TECH Scholarship for Master's Students
from Europe, South America & Mexico
The Carinthian University of Applied Sciences is offering a new TECH Scholarship for Master Students from Europe, South America & Mexico.
In order to inspire new and talented students from Europe, South America & Mexico to pursue a master's degree in Austria, the Carinthian University of Applied Sciences (CUAS) | FH Kärnten (in collaboration with companies and associations) is offering four scholarships of 8,400 euros each.
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.
Carinthia University of Applied Sciences (CUAS) has been an associate member of the European University ATHENA, an excellence program of the European Commission, since 1 January 2023.
Study & Work
With over 100 Study & Work partner companies and organizations, CUAS offers students the opportunity to combine studying and working!
Study & Work for full-time students
- Extent of employment: marginally up to 8h / week possible
- Timetable: Some degree courses are organized so that Monday is a day off.
Study & Work for part-time students
- Scope of employment: part-time up to max. 20h / week possible
- The timetable is organized in a work-friendly way (lectures at the end of the day, weekend, blocked or online).
Information
Contact
If you have any questions feel free to contact:
Head of Degree Program
Master
120.00
€ 363.36 / semester
- Master of Science in Engineering
4 semester
€ 24.70 / semester
English
- Villach
General Study Information
Curriculum
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.
- Click directly to the Research Groups ENABLE, ADMiRE and ROADMAP-5G
- More information to the Equipment and Measurement Labs
- More information to the Research groups and research projects
Current courses - Applied Data Science
| Lecture | Type | SPPS | ECTS-Credits | Course 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 |
| Lecture | Type | SPPS | ECTS-Credits | Course 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 |
| Lecture | Type | SPPS | ECTS-Credits | Course 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 |
| Lecture | Type | SPPS | ECTS-Credits | Course 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 |
| Lecture | Type | SPPS | ECTS-Credits | Course 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 |
| Lecture | Type | SPPS | ECTS-Credits | Course 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 |
| Lecture | Type | SPPS | ECTS-Credits | Course 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 |
| Lecture | Type | SPPS | ECTS-Credits | Course 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 |
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
FH-Prof. DI (FH) Dr. techn.
Markus Prossegger
DI DI
Aleksandar Karakas, BSc BSc MSc
FH-Prof.in Priv.-Doz. DI Mag.a Dr.in rer. nat.
Anita Kloss-Brandstätter
FH-Prof. DI Dr. techn.
Stefan Schrunner
Dr.
Rami Berry
Marius Birkenbach, BEng MSc
Harald Nezbeda
em.o.Univ.-Prof. Dr.
Jürgen Pilz
DI
Christof Wolf-Brenner, BSc MA
Campus
Campus Villach
Europastraße 4
9524 Villach, Austria
+43 5 90500 7700
villach[at]fh-kaernten[dot]at
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