Study Guide

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.

Exam schedules

Exams including retake will be in Week 48 or 49 (at the same day as we have the regular lectures).

International connections

The course includes about 11 theory sessions and personal 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

The exercises are mainly performed using Jupyter Notebook or other types of code scripts. Students will use TensorFlow and/or PyTorch. Strong python programming skills are needed to complete the exercises in part II.

Student workload

11 sessions (2.9-29.11.24 ) 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 90 h

Content scheduling

The course will be provided in two parts covering the following concepts:
Part I:
-- data security (encryption)
-- data privacy
-- 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

Evaluation scale

H-5

Assessment methods and criteria

This course comprises 100 points including:
-- 22 points (1+1p each contact class: Lecture and Practical Session)
-- 44points for exercises
-- 34points for the exam

-Participation and exercises (50% of total to pass): Students must achieve at least 50% of the points to pass the course. Participation points can only be gained by being present in class during the Lecture and Practical sessions.

- 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%

Assessment criteria, fail (0)

<50% of total points or failed exam, exercise or participation points total.

Assessment criteria, satisfactory (1-2)

50-69% of the total points with passed exam, exercise and participation.

Assessment criteria, good (3-4)

70-89% of the total points with passed exam, exercise and participation.

Assessment criteria, excellent (5)

90-100% of the total points with passed exam, exercise and participation.