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Course summary

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, as well as marketing and business. 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.
  • Adam Smith Business School is triple accredited.
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 from economics, 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. Core Courses Applied Time Series and Forecasting Bayesian Data Analysis Machine Learning and Artificial Intelligence for Data Scientists (School of Computing Science) Microeconometrics: Impact Evaluation and Causal Analysis Optional Courses Applied Computational Finance Deep Learning (School of Computing Science) Empirical Asset Pricing Financial Information Retrieval Financial Market Microstructure Programming and Systems Development (School of Computing Science) Text as data – Introduction to document analytics (School of Computing Science) 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. You will have the opportunity to attend the following two preparatory (pre-sessional) courses: Computational Statistics and Data Analytics Econometrics and Statistics Review

How to apply

International applicants

International applicant information can be found via by searching for 'international'.

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. 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 and 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: the reference you supply with your application should be an academic reference.

Fees and funding

Tuition fees

No fee information has been provided for this course

Additional fee information

All fees are published on the University of Glasgow website.

Sponsorship information

Sponsorship and funding information can be found via by searching for 'scholarships'.

Data Analytics for Economics and Finance at University of Glasgow - UCAS