Central European University (CEU)
Master of Science in Social Data Science (2 years)
Vienna, Austria
MSc
DURATION
2 years
LANGUAGES
English
PACE
Full time
APPLICATION DEADLINE
EARLIEST START DATE
TUITION FEES
EUR 12,000 / per year **
STUDY FORMAT
On-Campus
* application deadline for applicants for master’s programs who wish to be considered for CEU financial aid (where available).
** payable in one or two installments, non-refundable initial tuition fee installment (€500 EUR) is paid to confirm your acceptance of our offer of admission and is credited towards the 1st tuition fee installment in year 1
Key Summary
How does the Internet change Society? How to follow, forecast and control the spreading of a pandemic? How to achieve larger participation of people in the decision-making processes using new technologies?
Our digital era in the 21st century calls for data-driven reasoning to answer these and other similar new societal, organizational, and environmental challenges. In response, the MS program in Social Data Science aims to educate a new generation of data science experts, entrepreneurs, and policymakers with a strong affinity to the social sciences, who can understand digital societies and will be able to shape their future.
Why MS in SDS at CEU?
- English language data science program with a focus on the social sciences
- Degree with a global recognition that is accredited in Austria and the U.S.
- Specializations in four interdisciplinary fields
- High-quality yet affordable degree
- We develop employable graduates: CEU is perfect if you're looking to boost your employability skills and pursue a meaningful career with a deeper purpose
To master’s candidates, we award financial aid based on academic merit. You can apply for financial support for master’s studies in the Funding section of the Online Application Form.
1-year and 2-year programs
Tailored to the unique ambitions of a student, we offer 1 and 2-year full-time programs both accredited in Austria and the U.S. The 2-year program offers the full Master program training (120 ECTS, 60 credits) in Social Data Science with specializations in various fields of the social sciences, and the 1-year program (60 ECTS, 30 credits) builds on prior training in data science during an undergraduate or graduate program and it provides focused training in the specialization fields.
Year 1
Bootcamp
- Conceptions of Social Dynamics (ECTS 0)
- Introduction to Computer Science (ECTS 0)
- Pre-session in Mathematics (ECTS 0)
Fundamental Methods of Data Science
- Applied Statistics (ECTS 4)
- Data Analysis in Python (ECTS 4)
- Machine Learning 1 (ECTS 4)
- Social Data Mining (ECTS 4)
Specialization
- Debates and Contradictions in Social Data Science (ECTS 4)
- Social Networks 1 (ECTS 4)
- Course from the list of specialization tracks (ECTS 4)
Advanced Methods and Concepts
- Topics of Social Data Science (ECTS 4)
- Digital Data Collection Methods: (ECTS 4)
- Opportunities and Risks (ECTS 4)
- Data Visualization (ECTS 4)
Academic writing
- Academic Writing for MS in Social Data Science (ECTS 4)
Optional courses
- Free elective MS level course from any programs at CEU PU (ECTS 4)
Project 1
- Research Internship (ECTS 4)
Year 2
Fundamental Methods of Data Science
- Course from the mandatory elective course list of the module (ECTS 4)
Specialization
- Course from the list of specialization tracks (ECTS 4)
- Course from the list of specialization tracks (ECTS 4)
Advanced Methods and Concepts
- Network Science (ECTS 4)
- Ethics of Big Data (ECTS 4)
- Course from the mandatory elective course list of the module (ECTS 4)
- Course from the mandatory elective course list of the module (ECTS 4)
Optional Courses
- Free elective MS level course from any programs at CEU PU (ECTS 4)
- Free elective MS-level course from any program at CEU PU (ECTS 4)
Seminar
- Research seminar course (ECTS 4)
- Thesis seminar course (ECTS 4)
Project 2
- Capstone project 1 (ECTS 4)
- Capstone project 2 (ECTS 4)
The SDS MSc program will provide two tracks for participating students: i) academic and ii) applied social data science training with different emphases on academic and practical skills. The program will provide the following knowledge, skills, and competencies.
The students will acquire knowledge
- Of an Arsenal of Tools of Quantitative and Data-driven Approaches to Study Social Phenomena;
- Of the Fairness and Biases of Social Data Science Methods;
- Of the Legal and Ethical Framework of Data Collection and Analysis in Social Sciences, Including Specificities of Big Data;
- About Main Concepts, Ideas, and Challenges in at Least One Field of Social Sciences, as well as the Important Special quantitative and Qualitative Methods;
- Of the New Possibilities That Socially Related Big Data Types Enable for Studying Contemporary Problems in Business and Academic Research;
- To Identify the Societal Potential of and Challenges to Working With Big Data.
The students will be equipped with skills in how to
- Understand and model complex, networked, dynamic, social, economic, political, technological, and ecological systems;
- Apply a critical and reflexive view to the advantages and dangers of data-driven methodologies in real-world applications observing and predicting human behavior.
- Master the state-of-the-art programming language for collection, curation, processing, preparation, and analysis of data;
- Employ state-of-the-art data science tools, including methods from supervised and unsupervised machine learning, web mining, network analysis, visualization, spatial analysis, natural language processing, etc. to the analysis of societal and organizational problems;
- Collect data in various ways using tracking, monitoring, crawling, or transactional data collection methods or social experiments;
- Analyze data of various kinds recording temporal, spatial, relational, feature, etc. information
- Design online or digital social experiments, execute, measure, and interpret their results
- Identify correlation patterns and causal relationships in social data and build predictive models using human behavioral datasets;
- Combine quantitative and qualitative empirical methods from social sciences, including statistical analysis, digital methods, and experimental methods with Data Science tools to analyze societal and organizational problems;
- Communicate with researchers both in social sciences and data science;
- Communicate research-based knowledge in writing, visualization, and verbal presentation.
By the end of the program, students will be competent in
- Planning and completing social data science studies/examination/research of social phenomena in various fields of social sciences;
- Managing the ethical aspects of collecting and processing personal data as well as making decisions based on the data;
- Participating in and coordinating cooperation in interdisciplinary teams with people from other scientific fields and traditions to work on research problems of social data science;
- Independently taking responsibility for further personal scientific development and specialization in the academic and private sectors or governance and NGOs.
While all these skills are important for a successful career in academia or the data industry, learning outcomes will differentiate between the two tracks. More emphasis will be put on the fundamental questions and social science applications for students on the academic track, while the training of students on the applied social data science track will focus more on the methodological tools and real-world applications.
Develop the best skills to accelerate your career
In the two-year program, you will master state-of-the-art computational skills for the collection, curation, processing, preparation, and analysis of data. You will develop a high level of proficiency in methods from applied statistics, machine learning, web mining, network analysis, visualization, spatial analysis, natural language processing, and many more. Both 1- and 2-year programs will develop your skills via specializations to understand and model complex, networked, dynamic, social, economic, political, technological, or ecological systems with a critical reflection on the advantages and dangers of data-driven methodologies.