MSc Financial Analytics

1 Year On Campus Masters Program

Queens University Belfast

Program Overview

The MSc Financial Analytics at Queen’s University Belfast is designed for anyone who wants to combine finance with cutting‑edge data analytics skills. If you’re curious about financial markets, enjoy working with data, and want to make smart, data-driven decisions in a professional setting, this course gives you the tools to do exactly that, from programming to predictive modelling.

Curriculum Structure

Year 1 (Core Experience)
In your first year, you’ll get hands-on with modules like Financial Data Analytics, Advanced Analytics & Machine Learning, and Financial Modelling in Python. You’ll learn how to handle large financial datasets, build predictive models using machine learning, and apply Python programming to real financial problems. Modules like Data Management will also show you how to organise, extract, and clean data, a skill every financial analyst relies on.

Final Project / Dissertation (Summer)
The programme finishes with an Applied Research Project or Academic Dissertation, where you get to tackle a real-world question in finance. This is your chance to pull together everything you’ve learned — analyse data, apply models, and present your findings — giving you a portfolio piece that can impress future employers.

Focus areas:
Financial econometrics, machine learning for finance, Python programming, advanced data analytics, financial decision-making, data management, applied research.

Learning outcomes:
You’ll finish the MSc able to turn complex financial data into actionable insights, build and interpret predictive models, communicate findings clearly, and independently carry out research that connects theory with real-world financ8e applications.

Professional alignment (accreditation):
The course is part of the CFA Institute University Recognition Program and gives you access to resources from the Certificate in Quantitative Finance (CQF) Institute, helping you build the professional skills that employers in finance and fintech value most.

Reputation (employability & rankings):
Queen’s University Belfast is a Russell Group university, globally recognised (around #199 in the QS World University Rankings 2026) and known for strong graduate employment prospects. Plus, the triple-accredited Queen’s Business School (AACSB, AMBA, EQUIS) means your qualification is respected worldwide.

Experiential Learning (Research, Projects, Internships etc.)

At Queen’s, you won’t just learn finance and analytics — you’ll experience them. From using the same tools professionals rely on every day to working on real-world projects, the programme is built to make sure you graduate with practical skills you can apply straight away. You’ll get hands-on with data, collaborate with peers on challenging problems, and work on projects that mirror what you’ll encounter in a professional environment:

Here’s what that looks like in practice:

FinTrU Trading Room: Work with Bloomberg terminals to analyse live market data and simulate real trading scenarios, giving you a taste of life in a financial firm.
Industry-standard software: Get experience with Python, R, SQL, Hadoop, Spark, Google Cloud, and Posit Workbench, so you’re confident handling big data and advanced analytics.
Team projects and case studies: Many modules involve group work, letting you practise collaboration, presentation, and problem-solving skills just like in the workplace.
Applied Research Project or Dissertation: Tackle a substantial project where you define the question, analyse the data, and present your findings professionally — a great portfolio piece for future employers.
Student Managed Fund experience: Engage with a real investment portfolio to understand financial decision-making in action.
Modern facilities and collaboration spaces: The Business School provides breakout rooms, study spaces, and a Business Engagement Hub to support teamwork, networking, and learning in a professional setting.

Progression & Future Opportunities

Graduates of the MSc Financial Analytics at Queen’s step into careers where finance meets data, technology, and strategy. Many move into roles such as Data Scientist, Financial Engineer, Risk Analyst, Business Analyst, or Portfolio Analyst, using their analytical skills to solve complex problems in banks, consultancies, fintech firms, and global organisations:

Here’s how Queen’s helps turn your degree into real career momentum:

Personalised careers support: From day one, you’ll be supported by the Queen’s Careers, Employability & Skills team, offering one-to-one guidance, CV and interview coaching, and employer-led workshops — plus continued access to the MyFuture careers platform for up to two years after you graduate.
Strong graduate outcomes: Queen’s consistently performs well for graduate prospects in Accounting and Finance, and Financial Analytics graduates have progressed into roles with organisations such as Citi, Deutsche Bank, Bank of China, Amazon, and specialist analytics firms, reflecting the programme’s strong industry relevance.
Industry connections: The course’s applied focus and links with employers across finance, fintech, and analytics help you build professional networks and gain insight into what employers are really looking for.
Professional recognition that lasts: As part of the CFA Institute University Recognition Program and an academic partner of the Certificate in Quantitative Finance (CQF) Institute, the degree carries long-term value and recognition within global financial services.
Enhanced employability awards: You can boost your CV further through Queen’s employability initiatives, such as extracurricular activities, professional development programmes, and industry engagement opportunities alongside your degree.

Further Academic Progression:
If you decide to continue your academic journey, this MSc provides a strong foundation for PhD study in finance, data analytics, economics, or related quantitative fields. Graduates also go on to pursue advanced professional qualifications or specialist postgraduate study in areas such as quantitative finance, machine learning, or financial risk, opening doors to research-focused or senior technical roles.