Master of Science in Social Data Science (1 year)

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.

1-year SDS MS program course list

Fundamental Methods of Data Science

• Course from the mandatory elective course list of the module

Specialization

• Course from the list of specialization tracks
• Course from the list of specialization tracks
• Course from the list of specialization tracks

Advanced Methods and Concepts

• Topics of SDS
• Ethics of Big Data
• Course from the mandatory elective course list of the module

Optional Courses

• Free elective MS level course from any programs at CEU PU
• Free elective MS level course from any programs at CEU PU

Academic writing

• Academic Writing

Seminar

• Thesis seminar course

Projects

• Capstone project 1
• Capstone project 2

The program will provide two tracks for participating students: i) academic and ii) applied social data science training with different weights to 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.

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

• The Planning and Completion of 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.