Master's degree in Data Science & Engineering

Studienplan

Datenanalyse

• Studienprojekt Praktische Datenanalyse
• Textmining
• Studienprojekt Angewandte Datenanalyse
• Multivariate Statistik
• Numerische Methoden
• Computational Intelligence I
• Computational Intelligence II
• Modellbildung und Simulation
• Datenschutz und Privatsphäre
• Computer Vision

Informatik

• Visualisierung
• Process Mining
• Scripting Fortgeschritten
• Datenakquisition und -qualität
• Big Data
• Cloud Computing

Domänen Expertise

• Anwendungsdomänen-Modul I
• Anwendungsdomänen-Modul II

Sozialkompetenz

• Leadership Praxis

Wissenschaftliche Kompetenzen

• Masterarbeit Seminar
• Wissenschaftliches Arbeiten
• Masterarbeit

Schwerpunkte

• Datenverständnis
• Datenspeicherung und -management
• Datenanalyse
• Computer Vision
• Praxisbezogene Projekte

Themen

• Analyse großer, semi-strukturierter Datenmengen (Big Data)
• Mathematische und statistische Methoden zur Datenauswertung
• Künstliche Intelligenz, Data Mining und Mustererkennung
• Visualisierung von Daten und Zusammenhängen (Prozessen)
• Wahlfächer: Datenanalyse in Technik, Biologie, Wirtschaft, Medizin

Praxis und Forschung im Studium

From the second semester onwards, students apply their knowledge in practical research projects. The range of topics is very broad, with a focus on data analysis for biomedicine or Marketing and production, for example. Clients are well-known partners from business and research.

Auch Forschungsgruppen der FH OÖ (insbesondere am Campus Hagenberg) ermöglichen Studierenden eine Forschungstätigkeit, z. B. in den Bereichen Opinion Mining, Datenanalyse in der Produktion, molekularbiologische Datenauswertung und personalisierte Medizin.

The professional opportunities for graduates of this course are diverse. Data scientists are sought wherever large amounts of data are generated and/or need to be evaluated. Their interdisciplinary training makes them sought-after specialists in industries, commerce, production, finance, medicine and pharmaceutical research, among others.

They are experts in the areas of data analysis and data mining, in dealing with cloud and cluster systems, and in the mathematical evaluation of data, including using artificial intelligence methods. In addition, they are able to visualize and process the results and the connections they have discovered and often hold managerial positions in companies and research institutions.

After graduation

• Analysis of data or data models, IT landscapes and business processes with regard to the need and introduction of new approaches to knowledge extraction
• Design of processes for data extraction, cleansing and transformation
• Modeling data schemas for data integration and analysis
• Use of data mining and statistical methods as well as development of predictive models
• Designing solutions for processing and analyzing data in real time using the latest analytical tools and big data technologies
• Visualization of data and preparation of analytical insights
• Communication, development and presentation of solutions to decision makers (specialist departments and management)

Good to know

The Harvard Business Review and the New York Times speak of the “sexiest job in the 21st century” in connection with data science.