoop Architecture | Chapter 3 | ||||||||
| Week 4 | Spark Architecture and pySpark Implementation, No Lab | |||||||||
| Week 5 | Data Preparation | |||||||||
| Week 6 | Data Preparation cont.; In-Term Quiz | |||||||||
| Week 7 | Feature Engineering; Graded Lab | |||||||||
| Week 8 | MIDTERM Exam | |||||||||
| Week 9 | Predictive Machine Learning Models | |||||||||
| Week 10 | Clustering-based Analysis, No Lab (BiH holiday) | |||||||||
| Week 11 | Big Data Visualization and Graph Databases | |||||||||
| Week 12 | Stream Data Analysis | |||||||||
| Week 13 | Anomaly Detection and Handling | |||||||||
| Week 14 | No Lecture, No Lab (Holidays) | |||||||||
| Week 15 | FINAL Exam Preparation | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 35 | 1,2,3 | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm | 1 | 25 | 1,2,3 | Not Allowed | |
| In-Term Quiz | 1 | 10 | 1,2,3 | Not Allowed | |
| Term project and presentation | 1 | 10 | 1,2,3 | Consult Instructor | |
| Lab assignments | 2 | 20 | 1,2,3 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture hours | 3 | 14 | 42 | Assignments | 3 | 7 | 21 | |||
| Active labs | 2 | 14 | 28 | Home study | 1 | 14 | 14 | |||
| In-term exam study | 10 | 1 | 10 | Final exam study | 11 | 1 | 11 | |||
| Term project/presentation | 2 | 12 | 24 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 22/04/2026 | |||||||||