Master in Data Science for Social Sciences
Universidade de Aveiro
Key Information
Campus location
Aveiro, Portugal
Languages
English
Study format
On-Campus
Duration
2 years
Pace
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Tuition fees
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Application deadline
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Earliest start date
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Introduction
Master in Data Science for Social Sciences
The Master on Data Science for Social Sciences prepare students to: i) collect, manipulate and analyze large amounts of structured and unstructured data; ii) address problem formulation and design solutions, using models or computational systems, for concrete needs of companies and institutions and emerging societal challenges; and iii) understand of socio-economic phenomena and their territorial expression and the adequacy of decision support tools in the process of formulating, implementing and evaluating public policies.
The study cycle is organized as a complement to the initial training courses of the UA. Moreover, it allows adjusting or updating professional profiles for whom faces complex environments demanding analytical skills or involving decision-making efforts, either in the public or private domains. Training enables students to choose, develop and apply appropriate solutions to a given problem or challenge. The training options is designed in two directions: i) modern analytical instruments (advanced, complex), but consolidated in scientific research; ii) use several tools and models, enabling students to formulate (more) effective and (more) efficient answers to specific and emerging problems that arise in contemporary decision-making processes (e.g. territorial-based; organizational; production processes).
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Admissions
Scholarships and Funding
Several scholarship options are available. Please visit the university website for more information.
Curriculum
Year 1
1st Semester
- Programming and Algorithms in Science
- Introduction to Data Science
- Statistics and Optimisation for Decision Making
- Geographic Information Systems for Social Sciences
- Operations Research Methods
2nd Semester
- Database Systems
- Time and Spatial Econometrics
- Machine Learning
- Seminar
Year 2
1st Semester
Dissertation / Project / Internship