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BSc Mathematics and Data Science
About this course
Mathematics and data science is a combination that addresses one of the most significant intellectual and professional demands of contemporary life: the ability to reason rigorously about structure and pattern while also extracting meaningful understanding from the enormous quantities of data that organisations now generate and collect. Mathematics provides the formal foundations, statistical theory, linear algebra, calculus, probability and discrete mathematics, that give data science its analytical depth. Data science adds the computational methods, machine learning, data visualisation, programming and applied statistical modelling, that make those foundations useful in the real world. At Birkbeck College, University of London, this part-time programme is designed specifically for students who need to study alongside work or other commitments. Birkbeck has a long tradition of making rigorous degree-level education accessible to mature students and working professionals, and the evening teaching model allows you to pursue a mathematically demanding degree without putting your career or other responsibilities on hold. You will develop a solid command of the mathematical theory that underlies data science alongside practical skills in working with data, including programming, statistical analysis and the tools used by data professionals in industry and research. Graduates from mathematics and data science programmes are well placed for careers in data science, machine learning engineering, quantitative analysis, actuarial science, financial modelling, research and software development. The combination of mathematical rigour and applied data skills is particularly valued by employers who need graduates who can not only use data tools but understand what they are doing and why. The flexibility of the part-time mode means many graduates continue in their existing careers while building new skills, enabling direct application and career progression during the degree itself. Further study in machine learning, statistics, data engineering or related fields is a natural next step.
Syllabus & Modules
Typical curriculumStudent Satisfaction
National Student Survey - 15 respondents (87% response rate)
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