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Data Science

Entry requirements


A level

A,B,B

Grades ABB (preferably to include Computer Science or Mathematics)

We welcome applications that include the EPQ. Where relevant, this may be included in our offer, resulting in an ‘A’ Level offer reduced by one grade.

GCSE/National 4/National 5

GCSE English Language at grade 4 (C) PLUS GCSE Mathematics at grade 6 (B)

International Baccalaureate Diploma Programme

32

IB with 32 points to include 6 in all Higher Level subjects

Pearson BTEC Level 3 National Extended Diploma (first teaching from September 2016)

D*DD

IT/Numerate subjects (e.g. IT(Software Development) preferred

UCAS Tariff

128

Grades ABB (preferably to include Computer Science or Mathematics)

About this course


Course option

4.0years

Full-time | 2022

Subject

Computer science

There is high demand for specialists who can extract insights and value from data. These insights inform everything from marketing activities to government strategy, transforming business and society. This integrated master’s degree provides you with a strong basis in computer science alongside specialist skills to analyse complex data sets.

This degree is suitable for those who would like to develop creative computational solutions to derive the data-intensive transformation that is reshaping the way our society operates. It will build your foundational skills within computer science, such as algorithmic thinking and programming, and develop the specialist data scientists skills needed for the extraction of actionable insight from complex data collections.

You should have a strong interest in technologies that produce and analyse data and will need to develop computational solutions for the acquisition and analysis of data, and use creative problem-solving skills to extract knowledge that can answer challenging questions in a domain of investigation.

Data Science is a multidisciplinary domain that requires training in a wide-range of skills from programming to visualisation, to data analysis. The demand for data scientists in the UK has grown more than ten-fold in the past five years *. This programme aims to equip you with both strong foundational computer scientist skills and specialised data scientist skills. This powerful combination of computing and analytics will provide you a skill set that will be widely applicable not only within the computing industry but also in various application domains, from retail to health.

This skill set will be widely applicable within the computing industry and in various other sectors including retail and health, making our graduates highly employable.

- Acquire leading-edge knowledge, skills and techniques required by the data science profession

- Become proficient in a broad range of programming languages and software design techniques

- Work with and learn from active researchers in machine learning, high-performance computing and data visualization

- Apply your knowledge and skills to develop solutions in data-intensive sectors where insights can deliver commercial advantage or social benefit

- Access excellent work experience opportunities at nearby Tech City.

Modules

Year 1
Study our common first year for all our computer science students, learning six core topics including operating systems, web development and Java.

- Computation and Reasoning (15 Credits)
- Mathematics for Computing (15 Credits)
- Systems Architecture (15 Credits)
- Programming in Java (30 Credits)
- Databases and Web Development (30 Credits)
- Operating Systems (15 Credits)

Year 2
Deepen your knowledge of computer science with core modules such as C++ and data structures. Boost your professional skills with a team project and a work-based project.

- Data Structures and Algorithms (15 Credits)
- Language Processors (15 Credits)
- Object-Oriented Analysis and Design (15 Credits)
- Professional Development in IT (15 Credits)
- Team Project (30 Credits)
- Programming in C++ (15 Credits)
- Computer Networks (15 Credits)

Year 3
Build your data science skill set with five core modules and three elective modules, including principles of data science, and AI.

- Computer Vision (15 Credits)
- Principles of Data Science (15 Credits)
- Introduction to AI (15 Credits)
- Programming and Mathematics for AI (15 Credits)
- Agents and Multi Agents Systems (15 Credits)
- Games Technology (15 Credits)
- Advanced Databases (15 Credits)
- Theory of Computation (15 Credits)
- Advanced Games Technology (15 Credits)
- Professional Experience (Placement) Placement Reports (30 Credits)
- Data Visualization (15 Credits)
- Digital Signal Processing and Audio Programming (15 Credits)
- Advanced Programming: Concurrency (15 Credits)
- Functional Programming (15 Credits)
- Cloud Computing (15 Credits)
- Information Security Fundamentals (15 Credits)
- User Centred Systems (15 Credits)

Year 4
Develop professional data science expertise with five core modules, including big data and visual analytics. Showcase your knowledge with a data-intensive individual research project.

- Neural Computing (15 Credits)
- Machine Learning (15 Credits)
- Big Data (15 Credits)
- Visual Analytics (15 Credits)
- Information Retrieval (15 Credits)
- Individual Project (45 Credits)
- Software Systems Design (15 Credits)
- User-Centred System Design (15 Credits)
- Digital Signal Processing and Audio Programming (15 Credits)
- Service Oriented Architectures (15 Credits)
- Advanced Programming: Concurrency (15 Credits)
- Advanced Algorithms and Data Structures (15 Credits)
- Cloud Computing (15 Credits)
- Computational Cognitive Systems (15 Credits)
- Advanced Games Technology (15 Credits)

Assessment methods

"Most modules are assessed with examinations and coursework. Details can be found in the individual module specifications. Typically, modules are mainly assessed through written examination, and coursework also contributes to module assessment.

The written examinations will contain theoretical questions, including mathematical aspects, as well as writing and analysing small amounts of code and small essays on the applications of computational techniques.

As you move over to the more specialised modules as part of your Programme Stage 3 and Programme Stage 4, you will be expected to demonstrate how well you can synthesise various pieces of knowledge and be also assessed on how well you can critically reflect on the solutions you are suggesting.

The balance of assessment by coursework (assessed essays and assignments) unseen examinations and a final year project will to some extent depend on the optional modules you choose.

Year 1
Written examination: 41%
Coursework: 59%

Year 2
Written examination: 35%
Coursework: 65%

Year 3
Written examination: 24%
Coursework: 76%

Year 4
Written examination: 35%
Coursework: 65%"

The Uni


Course location:

City, University of London

Department:

Department of Computer Science

Read full university profile

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.

67%
med
Computer science

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

59%
Staff make the subject interesting
76%
Staff are good at explaining things
73%
Ideas and concepts are explored in-depth
77%
Opportunities to apply what I've learned

Assessment and feedback

Feedback on work has been timely
Feedback on work has been helpful
Staff are contactable when needed
Good advice available when making study choices

Resources and organisation

66%
Library resources
57%
IT resources
79%
Course specific equipment and facilities
64%
Course is well organised and has run smoothly

Student voice

Staff value students' opinions
Feel part of a community on my course

Who studies this subject and how do they get on?

83%
UK students
17%
International students
85%
Male students
15%
Female students
82%
2:1 or above
11%
First year drop out rate

Most popular A-Levels studied (and grade achieved)

C
C
C

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.

£27,300
med
Average annual salary
75%
low
Employed or in further education
68%
low
Employed in a role where degree was essential or beneficial

Top job areas of graduates

62%
Information technology and telecommunications professionals
12%
Information technology technicians
8%
Engineering professionals

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.

What about your long term prospects?

Looking further ahead, below is a rough guide for what graduates went on to earn.

Computing

The graph shows median earnings of graduates who achieved a degree in this subject area one, three and five years after graduating from here.

£28k

£28k

£36k

£36k

£40k

£40k

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.

Explore these similar courses...

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Lower entry requirements
Same University
Nearby University
Royal Holloway, University of London
Computer Science (Artificial Intelligence)
Master of Science - MSci
4.0 years | Full-time | 2022

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This is what the university has told Ucas about the criteria they expect applicants to satisfy; some may be compulsory, others may be preferable.

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This is the percentage of applicants to this course who received an offer last year, through Ucas.

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This is what the university has told Ucas about the course. Use it to get a quick idea about what makes it unique compared to similar courses, elsewhere.

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Course location and department:

This is what the university has told Ucas about the course. Use it to get a quick idea about what makes it unique compared to similar courses, elsewhere.

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Teaching Excellence Framework (TEF):

We've received this information from the Department for Education, via Ucas. This is how the university as a whole has been rated for its quality of teaching: gold silver or bronze. Note, not all universities have taken part in the TEF.

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This information comes from the National Student Survey, an annual student survey of final-year students. You can use this to see how satisfied students studying this subject area at this university, are (not the individual course).

This is the percentage of final-year students at this university who were "definitely" or "mostly" satisfied with their course. We've analysed this figure against other universities so you can see whether this is high, medium or low.

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This information is from the Higher Education Statistics Agency (HESA), for undergraduate students only.

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?

Have a question about this info? Learn more here