BSc (hons) mathematics with data science

3 Years On Campus Bachelors Program

London School of Economics and Political Science

Program Overview

The BSc Mathematics with Data Science combines advanced mathematical training with practical data science, programming, and statistical modelling — perfect for students who love mathematics but also want hands-on skills in analysing data and solving real-world problems. It’s an excellent fit for careers in data analytics, finance, technology, consulting, research, or any role that relies on quantitative and data-driven decision-making.

Curriculum Structure

Year 1

Your first year introduces the foundations of both mathematics and data science. You study core modules such as Mathematical Methods, Introduction to Abstract Mathematics, and Elementary Statistical Theory. Alongside this, you begin developing practical skills through Programming with Data Science, where you learn the fundamentals of coding, data structures, and computational thinking. You also take LSE100, a skills-based course that strengthens your critical thinking and interdisciplinary analysis.

Year 2

In the second year, you advance into deeper mathematical and statistical analysis, building strong theoretical foundations for data-driven work. You study subjects such as Statistical Inference, Probability, and Regression Analysis, which develop your understanding of statistical modelling and real-world data interpretation. You also continue expanding your computational and data science skills through modules that involve applied data analysis, algorithmic thinking, and early machine-learning concepts.

Year 3 (Final Year / Honours)

Your final year offers the flexibility to tailor your degree to your career goals. You can choose advanced electives from mathematics, statistics, data science, finance, or computing, allowing you to specialise in areas like machine learning, financial mathematics, or statistical modelling. This year strengthens your ability to work with complex datasets, build predictive models, and apply mathematical reasoning to large-scale analytical challenges.

Focus Areas

Mathematical methods, probability and statistics, data science, statistical modelling, programming, computational methods, data analysis, machine learning fundamentals, quantitative business applications.

Learning Outcomes

Graduates develop strong theoretical and applied skills in mathematics and statistics alongside practical programming and data analysis abilities. You will learn to build models, interpret data, design algorithms, and apply mathematical reasoning to real-world scenarios. The degree also builds critical thinking, analytical decision-making, and communication skills essential for quantitative professions.

Professional Alignment (Accreditation / Career Relevance)

This degree supports entry into data-driven and quantitative careers such as data science, finance, consulting, banking, actuarial analysis, technology, and analytics. Its structure provides strong alignment with industry needs by blending mathematical depth with practical computational and data-science skills. It also forms a strong foundation for postgraduate study in data science, machine learning, statistics, or mathematics.

Reputation (Employability & Outcomes)

Graduates from this programme are highly valued for their combination of mathematical expertise and applied data science capability. They frequently secure roles across financial services, technology companies, consulting firms, data-driven organisations, and research institutions. The programme’s quantitative intensity and practical orientation contribute to consistently strong employability outcomes.

Experiential Learning (Research, Projects, Internships etc.)

The BSc Mathematics with Data Science at LSE is built for students who want rigorous mathematical training combined with modern data-science skills. From your first year, you’ll work with mathematical methods, probability, statistics, programming, and the foundations of data analysis. As you progress, the programme gradually introduces advanced data science concepts including algorithms, data structures, and computational techniques — all taught through an environment shaped by LSE’s active research culture in discrete mathematics, optimisation, and applied mathematics.

You’ll develop the ability to use mathematics to interpret real datasets, understand algorithms that power modern technology, and apply quantitative reasoning to economic, financial, scientific, and social challenges. Experiential learning is woven into the structure of the programme, helping you move from theory to practical problem-solving.
Here’s how your practical exposure takes shape:

🎓 Experiential Learning — Tools, Methods, and Learning-by-Doing

Programming for Data Science Training: In your first year, you take a dedicated programming course focused specifically on data science applications, allowing you to build computational skills from the ground up.
Strong Maths–Statistics–Computing Integration: Mathematical Methods, Abstract Mathematics, and Elementary Statistical Theory form your foundation, ensuring you can transition confidently into algorithmic and data-rich modules later.
Algorithms and Data Structures: As you advance, you study algorithmic processes, data structures, discrete mathematics, and computational methods — essential building blocks for machine learning and modern analytics.
Exposure to Real Applications: Courses are deliberately designed to show how mathematics and data science drive decisions in finance, economics, business, operations, and social sciences, reflecting the broader strengths of LSE.
Research-Informed Teaching: Since the department is active in fields such as discrete mathematics, optimisation, operations research, and financial mathematics, the programme is shaped by current academic research and industry-relevant modelling.
Interdisciplinary Academic Environment: You benefit from LSE’s unique environment where mathematics connects with economics, management, finance, policy, and the social sciences — a major advantage for students targeting data-driven careers.
Career-Focused Skills Development: The training in mathematics, statistics, programming, and algorithms is precisely the blend sought after in data analysis, quantitative finance, business intelligence, consulting, and tech roles.

🎯 Why Students Choose This Program

You gain both deep mathematical rigour and hands-on data science skills — a rare combination.
The degree prepares you exceptionally well for roles in quantitative fields such as data science, analytics, finance, and research.
You learn in an environment where mathematical research and real-world applications meet.
The structure ensures a smooth progression from mathematical theory to advanced computational reasoning.
Employers worldwide value LSE’s academic reputation and quantitative training.

Progression & Future Opportunities

The BSc Mathematics with Data Science equips students with strong mathematical foundations combined with modern data-science skills, including programming, machine learning, statistical modelling, and analytical reasoning. This blend prepares graduates for high-demand roles where mathematical accuracy meets data-driven decision-making, particularly in technology, finance, research, and analytics sectors.

Typical career roles include:

Data Scientist / Machine Learning Engineer
Data Analyst / Business Intelligence Analyst
Quantitative Analyst / Risk Modeller
Statistical Analyst / Research Data Specialist

University support & employability features:

Careers & Employability Service: guidance, CV development, interview preparation, and networking tailored to quantitative and computing roles.
Technical skills workshops: support in programming, statistical computing, and advanced data-science tools to boost employability.
Industry-linked projects and hackathons: opportunities to apply mathematics and data science to real-world problems, often in collaboration with employers.
Optional internships or placements: practical experience in data-driven environments such as finance, tech, analytics, or research labs.

Employment statistics & salary outcomes:

Graduates in mathematics and data-driven disciplines typically secure employment or further study within 6–15 months.
Starting salaries generally fall between £28,000–£35,000, depending on sector, with significant growth potential in tech and finance.
Data-focused roles continue to expand globally, creating strong, long-term employment prospects.

Industry relevance & long-term value:

This degree reflects industry demand for professionals who can combine mathematical rigour with data-science expertise.
Training includes statistical computing, machine learning, modelling, and programming — core skills for modern analytics and AI roles.
Graduates gain a flexible skill set valuable across finance, consulting, technology, healthcare analytics, government, and research sectors.

Graduation outcomes:
Graduates finish with strong analytical, computational, and statistical abilities, ready for careers in data science, analytics, finance, research, or advanced technical roles across global industries.

Further Academic Progression:

After completing this programme, students may pursue:

Master’s degrees in Data Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Business Analytics, Financial Mathematics, or Statistics.
Research degrees (MSc/PhD) in Data Science, Statistical Modelling, Computational Mathematics, AI, or related quantitative fields.
Professional careers in technology, finance, consulting, data analytics, research, or business intelligence, leveraging strong mathematical and data-science training.