Socio-Economic Big Data-Project

F2026

Samfundsøkonomi

Project

Mandatory

English

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You register for activities through stads selvbetjening during the announced registration period, which you can see on the Study administration homepage.

When registering for courses, please be aware of the potential conflicts and overlaps between course and exam time and dates. The planning of course activities at Roskilde University is based on the recommended study programmes, which should not overlap. However, if you choose optional courses and/or study plans that goes beyond the recommended study programmes, an overlap of lectures or exam dates may occur depending on which courses you choose.

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Fuad Mehraliyev (fuadm@ruc.dk)

Nina Torm (ninatorm@ruc.dk)

ISE Tilmelding & Eksamen (ise-eksamen@ruc.dk)

U60890GB

The aim of this project is to strengthen students' competences to independently analyse a complex socio-economic problem at an advanced level.

Problem Area and Concept Clarification Students must identify one or more research questions grounded in academic literature and/or policy debates. A brief overview of the theories, concepts, and methods intended to address the research question(s) should be included. The problem formulation must have socio-economic relevance and necessitate big data analysis for empirical investigation. The operational definition of “big data” can be agreed upon with the supervisor, typically based on volume, variety, and/or velocity.

Literature Review This section should elaborate on the academic foundation of the problem through a review of relevant scientific studies. It must also include a critical assessment of the methodological strengths and weaknesses of these studies. Particular attention should be given to how the project extends existing findings and, where applicable, how the use of big data could enhance current knowledge.

Methodology This section must present a well-reasoned choice of method(s). It is important to reflect on both the advantages and potential limitations of the selected approach. Additionally, consider discussing other methods that were considered and explain why they were ultimately not used.

Presentation of the Empirical Data Used This includes a description of how the data were collected, processed, and cleaned for analysis. The discussion should also assess the quality of the data. The data used may be structured, unstructured, numeric, non-numeric, public, private, or a combination of various sources and types. It is encouraged to use visual tools such as tables and bar charts to illustrate data characteristics.

Analysis / Results Conduct an independent and in-depth analysis of the big data and present the main results. This could include but not limited to techniques such as clustering, regression, dimension reduction, text analytics, neural networks and/or other relevant tools to answer the research question(s). Visual tools, such as tables and figures, should be used to enhance the readability of the results.

Discussion and Conclusion Discussion and Conclusion could be combined into one or presented as two separate sections. The discussion includes the comparison of you results with previous literature, such as how/to what extent your results confirm, contradict, or extend previous literature, in what ways and why. The conclusion section usually has a key summary of your findings along with their contribution to theory, existing knowledge in the field, and/or practical implications.

Quantitative data and methods in projects and theses: a workshop

If you and your group find quantitative analyses relevant to answer your research question. We offer a workshop where a group of researchers will talk about how to use quantitative data in projects and theses. We will present an overview of publicly available data sets, as well as the possibilities, if you wish to collect your own data. We expect you to have taken a course in basic quantitative methods (BC8 or equivalent).

The workshop is held on Wednesday, February 25, at 12.15-02.00 pm, in theory room 25.2-005

There is no fixed syllabus.

Approx. 270 hours for each student, distributed as follows: Workshop introducing socio-economic methodology, empirical issues and project/group formation: 8 hours, independent work in the project group with development of analyses approx. 217 hours, supervision and preparation for supervision: approx. 15 hours, preparation for exam and exam approx. 30 hours.

The project is evaluated regularly. At the beginning of the semester, the project convenor is informed if the project is to be evaluated and notifies the students. The evaluation is carried out in accordance with the study board's evaluation practice.

By the end of the project, students will be able to:

• independently identify a relevant problem and organise an investigation into it

• choose a relevant analysis that can shed light on the chosen problem

• select different types of data for the analysis

• master the required statistical skills

• reflect at a high professional level on the scope of different economic methods

• be able to communicate his/her project's issues, data and results to decision-makers.

Oral project exam in groups with individual assessment


Permitted group size: 3-5 students.

The character limits of the project report are:
For 3 students: 96,000-120,000 characters, including spaces.
For 4 students: 120,000-144,000 characters, including spaces.
For 5 students: 144,000-168,000 characters, including spaces.
The character limits include the cover, table of contents, abstract, bibliography, figures and other illustrations, but exclude appendices.



Time allowed for the exam including time used for assessment is for:
3 students: 75 minutes.
4 students: 90 minutes.
5 students: 105 minutes.



Permitted support and preparation materials at the oral exam: All

Assessment: 7-point grading scale
Moderation: Internal co-assessor.

Samme som ordinær eksamen / same form as ordinary exam

The student's work will be assessed based on the degree to which they have:

demonstrated the ability to independently identify a well-defined, academic and/or policy relevant problem within the field of socio-economics

demonstrated skills in selecting, applying and mastering relevant big data analysis method(s) when working on the problem,

been able to discuss and communicate academic results and proposed solutions as well as new research-based knowledge at a high academic level in relation to socio-economic issues