BSc Hons Mathematics and Statistics

3 Years On Campus Bachelors Program

Queen Mary University of London

Program Overview

The Mathematics and Statistics BSc (Hons) at Queen Mary University of London delivers a rigorous grounding in both pure mathematics and statistical theory, while equipping students with skills to analyse real-world data and draw meaningful conclusions. It is well suited to students who enjoy logical, quantitative thinking and wish to apply mathematics and statistics to sectors such as finance, data science, research, or public-policy analysis.

Curriculum structure:

Year 1
In the first year, students build their core mathematical and statistical foundation. They study Programming in Python I, Numbers, Sets and Functions, Introduction to Analysis with Calculus, Probability & Statistics, Introduction to Algebra, and Vectors and Matrices. These modules introduce essential skills in analysis, algebra, probability, and coding — forming the bedrock for more advanced study.

Year 2
The second year advances into deeper mathematics and statistics. Students take compulsory modules including Linear Algebra I, Differential Equations, Probability and Statistics II, Programming in Python II, and Statistical Modelling I. They also choose from electives such as Actuarial Mathematics I, Complex Variables, Convergence and Continuity, Differential and Integral Analysis, Linear Optimisation and Game Theory, Number Theory, or Statistics for Insurance — allowing them to begin specialising according to their interests.

Year 3
In the final year, students deepen their statistical and applied mathematics knowledge and work on a substantial project. Core modules include Bayesian Statistical Methods and Statistical Modelling II. Elective choices allow for wide-ranging focus: options such as Actuarial Mathematics I, Introduction to Machine Learning, Random Processes, Partial Differential Equations, Financial Mathematics I/II, Time Series, Complex Networks, Numerical Computing with C and C++, or a Third Year Project give students the flexibility to tailor their degree toward data science, financial mathematics, theoretical statistics, or applied mathematics.

Focus areas:
Pure and applied mathematics, probability and statistics, statistical modelling, programming and computational statistics, mathematical analysis, data analysis, actuarial and financial mathematics, applied mathematics for real-world problems.

Learning outcomes:
Graduates will acquire strong mathematical reasoning, statistical and probabilistic understanding, ability to model and analyse data, competence in computational and statistical tools including programming, and flexibility to apply these skills in diverse contexts — from finance and data-driven industries to research and public policy.

Professional alignment (accreditation):
The programme is offered by the School of Mathematical Sciences at Queen Mary University of London. It is designed to meet academic and professional standards for mathematics and statistics, preparing graduates for a variety of quantitative, analytical, and data-oriented roles.

Reputation (employability & career prospects):
Graduates are in demand by employers in finance, banking, consulting, actuarial firms, data analytics, public sector, research organisations, and beyond. Alumni have gone on to work with leading institutions across banking, consulting, and technology.

Experiential Learning (Research, Projects, Internships etc.)

The BSc Mathematics and Statistics degree at Queen Mary offers a thorough grounding in both rigorous mathematics and statistical theory. It is ideal for students who enjoy logical reasoning, working with data, and wish to apply mathematical thinking to real-world problems. The programme combines pure and applied mathematics with extensive statistical and data analysis training, preparing graduates to handle complex quantitative tasks in a variety of industries.

Experiential Learning & Practical Exposure

Students benefit from a balance of academic theory and practical, data-driven skills:

Teaching and problem-solving through lectures, tutorials, and computer-laboratory sessions, enabling hands-on work with real data, statistical computing, and computational mathematics.
Programming in Python from early years, used in statistical computing, data analysis, and modelling tasks.
Advanced statistical and applied modules in later years, including statistical modelling, Bayesian methods, time-series, actuarial mathematics, random processes, financial mathematics, and machine learning (optional).
Access to modern teaching rooms, private and group study areas, and social-study spaces in the School of Mathematical Sciences building.
Collaborative learning through tutorials and exercise classes, promoting discussion, problem-solving, and communication skills.

Programme Structure & What Students Learn

Year 1: Fundamentals of mathematics and statistics, including Numbers, Sets and Functions; Introduction to Analysis with Calculus; Probability & Statistics; Vectors and Matrices; and introductory programming in Python.
Year 2: Linear Algebra, Differential Equations, Probability & Statistics II, Statistical Modelling I, and electives such as Actuarial Mathematics, Complex Variables, Optimization/Game Theory, Analysis, or Insurance Statistics.
Final Year: Advanced statistical and mathematical topics including Bayesian Statistical Methods, Statistical Modelling II, and optional modules such as Machine Learning, Time Series, Random Processes, Financial Mathematics, Numerical Computing, Partial Differential Equations, complex networks, or a supervised project.

Students can tailor their studies according to their interests, whether in pure mathematics, applied mathematics/statistics, data science, finance-oriented mathematics, actuarial science, or statistical modelling.

Why This Degree Gives a Strong Edge

Graduates combine analytical, mathematical, and statistical abilities with practical data-analysis and computational skills, highly sought after in finance, data science, actuarial work, research, consulting, and government sectors.
Exposure to programming and statistical computing tools provides an industry-relevant skill set.
Structured teaching, tutorials, lab-based learning, and optional modules give students flexibility to align the degree with future academic or professional goals.

Progression & Future Opportunities

Graduates of the Mathematics and Statistics BSc (Hons) at Queen Mary University of London (QMUL) are prepared for roles such as data analyst, statistician, actuarial analyst, business analyst, or quantitative consultant. The programme equips students with strong analytical, problem-solving, and statistical modelling skills, making them competitive for careers in finance, technology, insurance, consulting, and data-driven industries.

Why This Degree Opens Doors:

University‑supported employability services: The QMUL School of Mathematical Sciences provides careers support, including CV and application coaching, interview preparation, internships guidance, and networking opportunities.
Strong employment record & starting salary: A high proportion of graduates secure employment or further study within six months of graduation, with typical starting salaries around £30,000–£32,000.
Access to industry-leading employers: Graduates have opportunities with top firms in finance, insurance, consultancy, technology, and data science, including Deloitte, KPMG, PwC, EY, and J.P. Morgan.
Accredited, high‑quality academic training with long-term value: The programme integrates advanced mathematics and statistics, covering probability, regression, statistical modelling, and data analysis, providing a strong foundation for analytical and quantitative roles.
Diverse graduate outcomes: Graduates enter careers in data science, analytics, actuarial work, finance, risk management, business intelligence, and consulting.

Further Academic Progression:
After completing the BSc, graduates may pursue Master’s programmes or research degrees in statistics, data science, applied mathematics, financial mathematics, or operational research. Professional qualifications in actuarial science or data analytics are also viable, as are doctoral studies for those seeking advanced research opportunities.