Mathematics with Data Science
City, University of London
UCAS Code: G120 | Bachelor of Science (with Honours) - BSc (Hons)
Entry requirements
A level
AAB (including Mathematics or Further Mathematics)
Yes on a case-by-case basis
GCSE/National 4/National 5
Grade 4 in GCSE Mathematics and English Language
International Baccalaureate Diploma Programme
33 points total, including Higher Level Mathematics at grade 6 and minimum of grade 5 in Standard Level English.
Pearson BTEC Level 3 National Diploma (first teaching from September 2016)
DD with an A-level grade A in Mathematics or Further Mathematics
UCAS Tariff
AAB (including Mathematics or Further Mathematics)
About this course
This degree combines mathematics, teaching you sought-after skills for real-world problem solving, with data science where it focuses on practical and theoretical aspects of techniques and approaches for extracting insights from large collections of data, opening doors to possible careers in a wide variety of industries such as business, retail and finance.
Whether you are managing an investment portfolio, encrypting financial transactions or constructing a machine-learning algorithm to identify hidden patterns in your customer database you will likely be doing it through a combination of Mathematics and Data Science methods.
The Mathematics with Data Science BSc opens doors to the widest range of careers, because virtually all industries need graduates with skills in these disciplines. The confidence and knowledge you gain at City will open doors to a rewarding and satisfying career.
- Understand the universal nature of mathematics as a discipline that knows no borders or language barriers
- Master a wide range of mathematical and data science topics and techniques, such as calculus, probability and machine learning
- Learn to implement sophisticated algorithms, data mining techniques artificial intelligence with real world applications
- Boost your employability with an optional paid one-year work placement – past students have secured placements at organisations such as Axa, Barclays, Bloomberg, Disney, Microsoft, Toyota and Warner Music
- Take special career development modules to understand mathematics’ essential role across all industries and the opportunities available to you.
Explore your interests through a research project chosen from a wide variety of mathematical topics – past projects have included everything from life-saving mathematics in medical imaging, to wallpaper patterns.
Modules
This three-year BSc Mathematics with Data Science degree is focused on pure mathematics with real applications. As you progress, you will have increasing choice and flexibility about what you choose to study.
Year 1 consists of modules that make up 125 credits. All modules are core.
-Functions, Vectors and Calculus (30 credits)
-Algebra (15 credits)
-Linear Algebra (15 credits)
-Introduction to Probability and Statistics (15 credits)
-Logic and Set Theory (15 credits)
-Number Theory and Cryptography (15 credits)
-Introduction to Modelling (15 credits)
-Skills, Careers and Employability Analysis for Mathematics students (5 credits)
Year 2 consists of modules that make up 125 credits.
-Programming and Data Science for the Professions (15 credits)
-Real and Complex Analysis (30 credits)
-Vector Calculus (15 credits)
-Sequences and Series (15 credits)
-Decision Analysis (15 credits)
-Applied Mathematics (15 credits)
-Numerical Mathematics (15 credits)
-Professional Development and Employability (5 credits)
-Applications of Probability and Statistics (15 credits)
Year 3 consists of modules that make up 120 credits.
-Codes (15 credits)
-Techniques for Data Science (15 credits)
-Group project (15 credits)
-Principles of Data Science (15 credits)
Introduction to Artificial Intelligence (15 credits)
-Machine Learning (15 credits)
-Differential Equations (30 credits)
-Advanced Complex Analysis (15 credits)
-Stochastic Models (15 credits)
-Operational Research (15 credits)
-Probability and Statistics 2 (30 credits)
-Graph Theory (15 credits)
-Game Theory (15 credits)
-Dynamical Systems (15 credits)
-Introduction to the Mathematics of Fluids (15 credits)
-Introduction to Mathematical Physics
-Mathematical Processes for Finance (15 credits)
-Groups and Symmetry (15 credits)
-Mathematical Biology (15 credits)
Assessment methods
Assessment is based on examination and coursework. Marks are weighted in a 1:3:6 ratio for the three years of study to produce an overall aggregate.
Types of assessment
- Set exercises or coursework, which you take home and complete with the aid of your notes
- Formal unseen written examinations every year
- Class or online tests
- Group assessments, such as written reports, also form the basis of assessment for some modules.
- Also, a small number of modules require students to give presentations.
Feedback on assessment
You will normally be provided with feedback within three weeks of the submission deadline or assessment date. This would normally include a provisional grade or mark. For end-of-module examinations or an equivalent significant task (e.g. an end of module project), feedback will normally be provided within four weeks.
The timescale for feedback on final-year projects or dissertations may be longer.
Tuition fees
Select where you currently live to see what you'll pay:
The Uni
City, University of London
Department of Mathematics
What students say
We've crunched the numbers to see if overall student satisfaction here is high, medium or low compared to students studying this subject(s) at other universities.
How do students rate their degree experience?
The stats below relate to the general subject area/s at this university, not this specific course. We show this where there isn’t enough data about the course, or where this is the most detailed info available to us.
Computer science
Teaching and learning
Assessment and feedback
Resources and organisation
Student voice
Who studies this subject and how do they get on?
Most popular A-Levels studied (and grade achieved)
Mathematics
Teaching and learning
Assessment and feedback
Resources and organisation
Student voice
Who studies this subject and how do they get on?
Most popular A-Levels studied (and grade achieved)
After graduation
The stats in this section relate to the general subject area/s at this university – not this specific course. We show this where there isn't enough data about the course, or where this is the most detailed info available to us.
Computer science
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
This is a newly-classified subject area for this kind of data, so we don’t currently have very much information to display or analyse yet. The subject is linked to important and growing computing industries, and over time we can expect more students to study them — there could be opportunities that open up for graduates in these subjects as the economy develops over the next few years.
Mathematics
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
Want to feel needed? This is one of the most flexible degrees of all and with so much of modern work being based on data, there are options everywhere for maths graduates. With all that training in handling figures, it's hardly surprising that a lot of maths graduates go into well-paid jobs in the IT or finance industries, and last year, a maths graduate in London could expect a very respectable average starting salary of £27k. And we're always short of teachers in maths, so that is an excellent option for anyone wanting to help the next generation. And if you want a research job, you'll want a doctorate — and a really good maths doctorate will get you all sorts of interest from academia and finance — and might secure some of the highest salaries going for new leavers from university.
What about your long term prospects?
Looking further ahead, below is a rough guide for what graduates went on to earn.
Computer science
The graph shows median earnings of graduates who achieved a degree in this subject area one, three and five years after graduating from here.
£29k
£35k
£42k
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.
Mathematics
The graph shows median earnings of graduates who achieved a degree in this subject area one, three and five years after graduating from here.
£21k
£30k
£34k
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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You can use this to get an idea of who you might share a lecture with and how they progressed in this subject, here. It's also worth comparing typical A-level subjects and grades students achieved with the current course entry requirements; similarities or differences here could indicate how flexible (or not) a university might be.
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Post-six month graduation stats:
This is from the Destinations of Leavers from Higher Education Survey, based on responses from graduates who studied the same subject area here.
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:
The Higher Education Careers Services Unit have provided some further context for all graduates in this subject area, including details that numbers alone might not show
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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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