This Master's program is organized in a work-friendly way and is therefore compatible with a professional career.

Applied Data Science - Master's program

Exploiting data collections

The continuing trend towards the digitalization of work processes and the immense amount of data to be processed are subjects of current business practice. Companies have collected large amounts of data in recent years and are now faced with the challenge of exploiting these data collections and generating added value for their business areas. They are looking for qualified data scientists who can generate relevant information from large amounts of data and derive recommendations from the processed data.

Becoming a data scientist

A data scientist is expected to be familiar with the entire data value chain. Therefore, graduates of the Master of Science degree program have a practical as well as theoretical understanding in all of the following areas:

  • Data acquisition
  • Data transmission
  • Data storage
  • Data evaluation
  • Data visualization
  • Legal and ethical frameworks

Moreover, graduates can use their acquired professional and methodological competence to pursue a further scientific specialization in the form of a PhD at a technical university.

Application deadline

Winterm 2023

Period I:   01.11.-15.03.2023
Period II:  16.03.-15.05.2023
Period III: 16.05.-15.07.2023
Period IV: 16.07.-30.09.2023*

For applicants from outside Europe applications are only accepted within Period I & II (due to Visa Processing)

*We reserve the right not to open the period or to close it early.

Study start

October 2022

Teaching time

Wednesday & Thursday from 16:50 h
Friday from 13:30 h
max. 2 Saturdays per month & 5 attendance days on weekdays in the semester

Events

Study Guidance
Book your personal appointment.

Master Session Online
21.11.2022
16-18 h
More information

Campus Day
03.03.2023
15-18 h

You can find out more about our advisory services, events and fairs on our website.

Further information

Bachelor Degree with a Minimum of:

- Computes Science Basics and Programming (10 ECTS)

- Mathematics / Statistics / Algorithmics (10 ECTS)

- Data Management / Databases / Data Structures (5 ECTS)

Language of Study: English

Study & Work

Study & Work - is that possible?

With over 80 Study & Work partner companies and organizations, Carinthia University of Applied Sciences offers first-year students of a bachelor's or master's degree program the opportunity to combine their studies with a career.

This attractive offer gives students the chance to immediately put theoretical knowledge into practice and gain professional experience in Carinthia's leading companies.

Information

Contact

If you have any questions feel free to contact:

 

 

Head of Degree Program

 

 

Level of qualification
Master
ECTS credits
120
Tuition fees
€ 363.36 / semester
Qualification awarded
  • Master of Science in Engineering
Duration of study
4 semester
ÖH (Austrian Student Union) fee
€ 21.20 / semester
Language of instruction
Englisch
FH campus
  • Villach

General Study Information

 

 

 

 

 

 

 

 

Profile of the Study Program

The newly developed master’s degree program trains future data scientist for a variety of promising career paths in different business areas. There is a great need for experts that are familiar with the entire data value chain, from the acquisition of data, to the extraction of information, and finally the generation and representation of knowledge.

The technical-methodological skills acquired during the program allow the students to identify problems in the acquisition of (sensor) data as well as the transmission of these data and enable them to make a fundamental assessment with regard to the quality of the acquired data. A main focus is on the techniques of storing large, heterogeneous data in order to enable a subsequent valid and timely evaluation using models of machine learning and artificial intelligence. Modern methods of visualization are part of the curriculum to communicate the results and derive recommendations for action.
Graduates of the master’s degree program "Applied Data Science" can use their acquired professional and methodological competence to pursue a further scientific specialization in the form of a PhD at a technical university.

Data science is an incredibly fascinating field of applied sciences. The prerequisite for attending the master's degree program is a completed technical bachelor's or diploma degree from the FH Kärnten or an equivalent program at a recognized domestic or foreign post-secondary educational institution.
What students should bring to their studies:

  • 10 ECTS in Computer Science Basics and Programming
  • 10 ECTS in Mathematics, Statistics, and Algorithmics
  • 5 ECTS in Data Management, Databases, and Data Structures (FH Kärnten offers a bridge course during summer)A fundamental interest in topics of artificial intelligence and machine learning.
  • A personality who enjoys discovering innovative and creative solutions.
  • Solid mathematical knowledge and basic programming skills.
  • Basic knowledge of databases and data management.
  • Good language and communication skills.

Graduates of the master’s degree program “Applied Data Science” are able to:

  • work on concrete tasks in a problem-solving manner,
  • recognize the interrelationships of data analysis on large amounts of data on the technology, system, and application level,
  • •apply mathematical, statistical and computer science knowledge,
  • estimate the limits of the state-of-the art procedures, methods and models,
  • design, develop and implement systems for the analysis of large amounts of data
  • professionally carrying out projects with respect to development, implementation and application,
  • reflect, analyze and further develop the basic knowledge acquired during the program,
  • apply the basics and methods of scientific work.

Research in the study program

Research and development make an important contribution to transforming broad university knowledge into practical application solutions and to promoting cooperation between companies and universities.
 

 

Current courses - Applied Data Science

LectureTypeSPPSECTS-CreditsCourse number
Artificial Neural Networks and Deep Learning (I) ILV 3,5 5,0 M2.08760.11.131
Data Architecture and Database Technologies (I) ILV 3,5 5,0 M2.08760.11.111
Data Engineering ILV 3,5 5,0 M2.08760.11.041
Data Visualization (I) ILV 3,5 5,0 M2.08760.11.171
Project (II) Frameworks and Concept Study ILV 3,5 5,0 M2.08760.11.091
Supervised Learning ILV 3,5 5,0 M2.08760.11.071
LectureTypeSPPSECTS-CreditsCourse number
Data Source & Data Quality ILV 3,5 5,0 M2.08760.11.031
Information& Probability Theory ILV 3,5 5,0 M2.08760.11.011
Introduction to Machine Learning ILV 3,5 5,0 M2.08760.11.051
Project (I) Prerequisites and Project Domains ILV 3,5 5,0 M2.08760.11.081
Statistics ILV 3,5 5,0 M2.08760.11.021
Unsupervised Learning ILV 3,5 5,0 M2.08760.11.061
LectureTypeSPPSECTS-CreditsCourse number
Academic Skills SE 3,0 5,0 M2.08860.10.061
Advanced Topics ILV 3,5 5,0 M2.08760.11.151
Artificial Neural Networks and Deep Learning (II) ILV 3,5 5,0 M2.08760.11.141
Data Architecture and Database Technologies (II) ILV 3,5 5,0 M2.08760.11.121
Data Visualization (II) ILV 3,5 5,0 M2.08760.11.181
Project (III) Practical Implementation PA 3,5 5,0 M2.08760.11.101
LectureTypeSPPSECTS-CreditsCourse number
Artificial Neural Networks and Deep Learning (I) ILV 3,5 5,0 M2.08760.11.131
Data Architecture and Database Technologies (I) ILV 3,5 5,0 M2.08760.11.111
Data Engineering ILV 3,5 5,0 M2.08760.11.041
Data Visualization (I) ILV 3,5 5,0 M2.08760.11.171
Project (II) Frameworks and Concept Study ILV 3,5 5,0 M2.08760.11.091
Supervised Learning ILV 3,5 5,0 M2.08760.11.071
LectureTypeSPPSECTS-CreditsCourse number
Data Privacy and Ethics SE 3,0 5,0 M2.08760.11.161
Master Exam ME 0,0 3,0 M2.08760.11.221
Master Thesis MT 0,5 20,0 M2.08760.11.211
Master Thesis Seminar SE 2,0 2,0 M2.08760.11.201
LectureTypeSPPSECTS-CreditsCourse number
Data Source & Data Quality ILV 3,5 5,0 M2.08760.11.031
Information& Probability Theory ILV 3,5 5,0 M2.08760.11.011
Introduction to Machine Learning ILV 3,5 5,0 M2.08760.11.051
Project (I) Prerequisites and Project Domains ILV 3,5 5,0 M2.08760.11.081
Statistics ILV 3,5 5,0 M2.08760.11.021
Unsupervised Learning ILV 3,5 5,0 M2.08760.11.061
LectureTypeSPPSECTS-CreditsCourse number
Academic Skills SE 3,0 5,0 M2.08860.10.061
Advanced Topics ILV 3,5 5,0 M2.08760.11.151
Artificial Neural Networks and Deep Learning (II) ILV 3,5 5,0 M2.08760.11.141
Data Architecture and Database Technologies (II) ILV 3,5 5,0 M2.08760.11.121
Data Visualization (II) ILV 3,5 5,0 M2.08760.11.181
Project (III) Practical Implementation PA 3,5 5,0 M2.08760.11.101

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Job & Career

Graduates of the Master of Science degree program “Applied Data Science” are highly educated specialists with exciting career opportunities in different fields of work. The leading employers for data scientists are typically from the following areas:

  • Public and private research institutions
  • Banks
  • Manufacturers
  • Large retailers
  • E-commerce companies
  • Internet service providers
  • Public and private transport companies
  • Marketing departments/agencies

The successful completion of the master program Applied Data Science in turn qualifies the graduate to undertake a doctorate.

Faculty and Staff - Applied Data Science

Professor of Data Science

FH-Prof. DI (FH) Dr. techn.

 Markus Prossegger
+43 (0)5 90500-3148
m.prossegger[at]fh-kaernten[dot]at

Primoschgasse 8
9020 Klagenfurt am Wörthersee
Part-time Lecturer

Dipl.-Ing. (FH)

 Christof Lettner

FH-Prof. Dipl.-Math. Dr. phil. habil.

 Andreas Pester

DI Dr.in techn.

 Olivia Pfeiler

em.o.Univ.-Prof. Dr.

 Jürgen Pilz

Mag. DI Dr. techn.

 Kathrin Plankensteiner

DI Dr. techn.

 Konstantin Posch

Campus

Campus Villach

Europastraße 4
9524 Villach, Austria

+43 5 90500 7700 
villach[at]fh-kaernten[dot]at

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