Research Skills for Economics and Data Science - ECON8471

Looking for a different module?

Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2024 to 2025
Spring Term 7 5 (2.5) Adelina Gschwandtner checkmark-circle


The module consists of preparation of a 1-page initial proposal of a research question that involves significant Data Science content. Initial proposal needs to be submitted by the end of Week 26. Upon review of the proposal by the DoGS (taught) the student is allocated a supervisor. Using appropriate communication methods (e.g. online of face-to-face meetings), the supervisor helps the student better articulate their research question. For the rest of the term the student works on an extended proposal of research question. By the end of Week 35 the student submits the extended proposal along with annotated bibliography and description of expected results.


Contact hours

Private Study: 47
Contact Hours: 3
Total: 50


This is a compulsory module for MSc in Economics and Data Science (Double Award)

Method of assessment

Main assessment methods
• Initial topic proposal (20%)
• 1,000 words extended research proposal (80%)

Reassessment method: 100% coursework

Indicative reading

The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices.
The most up to date reading list for each module can be found on the university's reading list pages.

Learning outcomes

On successfully completing the module students will be able to:
1 Identify the topic of recent application, or an existing case study, of using big data analysis in its relation to a well-defined economics question
2 Understand how to work with some of the key mathematical and statistical methods applicable to the chosen topic
3 Identify sources of data, bibliographic and other information relevant to the chosen topic or case study.
4 Ask stimulating research questions by critically assessing gaps in significant areas of theory and practice
5 Contribute to existing research debates through proposing own transformative solutions
6 Bridge advanced research questions with high-performance applied and professional work with big data.


  1. Credit level 7. Undergraduate or postgraduate masters level module.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
Back to top

University of Kent makes every effort to ensure that module information is accurate for the relevant academic session and to provide educational services as described. However, courses, services and other matters may be subject to change. Please read our full disclaimer.