MScData Science and Engineering

Study location	Austria, Hagenberg
Type	Master courses, full-time
Nominal duration	2 years
Study language	German
Awards	MSc

Entry qualification

Undergraduate diploma (or higher) preferably with focus on computer science Relevant bachelor’s courses or university of applied sciences bachelor’s courses are characterized by a minimum scope of a total of 70 ECTS in: IT-Inhalte und Mathematik 70 ECTS The entry qualification documents are accepted in the following languages: English / German. Often you can get a suitable transcript from your school. If this is not the case, you will need official translations along with verified copies of the original. Attention: if given preliminary admission, international applicants MUST provide legalised documents translated to German or English for the following documents: - Bachelor Degree - Transcript of Records for Bachelor Degree Legalisation confirms the authenticity of documents, signatures and authorities that issued the document. Three scenarios are possible depending on the issuing country: - no legalisation is required, - legalisation in the form of the Apostille - full diplomatic legalisation To see if and what legalisation you would need for your diploma, please refer to this Legalization of Foreign Documents in Higher Education document

Language requirements	German B2 level (according to the European Framework of Reference)

Other requirements

Interview Is a fundamental part of the admission procedure. It’s going to be conducted over the Internet. Please also attach the following documents: - Birth certificate and its certified translation into German. - CV in German Specific requirements for Non-EU applicants: Please be aware that the visa process can take up to 3 months, therefore you should send your application as early as possible! For more details on the visa application please visit the following webpage: www.oead.at

More information	fh-ooe.at/..r/data-science-und-engineering

Overview

Interested? To learn more about this study programme, entry requirements and application process, please contact one of our consultants in a country nearest to you.

Programme structure

The master’s degree in data science and engineering with two specializations in biomedical data analysis or data analysis in marketing and production is not only focused on the technical and mathematical aspects of data science. It also prepares the graduates for the future professional field of data scientists by deepening their interdisciplinary knowledge from the application domains.

Focus of the training

_Structure of data understanding: data selection, data integration and data preparation, linking, transformation and indexing of various data sources, development of meaningful data representations and visualizations
-Data storage and management in combination with big data and cloud technologies, also for real-time data
-Data analysis with methods from the areas of computational intelligence and statistics for the creation of forecast models to answer a specific question from the company
-Computer vision methods for extracting knowledge from image data
-Practice-related projects for data analysis with cooperation partners from the domains of biomedicine, marketing and production

Career opportunities

Graduates of the master’s degree in Data Science and Engineering are able to generate information from large amounts of data and derive recommendations for action that enable the company to work more efficiently. To do this, they use innovative analysis methods and develop queries that generate valuable information from confusing amounts of data. Subsequently, hypotheses are derived, which are checked and prepared for management as a basis for decision-making.

The aim is to train data scientists as data architects with strategic vision, well-founded analytical skills and distinctive technical competencies and to prepare them for taking on demanding tasks in the data science and big data environment.

-Analysis of data or data models, IT landscapes and business processes with regard to the need and the introduction of new approaches to knowledge extraction
-Design of processes for extracting, cleaning and transforming data
-Modeling of data schemes for the integration and analysis of data
-Use of data mining and statistical methods as well as development of forecast models
-Conception of 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 findings
-Communication, development and presentation of solutions to the decision-makers (specialist departments and management)