Mathematics with Data Science BSc (Hons)

3 Years On Campus Bachelors Program

St Georges University of London

Program Overview

The BSc Mathematics with Data Science blends rigorous mathematical theory with practical data science skills, giving you the tools to understand, analyse, and interpret real-world data. It’s ideal for students who enjoy mathematics and want to apply it to fast-growing fields like AI, machine learning, finance, business analytics, and technology.

Curriculum Structure

Year 1 – Core Mathematical and Computational Foundations

Your first year focuses on essential mathematics, including algebra, calculus, vectors, probability, and introductory statistics. Alongside this, you build core computational thinking and introductory programming skills that form the base of your later data science training. This year ensures all students develop strong analytical and problem-solving foundations.

Year 2 – Developing Rigorous Mathematical and Analytical Tools

As you progress, you study more advanced topics such as real and complex analysis, vector calculus, sequences and series, and applied mathematics. These are complemented by computational techniques that support data-driven modelling. This year strengthens your theoretical background while preparing you for specialised data science work.

Year 3 (Final Year) – Data Science Specialisation & Project Work

In the final year, you dive into specialist modules such as Introduction to Data Science, Techniques for Data Science, Machine Learning, and Principles of Artificial Intelligence. You may also explore advanced mathematics options like Differential Equations, Discrete Mathematics (including Graph Theory), Probability, or Operational Research. You complete a major project — either group or individual — applying mathematical and data science methods to a real-world problem.

Placement Year (Optional – Makes It a 4-Year Degree)

If you choose the placement route, you’ll spend a full year working in industry, typically between your second and third years. Students often work in fields such as software development, data analytics, finance, or technology consulting. This year gives you practical experience, professional networks, and enhanced employability.

Focus Areas

Mathematics • Data Science • Machine Learning • Artificial Intelligence • Statistical Modelling • Coding Theory • Graph Theory • Numerical and Computational Methods • Real-world Data Applications

Learning Outcomes

You will graduate with strong mathematical reasoning, analytical and computational abilities, and hands-on experience in data science methods. You’ll be able to handle large datasets, build machine learning models, solve complex quantitative problems, and communicate data-driven insights effectively. The degree prepares you for careers across technology, finance, research, business analytics, and AI-related roles.

Professional Alignment & Reputation

The programme is designed to meet modern industry demands by combining mathematical depth with practical data science and programming. With its strong emphasis on applied projects, analytical rigour, and optional industry placement, the degree positions graduates competitively for roles in data science, fintech, analytics, and research sectors.

Experiential Learning (Research, Projects, Internships etc.)

The Mathematics with Data Science BSc (Hons) at St George’s University of London is designed for students who want to build a deep mathematical foundation while also gaining the modern data science skills that drive innovation across industries today.
The course blends pure and applied mathematics, statistical theory, computing, and data-driven analysis, preparing students to understand not only how data works but why the underlying mathematical principles matter.

Students learn to work with real datasets, develop computational approaches to solve complex problems, and apply mathematical reasoning to challenges in healthcare, finance, technology, science, and public policy.

What You Will Study – In Detail

1. Core Mathematics

You will study essential mathematical concepts that form the backbone of advanced data science, including:

Calculus (differentiation, integration, multivariable calculus)
Linear algebra (vectors, matrices, transformations)
Advanced algebra and number theory
Mathematical modelling for real-world systems
Discrete mathematics, logic, and algorithmic thinking

These modules develop your analytical thinking and provide the tools needed for high-level data interpretation.

2. Probability & Statistical Theory

A strong grounding in statistics is central to the curriculum. You will learn:

Probability theory and statistical distributions
Inference, hypothesis testing, and confidence intervals
Regression analysis and optimisation
Time-series analysis and experimental design

This helps you build the statistical reasoning needed for data-driven decision-making.

3. Data Science & Programming Skills

Practical computing and programming play a major role, with hands-on training in:

Data cleaning and manipulation
Data visualisation techniques
Programming languages such as Python and R
Working with large datasets
Database fundamentals
Computational mathematics

You develop the technical confidence to work with modern data tools used across industries.

4. Machine Learning & Statistical Learning

Advanced modules help you understand how predictive models are built:

Supervised and unsupervised learning
Classification and clustering algorithms
Model evaluation and validation
Neural networks fundamentals
Mathematical foundations behind machine learning

This prepares you to apply machine learning in practical and research environments.

5. Research & Professional Skills

Throughout the course, you develop:

Problem-solving and critical thinking abilities
Report writing and technical communication skills
Experience analysing real-world, complex datasets
Project work to simulate industry or research settings

The degree often culminates in a research project, allowing you to explore a data-driven topic in depth.

Progression & Future Opportunities

Graduates from a Mathematics with Data Science BSc would be equipped to enter roles such as Data Scientist, Machine Learning Engineer, Statistical Analyst, Business Intelligence Analyst, or Quantitative Modeller. With strong foundations in mathematics, analytics, and computational methods, students would be prepared for data-driven careers across technology, healthcare analytics, finance, consulting, and research.

What the Degree Would Offer (Skills, Curriculum & Employability Advantages)

Deep mathematical training — Covering calculus, linear algebra, probability, statistics, optimisation, and mathematical modelling to build strong analytical foundations.
Specialised data science content — Including machine learning principles, data analytics, statistical inference, algorithmic foundations, data visualisation, database systems, and programming (likely Python, R, or similar).
Applied learning linked to health and biomedical contexts — Since St George’s specialises in medical and health sciences, the programme could uniquely integrate real datasets from healthcare, epidemiology, public health analysis, or biomedical research.
Cross-disciplinary expertise — Combining mathematics with computational and analytical techniques, preparing students for roles requiring both theoretical insight and practical data handling skills.
Professional skill development — Training in problem-solving, coding, statistical reasoning, data interpretation, and communication to prepare students for fast-growing sectors that rely on data-led decision-making.
Career support and employability — Students would benefit from career guidance, interview support, CV workshops, and access to partnerships with healthcare organisations, research institutions, and analytics-driven companies.

Further Academic Progression

After completing this degree, graduates could:

Pursue postgraduate study in Data Science, Artificial Intelligence, Applied Mathematics, Statistics, Machine Learning, Bioinformatics, Health Data Analytics, or Computational Science.
Progress into research-oriented pathways through MSc programmes, followed by a PhD in data science, mathematical modelling, AI, or quantitative research fields.
Enter graduate training schemes in data analytics, technology, finance, healthcare data, or consultancy.
Work toward professional certifications in data science, cloud technologies, machine learning engineering, or advanced analytics.