Mathematics for Data Science BSc
Brunel University London
UCAS Code: G1ND | Bachelor of Science (with Honours) - BSc (Hons)
Entry requirements
A level
including A in Mathematics or Further Mathematics
Obtain a minimum of 120 UCAS tariff points in an Access to HE Diploma with 45 credits at Level 3 and grade A in A level Mathematics or Further Mathematics
Cambridge International Pre-U Certificate - Principal
including D3 in Mathematics or Further Mathematics
GCSE/National 4/National 5
A minimum of 5 GCSEs at grade C or grade 4 and above are required, including English Language (or grade B/5 in English Literature) and Mathematics
International Baccalaureate Diploma Programme
including grade 6 in Mathematics at Higher Level (Analysis and Approaches or Applications and Interpretation)
Leaving Certificate - Higher Level (Ireland) (first awarded in 2017)
including H2 in Mathematics or Further Mathematics
OCR Cambridge Technical Diploma
in any subject and an A level at grade A in Mathematics or Further Mathematics
OCR Cambridge Technical Extended Certificate
in any subject with A levels grade AC including a A in Mathematics or Further Mathematics
OCR Cambridge Technical Extended Diploma
in any subject with an A level at grade A in Mathematics or Further Mathematics
Pearson BTEC Diploma (QCF)
in any subject with grade A in A level Mathematics or Further Mathematics
Pearson BTEC Extended Diploma (QCF)
in any subject with grade A in A level Mathematics or Further Mathematics
Pearson BTEC Level 3 National Diploma (first teaching from September 2016)
in any subject and an A level at grade A in Mathematics or Further Mathematics
Pearson BTEC Level 3 National Extended Certificate (first teaching from September 2016)
in any subject with A levels grade AC including a A in Mathematics or Further Mathematics
Pearson BTEC Level 3 National Extended Diploma (first teaching from September 2016)
in any subject with an A level at grade A in Mathematics or Further Mathematics
Pearson BTEC Subsidiary Diploma (QCF)
in any subject with A level grades AC including A in Mathematics or Further Mathematics
Scottish Advanced Higher
including A in Mathematics or Further Mathematics
T Level
and grade A in A level Maths
UCAS Tariff
We've calculated how many Ucas points you'll need for this course.
About this course
Data analytics (Big Data) is a major phenomenon in the 21st century, there is an increasing demand for data analysts trained in this area who can collate, interpret and draw value from complex data sets.
This programme brings together a range of techniques that modern data analyst needs. You will study blocks in mathematics, statistics, data analysis and computing, and tackle a variety of interesting and engaging problems from business and industry. A good grounding in all these subjects is essential for creating and using algorithms and systems that identify patterns and extract value from masses of data.
The course will also develop key graduate skills such as problem-solving and communication, with a third of the credits at each level based on project-oriented work where students will develop their knowledge, professionalism and creativity in a supportive environment.
As an example, in your second year, you will be introduced to neural networks and deep learning. This important topic is at heart a powerful blend of linear algebra, nonlinear activation functions, vector calculus chain rule for gradients, and steepest descent optimisation with sampling. These fundamental building blocks will be brought together in theory and in software so that you will be able to build your own deep learning neural net, and be able to explain the function of every part of the algorithm. This last aspect of being able to explain the software’s function is key to the role of a mathematician as an understander as well as a user of methods, as opposed to just a consumer of software. The emphasis throughout will be on the practical rigour associated with getting deep learning to work.
Follow the four-year ‘Professional Placement’ degree programme and you‘ll benefit from our extensive experience in helping students to find well-paid work placements with blue-chip companies. Our sandwich students find that their mathematical and transferable skills are in demand in many sectors.
Modules
Typical Modules
Algorithms and their Applications
Calculus
Statistical Programming for Data Analytics
Decision Making in the Face of Risk
Stochastic Models
Scientific Computing
For a full list of modules please visit our website https://www.brunel.ac.uk/study/undergraduate/mathematics-for-data-science-bsc
Assessment methods
The Mathematics for Data Science BSc programme uses elements of formative and summative assessment. Although both forms of assessment will be graded, only the summative assessment will count for progression for your final degree.
Our academics use formative assessment as a fundamental component in the learning process, including; class tests (both in paper and electronic format), electronic quizzes, and short written exercises. Summative assessments throughout this course consist of coursework and examinations.
We base your final degree class on your performance in second and final year. Final year carries twice the weight of second year.
Tuition fees
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What students say
How do students rate their degree experience?
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Who studies this subject and how do they get on?
Most popular A-Levels studied (and grade achieved)
After graduation
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What are graduates doing after six months?
This is what graduates told us they were doing (and earning), shortly after completing their course. We've crunched the numbers to show you if these immediate prospects are high, medium or low, compared to those studying this subject/s at other universities.
Top job areas of graduates
The business and research sectors worry that the UK hasn't got enough people with good statistics skills, and as stats are at the heart of so much of the economy, and we only have a few hundred graduates a year in the discipline, this type of degree can be very useful and versatile. The finance industry is very popular with this group, and they're far more likely to be working in London than most other graduates. And who can blame them — statistics graduates starting work in London were earning an average of nearly £29k just six months after leaving university. There is also demand from the Scottish finance sector in Edinburgh and Glasgow - particularly in banking and insurance. But a good statistician can find work almost anywhere that data can be analysed - which, in an online world, is almost anywhere - and many industries struggle to find enough statisticians to fulfil demand, so stay flexible and you can find a variety of options.
What about your long term prospects?
Looking further ahead, below is a rough guide for what graduates went on to earn.
Statistics
The graph shows median earnings of graduates who achieved a degree in this subject area one, three and five years after graduating from here.
£25k
£29k
£37k
Note: this data only looks at employees (and not those who are self-employed or also studying) and covers a broad sample of graduates and the various paths they've taken, which might not always be a direct result of their degree.
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Post-six month graduation stats:
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It offers a snapshot of what grads went on to do six months later, what they were earning on average, and whether they felt their degree helped them obtain a 'graduate role'. We calculate a mean rating to indicate if this is high, medium or low compared to other universities.
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Graduate field commentary:
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The Longitudinal Educational Outcomes dataset combines HRMC earnings data with student records from the Higher Education Statistics Agency.
While there are lots of factors at play when it comes to your future earnings, use this as a rough timeline of what graduates in this subject area were earning on average one, three and five years later. Can you see a steady increase in salary, or did grads need some experience under their belt before seeing a nice bump up in their pay packet?
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