MSc Economics and Data Science

This unit will help students on the MSc Economics and Data Science in the development of vital study, employability and programming skills. Students will undertake a substantive research project under the supervision of a member of staff. The topic of the research is chosen by the student subject to the approval of the supervisor. The project work involves the formulation of the research question, the choice of appropriate statistical methods, the creation and/or collection of the data as needed, the development of appropriate computer code to implement the analysis, and a critical appraisal of the results.

All aspects of the projects must be summarized in a written document that forms the basis for an oral presentation of project results to the supervisor and another member of staff. The unit grade will depend on both the written document and the oral presentation.

Successful completion of course work component of MSc consisting of:

ECON61001,  ECON60101, ECON60111, ECON62020 Programming & other skills for data scientists, ECON61351 Data Science & Machine Learning 1, ECON62012 Data Science & Machine Learning 2

+ two 15 credit optional courses 

The unit aims to provide the MSc Economics and Data Science students with the opportunity to undertake a substantive research project involving the application of data scientific methods to economic data. This work is undertaken under the supervision of a member of staff; however, students are expected to use their own initiative to develop the project.  

Throughout the dissertation writing process students are expected to work independently. Supervisors will advise and guide.

The execution of this project provides an opportunity to use and further enhance the knowledge of data scientific methods learnt in ECON61351 & ECON62012 (Data Science & Machine Learning 1 & 2), programming skills learnt in ECON62020, and knowledge of economics gained in other courses.

The unit grade is based on a written research paper and an oral presentation attended by the supervisor and one other member of staff, thereby providing the student with experience in oral and written communication of data-scientific results.

In order to be able to take up positions in government, central banks or private sector organizations as a data analyst/economist students will have to be able to demonstrate strong skills in areas supported by this unit:

• Statistical methods for data-scientific analysis such as machine learning
• The mathematical theory behind data-scientific methods
• The implementation and interpretation of empirical data-scientific analysis of economic data
• The ability to organise and bring to completion a large project
• Communication (oral and written) 
   

Meetings with supervisor: 10 hours

Independent study: 440hrs  

Students are encouraged and supported in thinking about potential research questions and how they may be addressed as part of the ECON62020 Programming and Other Skills for Data Scientists course. Students will work on their dissertation projects following the conclusion of their summer exams until early September, a period of approximately 12 weeks. 
 

Students should be able to acquire understanding of strengths and weaknesses of new data science methods and their suitability for the particular question of interest.

Students should be able to:

• Describe economic and/or policy problems by applying key concepts of economic analysis to pinpoint the decisive mechanisms at work
• Analyse the potential effects of economic policy by applying appropriate economic theory to discuss potential policy actions 
• Explain features, assumptions and estimation methods used in econometric and data science methods
• Critically assess and engage with empirical economic research
• Identify the estimation methods which are most suitable to tackle the research question of the dissertation

Students should be able to:

• Implement appropriate econometric and data scientific techniques (using R and/or Python) to address empirical problems
• Identify data sources appropriate to investigating a particular question
• Prepare data from a variety of sources in a way that allows the application of statistical analysis
• Organise and maintain the data, code and drafts for a large project. 
   

Students should be able to:

• Analyse real life data to understand and describe empirical issues across a range of disciplines and real-world settings
• Present empirical research in written and oral form. 

Written dissertation of 7000 words, 80%

Oral presentation of 20 minutes, 20%

Formative assessment is via progress presentations in supervisor meetings. Feedback will be provided immediately on dissertation content, but also on presentation/communication.

Individual feedback provided for both the Written Dissertation and Oral presentation.