Data Analytics for Economics & Finance

1 Years On Campus Postgraduate Program

University of Glasgow

Program Overview

The MSc Data Analytics for Economics & Finance at the University of Glasgow is designed for students who want to sit at the intersection of data science, economics and finance. The programme blends advanced quantitative methods, econometrics and machine learning with real economic and financial applications, making it ideal if you’re aiming for analytical roles in finance, policy, consulting or research.

Curriculum Structure

Year 1

This one-year full-time master’s begins with preparatory training to ensure all students are confident with essential tools such as Python, MATLAB and Stata, alongside key mathematical foundations like matrix algebra and regression analysis. You’ll then develop advanced analytical skills through core modules including Applied Time Series and Forecasting, Microeconometrics: Impact Evaluation and Causal Analysis, and Statistical Machine Learning, learning how to extract insights from complex economic and financial data. In the second semester, you’ll expand into Bayesian Data Analysis and select optional modules such as Text as Data, Deep Learning, Applied Computational Finance, or Asset Pricing, allowing you to tailor the degree to your career ambitions.

Dissertation / Project

The programme concludes with a substantial 60-credit dissertation, where you can choose either a research-focused project or an applied industry-based project, applying your analytical expertise to real-world economic or financial challenges.

Focus Areas

Applied Time Series Analysis; Microeconometrics and Causal Inference; Statistical Machine Learning; Bayesian Data Analysis; Computational Finance; Asset Pricing; Text and Big Data Analytics; Deep Learning; Research or Industry Dissertation

Learning Outcomes

Graduates leave with the ability to analyse large-scale economic and financial datasets, apply advanced econometric and machine learning techniques, develop predictive and explanatory models, and communicate data-driven insights clearly to both technical and non-technical audiences.

Professional Alignment (Accreditation)

The programme is jointly delivered by the Adam Smith Business School and the School of Computing Science, ensuring strong alignment with industry expectations and exposure to interdisciplinary research-led teaching.

Reputation (Employability Rankings)

The University of Glasgow consistently ranks among the world’s leading universities, and this programme is recognised internationally through its inclusion in the QS Master’s in Business Analytics rankings, reflecting its strong employability outcomes and global reputation.

Experiential Learning (Research, Projects, Internships etc.)

At the University of Glasgow’s MSc Data Analytics for Economics & Finance, you’ll be building real, job-ready analytical skills from the very start. This programme is designed for students who want to work directly with data, not just study theory. You’ll combine economics, finance, statistics and computing science to solve practical problems using real datasets and industry-standard software. From intensive pre-sessional training to applied coursework and project-based learning, everything is structured to ensure you graduate confident using advanced analytical tools in professional environments.

Here’s how experiential learning is embedded throughout the programme:

Pre-Sessional Intensive Training: Before the academic year begins, you complete two intensive preparatory courses covering computational statistics, data analytics and econometric foundations, ensuring everyone starts with a strong technical base.
Industry Projects and Summer Internships: Students may have the opportunity to take part in summer project internships, allowing you to apply your data analytics skills to real organisational problems and gain valuable professional experience.
Programming and Analytical Software: You develop hands-on experience using Python, MATLAB and Stata for data manipulation, econometric analysis, forecasting, machine learning and financial modelling.
Applied Core Modules: Courses such as Applied Time Series and Forecasting, Microeconometrics, Statistical Machine Learning and Bayesian Data Analysis are heavily data-driven and focus on solving real analytical challenges.
Customisable Electives: Optional modules like Applied Computational Finance, Text as Data and Deep Learning allow you to tailor your studies toward finance, policy analysis or advanced data science roles.
Industry-Linked Dissertation Options: You can choose between a traditional research dissertation or an industry-focused project, giving you the chance to work with real data in a professional or applied research setting.
Collaborative Group Work: Many modules include team-based analytical projects and presentations, helping you build communication and teamwork skills alongside technical expertise.
Research and Study Facilities: Teaching and research take place on the Gilmorehill campus, with access to specialist libraries, digital research databases, computer labs and collaborative learning spaces.
Professional Development Opportunities: Through the Adam Smith Business School’s Graduate Award Scheme, you can enhance your employability by participating in workshops, seminars and extra-curricular professional activities.

Progression & Future Opportunities

Graduates from this programme leave with a powerful combination of data analytics, economics, and finance skills that are in high demand across global industries. You’ll be prepared to work at the intersection of data, policy, and finance, turning complex datasets into real economic and financial insight. Typical job roles include: Data Analyst, Economic Analyst, Financial Analyst, and Business Intelligence Analyst.

What this means for your future career:

University services that help students gain employment:
• The University of Glasgow Careers, Employability & Opportunity Service offers tailored careers coaching, CV and interview support, employer-led workshops, and access to graduate job and internship opportunities.
• Dedicated employability programmes, including employer presentations and data-focused careers events, help you understand how your skills translate directly into the labour market.
• You benefit from strong alumni networks, giving you access to graduates working in data analytics, finance, consulting, and public policy.
Employment stats and salary outlook:
• Graduates from data analytics and economics-related postgraduate programmes at the University of Glasgow typically move quickly into professional employment or further study.
• Roles in data analytics, finance, and economic analysis are among the fastest-growing and highest-paying graduate sectors in the UK, reflecting strong long-term demand for these skills.
University–industry partnerships:
• The programme is informed by close links with industry and public-sector organisations, ensuring that teaching reflects real-world applications of data analytics in economics and finance.
• Students gain experience using industry-standard tools and techniques commonly required by employers in finance, consultancy, and analytics-driven roles.
Long-term accreditation value:
• The University of Glasgow is a member of the Russell Group, recognised globally for research excellence and academic quality.
• The School of Social and Political Sciences and related economics departments have a strong international reputation, adding long-term value and recognition to your degree.
Graduation outcomes:
Graduates typically progress into roles across financial services, consulting firms, government departments, central banks, international organisations, and technology-driven companies, applying data-driven insights to economic and financial decision-making.

Further Academic Progression:
After completing the MSc Data Analytics for Economics & Finance, you can continue into PhD study in Economics, Finance, or Data Science, or move into specialised postgraduate research in quantitative economics or financial analytics. The programme also provides a strong foundation for professional development in advanced analytics, economic research, and data-driven finance roles.