Analysts for the Most Valuable Resource of the Information Society
Data Science is a young, but clearly established and highly demanded field. Data scientists develop end-to-end solutions, from data preparation and modeling to integration into a company’s IT landscape.
With the ongoing digitalization of all areas of our working and everyday lives, the amount of available data that needs to be examined in relation to different questions and objectives is increasing. Data scientists are able to process and analyze these large amounts of data and turn them into meaningful information. They thus help companies and organizations to derive benefits from data sets.
Graduates of a data science degree program have the necessary knowledge and support companies in important decision-making processes in all phases. The Data Science Master’s program therefore has a strong practical focus. Students learn comprehensive skills in handling large amounts of data. Since data analysis plays a major role in almost all industries, graduates of the Data Science Master’s program have many possible career paths open to them.
Program
Facts
- Start of semester: September
- Duration: 120 ECTS credits, 4 semesters
- Degree: Master of Science (MSc)
- Mode: Part-time | English
- Costs per semester: € 363.36 tuition fee, € 25,20 ÖH fee; € 3,000 Tuition fee for students from third countries: exceptions and information
- Remote learning elements (blended learning)
- Recommended semester abroad (optional): 3, 4
- Available Double Degree program(s): Buenos Aires Institute of Technology
Attendance times
- Perform the complete data collection process according to the current state of the art, for example for textual data, image and video data or sensor data.
- Prepare and model this data for analysis.
- Conduct analyses taking into account ethical, data protection, infrastructure and business aspects.
- Compare, select, and apply relevant analysis methods, approaches, and algorithms.
- Communicate the results of the analyses in a way that is appropriate for the target group and transfer them to operations.
- Plan, implement, and successfully manage Data Science projects in consideration of business needs and for the purpose of value creation.
- for Data Science projects to elicit requirements and define goals.
- Plan and implement Data Science projects as an interface together with the business department and the IT department.
- Communicate with technical and non-technical professionals when designing and implementing Data Science projects, and present ideas and implementation proposals.
- Elective 1 (2nd semester)
- Smart City: Collection & evaluation of sensor data, application to optimization of urban development (e.g., detection of heat hot spots, smart metering etc.)
- Process Analytics: Process analysis, process improvement, process effectiveness, adherence to specifications/compliance
- Data Warehouse & BI: Star Scheme, ETL-Process, Reporting, OLAP
- Elective 2 (2nd semester)
- Natural Language Processing: text transformation, preprocessing, supervised and unsupervised models, sentiment analysis, generative AI
- Finance: Dealing with financial time series, ARIMA models, tools in R
- Big Data Analytics: Storage & processing of very large amounts of data, parallel processing with Hadoop ecosystem, analyzes with Spark/Kafka
- Elective 3 (3rd semester)
- Smart Maintenance: Reliability Analysis, Time-to-Failure, Machine learning methods.
- Marketing Analytics: Real-time behavior-based marketing, user profiling, pricing strategies, market simulation
- Trustworthy AI: Data Bias, Fairness, Explainability, Model Robustness
- Elective 4 (3rd semester)
- Renewable Energies: smart metering, prediction of demand curves, solar panel detection
- Medical Imaging: Treatment of medical data, image recognition processes using deep learning methods (e.g. tumor detection), U-Net Architecture
- Security & Privacy in AI: Anonymization, Federated Learning; Attacks, Model Robustness, Adversarial examples & defense
Career Prospects
This is an exceptionally high-demand job description, and this degree program strikes a chord with the labor market’s need for data professionals.
Requirements
Master’s degree programs build on a completed bachelor’s degree program and allow students to specialize or focus on topics in more detail or to expand their existing expertise.
You must meet subject-matter requirements to be admitted to the Data Science master’s degree program. Prerequisites include a bachelor’s degree from a UAS in a relevant subject matter or an equivalent degree from a recognized post-secondary educational institution (at least 180 ECTS credits) in Austria or another country.
If basic equivalence has been established except for a few missing prerequisites, the program director can require students to take exams to establish full equivalence. These exams are taken during the master’s program.
News from this Program
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Application
The next step to study Master Data Science is to apply via our online application system:
- The entire application process is handled via a dedicated application website.
- Your data is stored securely and is being treated with strict confidentiality.
- A registration system makes it possible to start an application and complete it at a later point in time.
- Once you have entered your user data and uploaded documents, you can also use them for subsequent applications.
