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247-0111-01L 3 Credits NDS , WBZ D-ITET , D-INFK
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Data Science: From Analytics to Learning

Lecturers & Examiners: Prof. Dr. Ender Konukoglu, Dr. Oylum Akkus Ispir
VVZ CR n/a

Last Updated: 2026-06-01 11:30:45

Abstract

In this module, basic paradigms and techniques in working with data will be discussed, especially towards data security, managing data decentrally, and learning from data.

Objective

Participants will understand some of the concepts in detail and see the mathematics behind them.

Content

This module covers the essential concepts and tools of data science. The main purpose is to provide you the basic knowledge and intuition to use data and understand how it is used. You'll explore the data landscape, understand key data science techniques, and learn how to apply them. The key topics of this module are the types of data, sources, and collection methods, data lifecycle, data-driven decision making, exploratory data analysis, experimental testing, regression models, and machine learning. Each topic will be enriched with collaborative discussions and hands-on exercise, enabling you to develop a practical understanding of how data science is leveraged across various industries.

General Information

Language
English
Levels
NDS , WBZ
Frequency
Yearly recurring

Examination

Type
graded semester performance
Two homeworks and a written exam (90 minutes). Each homework as well as the exam are graded. If H1, H2 and E are the homework and exam performances (measured in the percentage of points achieved), the module performance is 0.1*H1 + 0.1*H2 + 0.8*E. This means, each homework contributes 10%, and the exam contributes 80%.A module performance of 50% or higher is guaranteed to be a passing performance, but depending on the cohort, we may also require less than 50% to pass the module.Repetition of a failed exam is possible immediately after the CAS (usually before the end of February). Dates are to be negotiated directly with the lecturer(s) in question. If you do not take the repetition exam or fail again, the module and thus the whole CAS are considered as failed.

Registration & Places

Priority: Registration for the course unit is only possible for the primary target group

Course Components

Type Title Time & Place Hours
lecture Data Science: From Analytics to Learning
Permission from lecturers required for all students. Block course 24.10.2025 / room HG E 33.3 (Exam)
  • 26.09 Date 08:15-17:00 (HG E 33.3)
  • 27.09 Date 08:15-13:00 (HG E 33.3)
  • 10.10 Date 08:15-17:00 (HG E 33.3)
  • 11.10 Date 08:15-13:00 (HG E 33.3)
  • 24.10 Date 08:15-09:00 (HG E 33.5)
39 h semesterly

Offered In