This programme offers students a multi-disciplinary curriculum that will prepare them for work in all fields of data leading professions related to economics and finance. The key features of the programme include its combination of insights from economics and finance, statistics and computing science with a focus on creating a new generation of professionals who can use data-rigorous methods in order to inform complex decision making and will provide you with a set of skills that are future-proof and always in high demand. The programme is delivered collaboratively with the Adam Smith Business School and the School of Computing Science
Why this programme
- Join the prestigious Adam Smith business school at an exciting time as we embark on transforming the University of Glasgow into one of the leading institutions worldwide covering the field of Data Analytics.
- Choose from a series of high-quality courses delivered from Economics and Finance in the Adam Smith Business School and the School of Computing Science.
- You will complete advanced training in order to develop data analytic skills in preparation for successful careers in business or industry.
- The programme also provides a solid foundation for PhD study.
- You will have the opportunity to participate in summer project internships with some of our prestigious partners.
- You can gain recognition for extra-curricular activities by joining the Adam Smith Business School’s Graduate Award Scheme.
Programme structure
This programme offers a distinctive and innovative multi-disciplinary approach to the field of data analytics as applied to economics and finance, comprising core and elective courses offered from the Departments of Economics, Accounting and Finance, and Computing Science.
The programme will build on students’ strong interest in data analytics to develop their skills in using data rigorous methods in order to inform complex decision making, using big data. It will provide advanced training in time series analysis, panel data econometrics and Bayesian inference, based on internationally-recognised research to equip students to apply their knowledge and skills to conduct state-of-the-art research to lead and deliver projects.
Schedule
- The programme begins with two pre-sessional courses in early to mid-September, one course in computational methods (Python, MATLAB and Stata), and one course in revisions of matrix algebra and statistical regression models. These courses ensure that students from different backgrounds can all catch up to the required level of pre-requisite knowledge.
- The first semester begins in October with three core courses covering three aspects of data analysis: econometrics of macroeconomic and financial data (i.e. time series analysis); econometrics of microeconomic/disaggregated data; and machine learning inference.
- The second semester comprises one core course and electives. The core course introduces Bayesian inference as a unified estimation methodology for all aspects of econometric models, both traditional and high-dimensional (e.g. models for policy-making that have more variables than available observations).
- The elective courses cover various aspects of applied finance as well as data science, in order to fit the needs of students with different career prospects and different educational backgrounds.
- Note that in order to be able to select in the second semester the two electives offered from the School of Computing Science (Text as data, and Deep Learning) students have to also select the course Programming and Systems Development (PSD) which is offered in the first semester. That is, the PSD course is a pre-requisite for the two electives from Computing Science. Our finance electives can be selected by all students without restrictions.
- Award of the MSc in Data Analytics for Economics and Finance, requires students to accumulate 120 credits from taught courses.
- There is also a thesis component worth 60 credits. The thesis can be delivered using the standard research pathway, or (conditionally on good performance in the core courses) the thesis can be written as part of an internship with one of our partners.
Presessional courses
You will have the opportunity to attend the following two preparatory (pre-sessional) courses:
- Computational Statistics and Data Analytics
- Econometrics and Statistics Review
Both are two week intensive courses that take place in September.
Core courses
- Applied time series and forecasting
- Microeconometrics: impact evaluation and causal analysis
- Statistical machine learning
- Bayesian data analysis
Optional courses
- Text as data for msc
- Deep learning for msc (m)
- Cybersecurity fundamentals for msc (m)
- Financial information retreval
- Applied computational finance
- Empirical asset pricing
- Financial market microstructure
- Data science for marketing analytics
Dissertation
- Dissertation DAEF Industry pathway or Dissertation DAEF Research pathway
Programme alteration or discontinuation
The University of Glasgow endeavours to run all programmes as advertised. In exceptional circumstances, however, the University may withdraw or alter a programme. For more information, please see: Student contract.
Career prospects
The MSc Data Analytics for Economics & Finance will be suitable for those who have developed a strong interest in obtaining data analytic skills combined with economic insights and aspire to follow a career in finance, banking, consulting, government and other major international institutions.
The programme delivers a multi-disciplinary curriculum that will prepare graduates for work in all fields of data-rigorous professions related to economics and finance, as well as marketing and business. It is also advantageous for direct admission to a PhD programme.
Fees funding
Tuition fees for 2023-24:
MSc
- Uk - Full-time fee: £11,520
- International EU - Full-time fee: £27,930
Entry requirements
2.1 Honours degree in Economics, Finance, Computing Science or another subject with a quantitative focus.
All courses in this degree require lots of programming and, while prior programming experience is not necessary, you will be expected to gain advanced programming skills in a short time frame.
Additional documents required when applying for this programme
In addition to the documents listed in 'how to apply', please supply a personal statement detailing:
- why you have applied for this programme
- the relevant experience you have to support your application
- how you think the programme will benefit you in the future
It is compulsory to submit this personal statement along with the rest of your application.
Please note that a research proposal is not required as part of the application process.