Mathematics with Statistics BSc Hons

3 Years On Campus Bachelors Program

Lancaster University

Program Overview

Mathematics with Statistics BSc (Hons) at Lancaster combines rigorous mathematics with practical statistical and data‑analysis skills, making it ideal for students who want to apply mathematics to real-world problems. It’s perfect for those interested in fields like data science, finance, epidemiology, AI, environmental science, or any role that requires strong analytical and statistical thinking.

Curriculum Structure

First Year

In Year 1, students build a solid foundation in both mathematics and statistics through core modules such as Matrices and Calculus, Logic and Discrete Mathematics, Probability and Statistics, and modules on sequences and symmetry or discrete analysis. Optional modules provide flexibility, allowing exploration of introductory computing, programming, or complementary subjects to broaden your skill set.

Second Year

Year 2 deepens mathematical and statistical knowledge with modules such as Linear Algebra, Real Analysis, Multivariate Probability and Statistics, and a Project Skills module. These develop advanced analytical abilities, research skills, and statistical computing techniques, while optional modules allow specialization in applied or theoretical areas.

Third Year

The final year allows students to specialize further, combining core and optional modules. Core statistics modules may include Statistical Inference or supervised learning concepts, while optional modules can include Stochastic Processes, Time Series, Environmental or Medical Statistics, Graph Theory and Algorithms, Cryptography, or advanced mathematical/statistical topics. This structure enables tailoring the degree toward careers in data science, finance, research, modelling, or pure mathematics/statistics.

Focus Areas

Applied and theoretical mathematics, probability and statistics, statistical inference, stochastic processes, mathematical modelling, data analysis, computational statistics.

Learning Outcomes

Graduates develop strong skills in mathematical reasoning, statistical modelling, data analysis, probability, stochastic methods, and computing for data handling. They can analyse real-world datasets, interpret results, build models, and communicate findings effectively, while also possessing solid theoretical mathematical foundations.

Professional Alignment (Accreditation)

The degree is accredited by the Institute of Mathematics and its Applications (IMA). The statistics component allows graduates to apply for professional recognition from the Royal Statistical Society (RSS), demonstrating competence and professionalism in statistics and data-related careers.

Reputation (Employability & Rankings)

Lancaster’s Mathematics programmes are highly respected and ranked among the top in the UK, reflecting both teaching quality and academic strength. Graduates are sought after in sectors such as data science, finance, analytics, research, AI, environmental modelling, and healthcare analytics — benefiting from both theoretical and practical training.

Experiential Learning (Research, Projects, Internships etc.)

This programme combines rigorous mathematics with applied statistics, preparing you for both academic and professional pathways. From Year 1, you build a strong foundation in core mathematics and statistical concepts. As you progress, you gain practical experience through project work, statistical computing, and research-style assignments. By your final year, you can specialise in advanced statistical and mathematical modules that align with your career ambitions, such as data science, finance, medical statistics, or research.

Transitioning to the specific experiential learning opportunities offered:

• Strong foundational coursework

Year 1 introduces core topics: calculus, discrete mathematics, probability, statistics, logic, and proofs.
Year 2 advances to analysis, linear algebra, multivariate probability, and applied statistics, providing a robust mathematical and statistical grounding.

• Projects and applied statistical work

The “Project Skills” module includes individual and group projects where you learn scientific writing, statistical reporting, and use professional tools such as R and LaTeX.
Collaborative work helps develop teamwork, communication, presentation, and problem-solving skills applicable in research and industry.

• Specialist modules and electives

In the final year, students choose from optional modules such as medical statistics, environmental statistics, stochastic processes, mathematical finance, time series analysis, or machine learning.
This flexibility allows you to tailor your degree toward data science, analytics, finance, health-data analysis, or pure mathematics.

• Professional recognition

Completion of relevant modules allows eligibility for accreditation by the Royal Statistical Society and recognition by the Institute of Mathematics and its Applications, confirming high professional standards.

• Career-ready skills

Students graduate with expertise in data analysis, statistical modelling, quantitative reasoning, and computing.
Career support helps prepare for roles in finance, data analysis, analytics, research, healthcare statistics, environmental modelling, and more.

• Supportive academic community

Weekly workshops, problem-solving classes, and one-to-one academic support ensure smooth transition from school to university-level mathematics and statistics.
Peer learning communities provide informal discussion spaces for collaboration, deeper engagement, and skill development outside formal lectures.

Who this course suits

Students interested in both pure mathematics and applied statistics, looking to use mathematics for real-world problem solving.
Those aiming for careers in data science, analytics, finance, research, or any sector requiring statistical modelling and quantitative analysis.
Learners who value flexibility and the ability to specialise in advanced modules aligned to their career goals.
Students seeking a degree with strong academic credentials and professional recognition, opening pathways to employment or further study.
Individuals ready to engage with rigorous mathematics while developing proficiency in statistical tools and research methods.

Progression & Future Opportunities

Graduates from Lancaster’s Mathematics with Statistics BSc combine strong mathematical foundations with advanced statistical and data analysis skills. Many secure roles in finance, data science, actuarial work, business analytics, research, or technology. The program’s focus on practical data and analytical skills ensures that a large proportion achieve highly skilled employment within 15 months of graduation.

Typical job roles include:

Data Analyst or Statistical Analyst
Quantitative or Actuarial Analyst
Business or Financial Analyst
Research Analyst or Data Scientist

How Lancaster supports your career success:

Comprehensive Curriculum: The program covers core mathematics (calculus, algebra, probability, logic, discrete mathematics) and advanced statistics (statistical inference, multivariate statistics, stochastic processes, applied data science, medical and environmental statistics), preparing students for data-focused careers.
Flexible Optional Modules: In later years, students can tailor their degree with modules such as data science, financial mathematics, stochastic modelling, and mathematical modelling to match career goals or interests.
Professional Accreditation: Completion of the required statistics modules makes graduates eligible for accreditation by the Royal Statistical Society (RSS) and Institute of Mathematics and its Applications (IMA), enhancing professional credibility.
Employability and Transferable Skills: Through coursework, project-based modules, and training in statistical software, students develop data analysis, problem-solving, programming, data visualization, and mathematical reasoning skills — highly valued in multiple sectors.
Career Flexibility: Graduates are prepared for roles in finance, insurance, data science, machine learning, healthcare analytics, government and public policy, consultancy, and research, offering broad employment options.

Further Academic Progression:

After completing this BSc, students have multiple academic pathways:

Pursue a Master’s (MSc) in data science, statistics, financial mathematics, actuarial science, applied mathematics, machine learning, or computational modelling.
Undertake a PhD in statistics, applied mathematics, data science, or mathematical modelling for research or academic careers.
Pursue specialised professional qualifications or certifications for roles in actuarial work, data analytics, or risk analysis.