Universität Passau
5779V Lecture: Data Science - Details
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General information

Subtitle Please register under 5945V Advanced Data Science
Course number 5779V
Semester WiSe 20/21
Current number of participants 111
expected number of participants 90
Home institute Lehrstuhl für Data Science
Courses type Lecture in category Lehre (mit Prüfung)
First date Wed., 04.11.2020 14:00 - 16:00 Uhr
Type/Form Vorveranstaltung
Bachelorstudierende Internet Computing und Informatik und Bachelorstudierende Mathematik im Wahlfach Data Science
Learning organization
Vorlesung und Übungen auf Englisch, Präsentation von Folien, Übungsaufgaben praktische Beispiele,
Performance record
90minütige Klausur oder mündliche Prüfung (20 Minuten); die genaue Prüfungsart wird zu Beginn des Semesters durch Aushang und auf den Internetseiten der Fakultät bekannt gegeben
Die Literatur wird in Abhängigkeit der konkreten Aufgabenstellung ausgewählt und bekanntgegeben.
ECTS points

Course location / Course dates

n.a Wednesday: 14:00 - 16:00, weekly
Thursday: 14:00 - 15:00, weekly
(AM) HS 9 Monday. 01.03.21, Friday. 16.04.21 10:00 - 12:00
(WIWI) HS 5 Monday. 01.03.21 10:00 - 12:00

Fields of study

This information on acceptance for credit of modules for individual degree programmes is not binding; please check the module catalogue at the Faculty of Computer Science and Mathematics to confirm that this module can be counted towards your degree.


Data Science describes a set of methods and processes for extracting knowledge from large data sets. This module introduces the process of data science, gives an overview on the different methods for every stage and their application in different application scenarios. In the exercise, students apply those methods on example data sets.
The course emphasizes practical over theoretical aspects and a more programmatic approach, rather than a mathematical one.

Topics :
• Data science: history and background
• The Knowledge Discovery Process: data gathering, feature engineering, data mining, machine learning and visualizations, discovery, exploration, testing and evaluation
• Descriptive Statistics and Univariate/Bivariate Visualisations
• Feature Engineering: feature selection, feature transformation, dimensionality reduction
• Selected Supervised and Unsupervised Machine Learning Models (e.g. Decistion Trees, Neural Networks, Probabilistic Classifiers, Clustering)
• Selected application domains: Recommendation engine; Fraud detection; Simulators, Forecasting and Classification; Social Network Analysis, Text Mining
• Current trends

Kenntnisse/ Knowledge:

The students gain a very good understanding of a set of methods and processes for extracting knowledge from large data sets.


The students understand the foundations of data science and are able to apply them in big data settings. Students are also able to apply techniques for extracting knowledge from data and to self-learn data science methods not taught in the course.

Kompetenzen/ Competences:

The students became familiar with large-scale data analysis in different applications. They have the ability to select methods best suited for particular application settings.