AID402 Applied Data Engineering

Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026

Referencing Curricula

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Artificial Intelligence and Data Engineering

Academic Year

-

Semester

-

Course Code

AID402

Weekly Hours

3 Teaching + 2 Practice

ECTS

6

Prerequisites

None

Teaching Mode Delivery

Face-to-face

Prerequisite For

-

Teaching Mode Delivery Notes

-

Cycle

I Cycle

Prof. Jane Doe

TBA

Course Lecturer

Position

-

Email

Phone

033 957

Assistant(s)

-

Assistant E-mail

Course Objectives

• By the end of this course, attendees will gain comprehensive knowledge of the essential components of Big Data Analytics and its associated ecosystems. • Participants will possess the skills to design, build, test, and sustain architectures such as databases and high-capacity processing systems. They will be capable of conducting batch and real-time streaming analytics on both structured and unstructured data, and demonstrate proficiency in professional data management. Additionally, they will be equipped to create visually appealing visualizations and dashboards. • This course offers a thorough, progressive, and practical hands-on learning experience.

Learning Outcomes

After successful completion of the course, the student will be able to:

1

Analyze the characteristics of data-intensive applications that rely on streaming data and propose appropriate solution architectures. This includes considering a combination of batch and streaming data processing techniques.

2

Develop software applications that incorporate the analysis techniques and technologies covered in the course.

3

Utilize frameworks for large-scale distributed machine learning in their data analysis and processing tasks.

Course Materials

Required Textbook

There is no required textbook.

Additional Literature

Teaching Methods

The course will begin with a one-hour theoretical overview, providing a thorough explanation of the topic's background

Following that, we will proceed to practical programming exercises and hands-on practice

Weekly Topics

This weekly planning is subject to change with advance notice.

Week	Topic	Readings / References
1	Features of Data Engineering
2	Metadata Management
3	Consolidating Multiple Data Sources
4	Data Ingestion, Cleansing and Transformation
5	Hadoop Architecture and Ecosystem
6	Flat Files Ingestion into Hadoop
7	RDBMS and Hadoop Integration
8	Hive Data Processing
9	Interactive Query using Impala
10	Log Files Handling and Processing
11	Introduction to Spark
12	Processing Data using PySpark
13	Spark Data Query
14	Real-time Data Analytics in Spark Streaming
15

Course Schedule (All Sections)

Course Schedules with all sections will be available here soon.

Office Hours & Room

Course Office hours will be available here soon.

Assessment Methods and Criteria

Assessment Components

30%x1

Final Exam

AI: Not Allowed

Alignment with Learning Outcomes :

25%x1

Midterm

AI: Not Allowed

Alignment with Learning Outcomes :

15%x3

Quizzes

AI: Not Allowed

Alignment with Learning Outcomes :

15%x1

Term project and presentation

AI: Not Allowed

Alignment with Learning Outcomes :

15%x7

Lab assignments

AI: Not Allowed

Alignment with Learning Outcomes :

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	F	5
55 - 64	C	2	E	6
65 - 69	C+	2.3	D	7
70 -74	B-	2.7	D	7
75 - 79	B	3	C	8
80 - 84	B+	3.3	C	8
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 [AID402] 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

Engage Actively

Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.

Read and Review Purposefully

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.

Think Critically in Assignments

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.

Ask Questions Early

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

Referencing Curricula Print this page

Course Code	Course Title					Weekly Hours*		ECTS		Weekly Class Schedule
						T	P
AID402	Applied Data Engineering					3	2	6
Prerequisite	None				It is a prerequisite to	-
Lecturer					Office Hours / Room / Phone	Currently not available
E-mail
Assistant					Assistant E-mail
Course Objectives	• By the end of this course, attendees will gain comprehensive knowledge of the essential components of Big Data Analytics and its associated ecosystems. • Participants will possess the skills to design, build, test, and sustain architectures such as databases and high-capacity processing systems. They will be capable of conducting batch and real-time streaming analytics on both structured and unstructured data, and demonstrate proficiency in professional data management. Additionally, they will be equipped to create visually appealing visualizations and dashboards. • This course offers a thorough, progressive, and practical hands-on learning experience.
Textbook	There is no required textbook.
Additional Literature
Learning Outcomes	After successful completion of the course, the student will be able to:
	Analyze the characteristics of data-intensive applications that rely on streaming data and propose appropriate solution architectures. This includes considering a combination of batch and streaming data processing techniques. Develop software applications that incorporate the analysis techniques and technologies covered in the course. Utilize frameworks for large-scale distributed machine learning in their data analysis and processing tasks.
Teaching Methods	The course will begin with a one-hour theoretical overview, providing a thorough explanation of the topic's background. Following that, we will proceed to practical programming exercises and hands-on practice.
Teaching Method Delivery	Face-to-face			Teaching Method Delivery Notes
WEEK	TOPIC								REFERENCE
Week 1	Features of Data Engineering
Week 2	Metadata Management
Week 3	Consolidating Multiple Data Sources
Week 4	Data Ingestion, Cleansing and Transformation
Week 5	Hadoop Architecture and Ecosystem
Week 6	Flat Files Ingestion into Hadoop
Week 7	RDBMS and Hadoop Integration
Week 8	Hive Data Processing
Week 9	Interactive Query using Impala
Week 10	Log Files Handling and Processing
Week 11	Introduction to Spark
Week 12	Processing Data using PySpark
Week 13	Spark Data Query
Week 14	Real-time Data Analytics in Spark Streaming
Week 15

Assessment Methods and Criteria	Evaluation Tool	Quantity	Weight	Alignment with LOs	AI Usage
	Final Exam	1	30		Not Allowed
	Semester Evaluation Components
	Midterm	1	25		Not Allowed
	Quizzes	3	15		Not Allowed
	Term project and presentation	1	15		Not Allowed
	Lab assignments	7	15		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: 27/03/2026

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