BSc Hons Mathematics and Statistics with Professional Placement

4 Years On Campus Bachelors Program

Queen Mary University of London

Program Overview

The Mathematics and Statistics BSc (Hons) with Professional Placement at Queen Mary University of London combines rigorous mathematical and statistical training with a practical, paid work placement year. It is ideal for students who want strong analytical and quantitative skills and real-world experience before graduation, providing a head start for careers in statistics, data analysis, finance, research, public sector, or other quantitative fields.

Curriculum structure:

Year 1
The first year builds a strong foundation in mathematics and statistics, along with introductory computing and problem-solving skills. Students study Programming in Python I, Numbers, Sets and Functions, Introduction to Analysis with Calculus, Probability & Statistics, Introduction to Algebra, and Vectors and Matrices, ensuring solid grounding in mathematical concepts and statistical thinking while developing programming competence.

Year 2
The second year deepens mathematical and statistical understanding, including advanced algebra, analysis, statistical modelling, and applied mathematics. Students gain preparation for more specialized study or the upcoming placement year, balancing theoretical and practical skills to be ready for real-world applications.

Year 3 (Professional Placement Year)
During the third year, students undertake a paid professional placement in a relevant organisation, such as finance, business, data analysis, public sector, or research. This placement allows students to apply their mathematical and statistical knowledge in a real-world setting, gaining hands-on experience, workplace skills, and career insight. The university provides support for placement applications and preparation.

Year 4 (Final Academic Year)
In the final year, students complete advanced modules in mathematics and statistics. Options include Bayesian Statistical Methods, Statistical Modelling II, Random Processes, Financial Mathematics, Time Series, Numerical Computing with C and C++, Machine Learning / Neural Networks, and a substantial independent project. This allows specialisation in statistical analysis, data science, mathematical modelling, or applied statistics in finance, research, or public service.

Focus areas:
Mathematics, probability and statistics, statistical modelling, data analysis, programming, applied and theoretical statistics, mathematical modelling, computational statistics, practical industry experience.

Learning outcomes:
Graduates develop strong mathematical and statistical reasoning, programming and computational skills, and the ability to model and analyse data. They gain practical work experience, preparing them for careers in data analysis, finance, insurance, public sector analytics, research, or further postgraduate study in statistics, data science, or related fields.

Professional alignment (accreditation):
The programme is offered by the School of Mathematical Sciences and includes a structured professional placement, ensuring graduates meet both academic and practical standards for careers in data- and math-oriented roles.

Reputation (employability & career prospects):
Graduates are sought after in sectors including government statistical agencies, finance, consulting, pharmaceuticals, public health, and technology. Many find roles as statistical analysts, data scientists, actuarial trainees, or risk analysts, while others pursue postgraduate studies. The professional placement adds a significant advantage on their CV.

Experiential Learning (Research, Projects, Internships etc.)

The four-year BSc (Hons) in Mathematics and Statistics with Professional Placement combines a strong foundation in mathematical theory, probability, and statistics with a valuable year of real-world work experience. Students apply quantitative and analytical learning in business, finance, technology, or research environments, graduating with both knowledge and practical experience.

Experiential Learning & Practical Exposure

Students benefit from a mix of academic learning, computational practice, and industry exposure:

Builds statistical theory and methodology on a solid mathematical foundation, including probability theory, statistical inference, data analysis, and statistical computing.
Hands-on work with data analysis and statistical computing packages, including real datasets, regression, time-series, probabilistic models, and statistical modelling.
Full-year paid professional placement in industry, often in roles such as data analyst, business analyst, finance intern, statistical or risk-analysis assistant.
Structured support for placements, including guidance on applications, interviews, and mentorship, helping students smoothly transition from theory to practice.
Advanced courses and a final-year project or research work after the placement year, integrating academic learning with industry experience.

Programme Structure & What Students Learn

Years 1 & 2: Core mathematics (calculus, algebra, linear algebra, vectors/matrices, analysis), probability and statistics fundamentals, introduction to statistics and probability theory.
Year 3 (placement year): Full-time, paid industry placement applying mathematical and statistical skills to real-world problems.
Year 4 (final year): Advanced mathematics and statistical modules, optional electives such as time-series analysis, advanced probability, data science/statistical modelling, and a major project or dissertation.

Why This Degree Provides a Strong Edge

Graduates gain both rigorous mathematical/statistical training and real-world industry experience, appealing to employers in finance, consulting, data analysis, insurance, government, research, and technology.
The placement year converts theoretical learning into practical understanding, building workplace skills and a strong CV.
Flexible specialization options allow students to focus on statistics, data science, applied mathematics, risk, and more, aligning with diverse career paths.

Progression & Future Opportunities

Graduates of the Mathematics and Statistics with Professional Placement BSc (Hons) at Queen Mary University of London (QMUL) are prepared for careers such as data analyst, statistician, actuarial analyst, business analyst, or risk analyst. The professional placement equips students with practical experience, enhancing employability and providing a strong foundation for careers in finance, insurance, consulting, technology, and analytics-driven industries.

Why This Degree Opens Doors:

University‑supported employability services: The QMUL School of Mathematical Sciences offers dedicated careers support, including placement guidance, CV and application coaching, interview preparation, and networking events.
Strong employment record & starting salary: A significant proportion of graduates secure employment or further study within six months of graduation, with typical starting salaries around £30,000–£32,000, reflecting the value of placement experience.
Access to industry-leading employers: Graduates gain opportunities with top firms such as Deloitte, KPMG, PwC, EY, J.P. Morgan, and major insurance and financial services companies.
Accredited, high‑quality academic training with long-term value: The programme integrates advanced mathematics with statistics, including modules in probability, regression, and data modelling, preparing students for analytical and quantitative roles.
Professional placement experience: The placement provides real-world application of skills, practical problem-solving experience, and professional networking opportunities before graduation.
Diverse graduate outcomes: Graduates are equipped for roles in data science, analytics, actuarial work, finance, risk management, business intelligence, and consultancy.

Further Academic Progression:
After completing the BSc, graduates may pursue Master’s degrees or research programmes in statistics, data science, applied mathematics, financial mathematics, or operational research. They may also undertake professional qualifications such as actuarial certifications (e.g., IFoA), data analytics credentials, or progress to doctoral studies for advanced research opportunities.