Advanced topics in Data Engineering & AI (5 cr)

Evaluation scale

H-5

Content scheduling

The course will be provided in two parts covering the following concepts:
Part I:
-- data privacy and security (encryption, certificates)
-- object storages, data warehouses and data lakes
-- legislation on data protection (GDPR, data act)
Part II:
-- Data Regulations and Ethics in AI
-- Synthetic data generation
-- Differential privacy techniques
-- Decentralized machine learning and federated learning

Objective

After completing the course, the student can:
- work with advanced topics in data engineering and AI

Content

Advanced topics in Data Engineering, AI and data analytics such as
- application security
- data privacy
- legislation on data protection
- ethics of AI

Materials

Course materials are prepared by the lecturer from various sources including books, online material, etc.

Recommended books to study in this course are:
-- Practical Data Privacy: Enhancing Privacy and Security in Data 1st Edition by Katharine Jarmul
-- Fundamentals of Data Engineering: Plan and Build Robust Data Systems 1st Edition
by Joe Reis and Matt Housley

Teaching methods

Weekly contact sessions with total of 3 hours of theory and practical exercises. Participation on the contact teaching is obligatory in order to be able to solve the practical exercises. Help will be provided during the contact teaching where needed.

Exam schedules

Exams including retake will be in December 2025 and January 2026.

Pedagogic approaches and sustainable development

The course includes theory sessions and personal practice tasks. The student is expected to have participated on the theory sessions in order to be able to solve the practice tasks.

This learning method combines theoretical knowledge with practical applications and real-world examples. It emphasizes understanding data engineering fundamental and privacy AI concepts, studying relevant technologies and techniques, and exploring practical implementations and use cases. Hands-on exercises, case studies, and projects will be incorporated to reinforce the learning experience.

Completion alternatives

n/a

Student workload

11 sessions (8.9. - 5.12.2025 ) each 3 hours (2h lecture, 1h practice)+ Exam

Contact hours:
- Weeks 36 - 47: Theory & practice (3h/week): 11 x 3h = 33h
- Week 48: Exam: 2h
- In addition, about 5 support and inquiry hours (biweekly): 5x 1h = 5h

Total contact hours: 40 hours
Independent study and homework: about 95 h

Evaluation methods and criteria

Assessment is done as follows:
-- 22% of the grade for class exercises
-- 44% of the grade for homework exercises
-- 34% of the grade for the exam

-Class and homework exercises (50% of total to pass): Students must achieve at least 50% of the points to pass the course. Class exercises are complete during the exercise classes. If returned late, the maximum points achievable will be reduced by 50%. Participation on the contact classes is mandatory.

- Exam (50% of total points to pass): Students must achieve at least 50% of the points in order to pass the course.

The course is graded on a scale of 0-5.

Grading will be according to the total points collected by the student during the course as well as the exam.
1: 50% (minimum to pass the course)
2: 60-69%
3: 70-79%
4: 80-89%
5: 90-100%

Failed (0)

Either
<50% of the total points available
OR
<50% of the total exam points
OR
<50% of the total exercise points

Assessment criteria, satisfactory (1-2)

50-69% of total points.
AND
>= 50% of the exam points
AND
>= 50% of the exercise points

Assessment criteria, good (3-4)

70-89% of total points.
AND
>= 50% of the exam points
AND
>= 50% of the exercise points

Assessment criteria, excellent (5)

>=90% of total points.
AND
>= 50% of the exam points
AND
>= 50% of the exercise points

Further information

Itslearning, contact classes