Program Overview
Introduction to the Data Science Master's Program
The master's program in Data Science at the University of Vienna is a joint venture of several faculties, including Business, Economics and Statistics, Computer Science, Historical and Cultural Studies, Mathematics, and Philological and Cultural Studies. This program aims to provide a practically oriented and scientifically sound education in the field of modern data science.
Program Overview
The Data Science master's program is designed to equip students with the skills to gain knowledge from large volumes of data, which represents added value for the respective area. The program requires a diverse and interdisciplinary competence profile, including the practical handling of large amounts of data, a solid mathematical and statistical foundation, as well as knowledge in the respective area of application.
Program Structure
The program consists of several compulsory module groups, including:
- Mathematical and Statistical Foundations
- Optimisation Methods
- Machine Learning
- Scalable Algorithms
- Visual Data Analysis
- Doing Data Science
- Ethical and Legal Issues
- Specialisation in Areas of Data Science
- Master's Thesis
Admission Requirements
To be eligible for the program, students must have a bachelor's degree in a relevant field, such as Computer Science, Business Informatics, Mathematics, or Statistics. English language proficiency at level B2 (Common European Framework of Reference for Languages) is also required.
Program Details
- Degree: Master of Science
- Duration: 4 semesters / 120 ECTS credits
- Language: English
- Selection Procedure: Information about the selection procedure is available
- Curriculum: The curriculum includes a range of courses, such as Introduction to Machine Learning, Statistics for Data Science, and Visual and Exploratory Data Analysis
Focus on Interdisciplinarity
The program is designed to provide students with a broad interdisciplinary exchange, with courses and disciplinary locations allowing for a very broad exchange.
Five Key Concepts
The program focuses on five key concepts:
- Mathematical & Statistical Foundations
- Mining Massive Data
- Optimisation
- Visual & Exploratory Data Analysis
- Ethics & Legal Issues
Program Structure and Topics
The program structure and topics include:
- CORE Modules (30 ECTS)
- Introduction to Machine Learning (6 ECTS)
- Statistics for Data Science (6 ECTS)
- Mathematics of Data Science (4 ECTS)
- Optimisation Methods for Data Science (4 ECTS)
- Mining Massive Data (6 ECTS)
- Visual and Exploratory Data Analysis (4 ECTS)
- Doing Data Science, Ethical and Legal Issues (28 ECTS)
- Doing Data Science, Ethical and Legal Issues (12 ECTS)
- Data Analysis Project and Seminar (16 ECTS)
- Specialisation in Areas of Data Science (34 ECTS)
- Specialisation in Areas of Data Science (34 ECTS)
- Master's Seminar (2 ECTS)
After Graduation
Graduates of the program receive a sound and broad education in core modules, including algorithmic, mathematical, and statistical foundations, as well as explorative data analysis. They also gain in-depth knowledge in concrete fields of application and acquire practical skills in great demand on the labor market.
Career Paths
Graduates are qualified to deal with huge volumes of data, statistically analyze complex data, and develop, implement, and analyze efficient algorithms for data analysis. They can pursue careers in various fields, including humanities, language processing, finance, medicine, physics, or computational science.
Graduate Perspectives
Graduates of the program rate the level of difficulty as high (3.8) and the overall program as good (2.0). They find employment immediately after graduation and earn an average salary of 3,725 (women) and 3,991 (men) gross per month within three years after graduation.