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Bangor University

Data Science and Machine Learning

UCAS Code: H113

Bachelor of Science (with Honours) - BSc (Hons)

Entry requirements


GCSE/National 4/National 5

Maths grade C/4 or equivalent required

UCAS Tariff

112

Typical offer is based on a minimum 112 tariff points from a Level 3 qualification* e.g. • A Levels (including 1 in a Science/Maths/Computing/IT, excluding General Studies) • BTEC National/Extended Diploma and Cambridge Technical Extended Diploma in relevant subject (as above): DMM • City & Guilds Advanced Technical / Extended Diploma: case by case • International Baccalaureate Diploma is accepted • Access to HE in a relevant subject as listed above**: Pass **Other subject areas may be considered on a case by case basis. • Welsh Baccalaureate is accepted. • Key Skills are not accepted International Candidates: school leaving qualifications and college diplomas are accepted from countries worldwide (subject to minimum English Language requirements), details at: www.bangor.ac.uk/international/applying/entryrequirements We also welcome applications from mature applicants. *For full details go to our website and for a full list of accepted Level 3 qualifications, go to www.ucas.com.

About this course


Course option

3.0years

Full-time | 2020

Subject

Machine learning

The Data Science with Machine Learning degree is designed to provide students with the opportunity to join careers in the modern data-driven economy, or further academic study. This degree provides a firm footing in data handling, analysis and communication of findings and using modern research-driven Machine Learning techniques. Graduates from this course are anticipated to join industry in data analysis, financial analysis, data support or similar positions. Their degree will have equipped them with not only practical skills, but an understanding of the underpinning theory. This allows them to both provide accurate and insightful analysis and to communicate that insight to colleagues.

The B.Sc. Data Science with Machine Learning degree provides an inventive combination of computer science fundamentals, programming, data analysis, critical reasoning and machine learning. The programme produces graduates that are proficient in using data to derive insight using up-to-the-minute methods and algorithms. This combination allows graduates to exploit the new frontier of machine learning and AI, idealling placing them to take up roles in both industry and academia.

In addition, the programme places a premium on responsible use of data. It champions the professional values necessary commensurate with the immense power analytics can wield. To this end, an appreciation of relevant ethical, legal and social issues is embedded throughout. Graduates are also equipped with the transferable skills needed for any knowledge economy position; teamwork, communication, self-management, independence and discipline.

Modules

For details of the modular structure, please see the course description on Bangor University's website.

Tuition fees

Select where you currently live to see what you'll pay:

Channel Islands
£9,000
per year
England
£9,000
per year
EU
£9,000
per year
Northern Ireland
£9,000
per year
Scotland
£9,000
per year
Wales
£9,000
per year

The Uni


Course location:

Bangor University

Department:

School of Computer Science and Electronic Engineering

TEF rating:

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What students say


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.

Computing

Sorry, no information to show

This is usually because there were too few respondents in the data we receive to be able to provide results about the subject at this university.


Who studies this subject and how do they get on?

82%
UK students
18%
International students
86%
Male students
14%
Female students
63%
2:1 or above
12%
Drop out rate

Most popular A-Levels studied (and grade achieved)

D
C
D

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.

Computing

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.

£21,000
med
Average annual salary
89%
low
Employed or in further education
90%
med
Employed in a role where degree was essential or beneficial

Top job areas of graduates

72%
Information technology and telecommunications professionals
13%
Business, finance and related associate professionals
3%
Business, research and administrative professionals
What do graduate employment figures really tell you?

What about your long term prospects?

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

Machine learning

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

£20k

£20k

£24k

£24k

£29k

£29k

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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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).

We calculate a mean rating of all responses to indicate whether this is high, medium or low compared to the same subject area at other universities.

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This information is from the Higher Education Statistics Agency (HESA).

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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