Finance with Data Science MSc

1 Years On Campus Postgraduate Program

University College London

Program Overview

The MSc Finance with Data Science at University College London is a one‑year full-time programme that combines core finance theory with modern data science, econometrics, and computational methods. It is ideal for students with strong quantitative backgrounds who want to pursue careers in finance, data analytics, fintech, investment analytics, or quantitative finance roles that require both financial knowledge and data-driven expertise.

Curriculum Structure

Term 1

Students start with Financial Econometrics and Data Science and Corporate Finance and Financial Markets, which provide a strong foundation in finance fundamentals, market structure, valuation, corporate finance, and the statistical and computational tools needed for financial data analysis.

Term 2

This term includes Time Series Analysis and Forecasting and Big Data Analytics, equipping students with advanced data science and forecasting skills applicable to financial markets. Optional modules allow specialisation in areas like Options and Derivatives, The Economics of Trading and Exchanges, and Advanced Corporate Finance, depending on your career focus.

Term 3 & Summer (Project / Dissertation Phase)

The final phase involves a Finance with Data Science Research Project, applying your combined finance and data science skills to real-world problems, including financial modelling, forecasting, or empirical analysis. Electives may further cover Investment Strategies and Risk Management, International Finance, or Behavioural Finance and Neuroeconomics, providing flexibility to deepen expertise.

Focus Areas

Financial econometrics and data science; corporate finance and financial markets; time-series forecasting and big-data analytics; derivatives and trading economics; investment strategies and risk management; international finance; behavioural finance.

Learning Outcomes

Graduates will:

Gain strong competence in finance theory, including corporate finance, markets, and valuation, combined with advanced data analytics and econometrics skills.
Develop the ability to analyse large financial datasets, apply statistical and machine-learning methods, build models for forecasting and risk evaluation, and generate data-driven insights for finance decisions.
Acquire hands-on research and practical experience through the dissertation, preparing for data-intensive roles in finance, investment analysis, risk modelling, fintech, or quantitative finance.

Professional Alignment (Career Relevance)

The programme prepares students for roles blending finance and data, such as quantitative analyst, risk analyst, investment/portfolio analyst, financial engineer, fintech analyst, or positions at hedge funds, investment banks, and financial-technology firms. The strong emphasis on data and quantitative skills gives graduates a competitive edge in global finance and fintech careers.

Reputation (Employability & Academic Standing)

UCL is globally recognized for academic excellence, and this programme combines expertise from the School of Management and Economics Department. It equips graduates with rigorous analytical and quantitative training, preparing them for careers in major finance centres or data-driven finance roles internationally.

Experiential Learning (Research, Projects, Internships etc.)

If you join the Finance with Data Science MSc at University College London (UCL), you won’t just study finance theory. You’ll build real-world, data-driven finance skills, working hands-on with actual financial data, modern coding tools, and analyses that reflect what banks, hedge funds, asset managers, or fintech firms do. You’ll be based in London’s financial hub — perfect for networking, industry exposure, and living near the heart of global finance activity.

Here’s how the experiential learning works — and where you’ll get practical, relevant training:

Finance + Data Science blended coursework: The program starts with core modules such as Financial Econometrics & Data Science and Corporate Finance & Financial Markets, giving you a strong grounding in finance theory along with data-analysis methods. In the second term, you take modules like Time Series Analysis & Forecasting and Big Data Analytics / Machine Learning, learning to work on and model real financial data.
Hands-on data tools & real financial databases: You’ll work with industry-standard financial data sources and analyse them using tools like Python and Excel. This ensures you graduate fluent in finance, coding, data manipulation, and producing data-driven analyses — a “quant-ready” finance specialist profile.
Customizable electives + breadth of finance/data focus: After core modules, you can choose electives such as Options & Derivatives, Advanced Corporate Finance, Investment Strategies & Risk Management, International Finance, or Behavioural Finance & Neuroeconomics. This lets you tailor the degree to focus on quant-fund work, corporate finance, risk management, or behavioural finance.
Research project – bridging data & finance: In the final term, you complete a substantial research project. This allows you to apply everything you’ve learned: data collection & cleaning, econometric/statistical analysis, financial modelling, and reporting — mimicking real-world finance-data work.
Industry & career exposure via location & networking: Since teaching happens in London’s financial hub, you're immersed in one of the world’s leading financial centres. UCL also supports networking through speaker series, employer events, alumni connections, and partnerships, giving you access to industry contacts and insight beyond academics.

🎯 What this degree prepares you for

You’ll graduate with a powerful combination: strong finance fundamentals + high-level data science & econometric skills + coding ability + data handling capability — a profile that stands out for quantitative finance, risk analytics, asset management, fintech, or data-driven finance roles.
Typical career paths include quantitative analyst (hedge funds, banks), risk analyst or risk-management specialist, data-driven investment/portfolio analyst, credit analyst, or roles in fintech startups performing data-intensive finance work.
Because UCL is globally ranked and the programme is cutting-edge, the degree provides global mobility and brand value, especially if you plan to work in major financial hubs such as London, New York, or Hong Kong.

Who this programme is ideal for

You have a quantitative undergraduate degree (economics, finance, mathematics, statistics, econometrics, or similar), or a math-heavy background from another field.
You’re excited about both finance and data science/coding — you enjoy working with numbers, data, and programming, and want a career at the intersection of finance and analytics.
You want a one-year full-time MSc that equips you not only with finance knowledge but also practical data-analysis and quantitative skills, preparing you for modern finance industry demands.

Progression & Future Opportunities

If you complete the MSc Finance with Data Science at UCL, you’ll be well‑positioned for roles like Quantitative Analyst, Risk Analyst, Portfolio/Investment Analyst, Credit or Credit‑Rating Analyst, Financial Engineer, or Data‑driven Finance Specialist in banks, hedge funds, asset‑management firms, fintech companies, or finance-driven consultancies.

Progression & Future Opportunities:

University services to support your employment: You get dedicated support via UCL’s careers team (career‑coaching, employer engagement), plus access to a global alumni network. There are regular networking events, corporate‑speaker series, and industry insight sessions — great for internships or job placements in finance or hub firms.
Strong curriculum & quantitative + data‑science training: The programme combines rigorous finance fundamentals (corporate finance, financial markets, econometrics) with advanced data‑science training (Python, big data analytics, time‑series analysis, forecasting, machine learning) — meaning you graduate with a rare skill set that merges financial acumen with data‑analysis capability.
Real‑world data exposure & technical fluency: You will work hands‑on with real financial data, learn data manipulation, modelling, data visualisation, and make data‑driven financial decisions — a big edge for employers in modern finance.
Flexibility in career paths & demand across sectors: Graduates are suited for roles in banking, asset management, hedge funds, fintech, consulting, and regulatory bodies — any place where finance + data skills are valued.
Global recognition & academic strength: The programme is offered jointly by UCL’s School of Management and Department of Economics — institutions highly ranked for research and teaching excellence, giving strong academic credibility and global recognition to your degree.

Further Academic Progression:
You can build on this MSc by going into a PhD or research‑oriented master’s in quantitative finance, financial econometrics, financial engineering, or data science; or specialize further by combining with financial analytics, risk management, fintech innovation, or computational finance — paving the way for senior research, quant‑finance, or data‑science‑driven leadership roles in finance.