About the course

Overview

To meet employer demand, the number of skilled people in AI and data science needs to increase by 70% by 2020, just to keep pace with demand. There is also an acknowledgement that increasing diversity in the AI workforce is vital to ensure that everyone with the potential to participate has the opportunity to do so, in order to reflect the needs and make-up of society as a whole. This course therefore, has been developed with an Employer Steering Group including organisations such as Synectics Solutions Ltd, Powelectrics, Aspire Housing, SSE Enterprise Energy Solutions, Santander, Caja Ltd Astec IT Solutions, Teal Legal, Hildebrand Technology Limited, Rushkeep, Digital Law UK & Anson Evaluate, so that it not only meets the current demands of industry but also the skills that future industrial AI and data science will require.

We have then combined this with our own research into the barriers that underrepresented and non science, technology, engineering and mathematics STEM) students face when studying AI/Data Science courses, to create the course content, state-of-the-art delivery design, contextual assessments and pastoral support.

This course aims to support students, from both STEM and non-STEM disciplines by providing those without prior experience, the foundations of areas such as programming and mathematics, so that all students can then progress to more advanced areas such as Machine Learning, Data Analytics, Visualisation, Cloud Computing, the “Internet of Things” and Intelligent Systems. During the course, students will also further develop their academic and professional skills and get the chance to work with other students and external organisations to make real interdisciplinary and societal impact in taught modules, an A.I. and Data Science ambassador scheme and as part of an industrial project/placement.

Teaching facilities

You will be taught in dedicated teaching and laboratory space equipped with cutting edge computer systems. We also have a MakerSpace, a multi-purpose research lab equipped with a variety of robots and aVicon motion-tracking system, a Gaming Lab and a perception lab with state-of-the-art virtual reality equipment. You will also have the opportunity to access data and systems from current research projects such as the Smart Energy Network Demonstrator (SEND) and to visit the Horwood Energy Centre on the Keele campus, the control room for SEND.

Should you wish for an alternative route and/or complete flexibility with your studies, we also offer 100% online Computer Science programmes. Find out more here.

100% Online Computer Science Programmes:

• MSc Computer Science
• MSc Computer Science with Data Analytics
• MSc Computer Science with Artificial Intelligence

Course structure

Indicative Modules

Semester 1

CSC-40045 Distributed Intelligent Systems - 15 credits
Provides you with the ability to evaluate and design intelligent systems involving the integration and coordination of multiple intelligent systems.

CSC-40054 Data Analytics and Databases - 15 credits
Equips you with the knowledge of a variety of tools and statistical techniques that enable you to make sense of the emergence and exponential growth of big data.

Optional Modules (students choose two from four dependent on their prior experience)

CSC-40044 System Design & Programming - 15 credits
Comprehensive introduction to system design and programming for students who did not graduate from a computer science or related programme or have relevant experience.

CSC-40072 Mathematics for A.I. and Data Science - 15 credits
Introduction to the mathematical concepts relevant to A.I. and Data Science for students from non-mathematical backgrounds.

CSC-40041 Research Horizons - 15 credits
Provides you with the knowledge and skills required to be able to undertake a simple literature review of a research topic related to the Division's research in A.I. and Data Science, and to develop a novel research idea and plan for a research proposal.

CSC-40056 Internet of Things - 15 credits
Provides a practical and theoretical understanding of IoT technologies and their applications in a variety of industries. This includes advanced communications, data analytics and security issues involved in IoT systems and evaluation of their applicability for different types of problems.

Semester 2

CSC-40048 Visualisation for Data Analytics - 15 credits
Equips you with an appropriate understanding of the use of Data Analytics within areas such as health, security, science and business and with a variety of Data Visualisation techniques to interpret trends and patterns in big data.

CSC-40070 Applications of A.I., Machine Learning and Data Science - 15 credits
Equips you with knowledge and experience of a variety of cutting edge A.I. and Machine Learning techniques applied to “real-word” problems and datasets.

Optional Modules (students choose two from three)

CSC-40039 Cloud Computing - 15 credits
Provides a practical and theoretical understanding of virtualisation technologies and their realisation in Cloud Computing implementations

CSC-40038 Collaborative Application Development - 15 credits
Gives an opportunity to overcome the practical difficulties of working with stakeholders and team members to produce applications to solve A.I. and Data Science problems.

CSC-40050 Research and Consultancy Skills - 15 credits
Aims to enhance your skills and knowledge in preparation for your MSc Project or Industrial Placement and for a successful career as an AI. and Data Science professional.

Semester 3

CSC-40040 MSc Project OR CSC-40035 Industrial Placement - 60 credits
After the taught modules have been completed you have the choice to undertake a formal academic project supervised by academic staff in the School or to take an industrial placement in a relevant company or organisation. In both options you will apply the skills you have learned during the taught modules. The decision about the type of project or placement you will do will be made together with the academic supervisors and will be based on your performance during the taught modules.

How the course is taught

You will have interactive lectures delivered by academic staff who are experts in the relevant area, as well as tutorial and practical sessions. The course uses a variety of innovative learning tools and methods including working with real clients and problems. This gives students experience and skills they can apply directly to their career and jobs in the future.

Small group teaching is central to how we deliver the course and our staff and students are able to very quickly create supportive working relationships both in person and online.

Each module in the first semester is taught intensively over 6-week periods. This means that you will be able to learn the key competencies and skills very quickly. This is particularly important for students who do not have previous experience of studying Computer Science or Maths. In the second semester each module is taught over a 12-week semester. Here, you will study more advanced topics and put the key skills you have learned in semester one into practice.

On average you will have around sixteen hours of lectures and practicals each week. Alongside this you will be expected to work independently on exercises and coursework projects, as well as researching course topics online and via the University library.

Entry Requirements

Academic entry requirements

Undergraduate degree in any subject with second class lower (2:2) or an international equivalent. We will also consider students with equivalent industrial work experience.

English Language Entry Requirement for International Students

IELTS 6.5 with a minimum of 5.5 in each component . The University also accepts a range of internationally recognised English tests.

If you do not meet the English language requirements, the University offers a range of English language preparation programmes.

During your degree programme you can study additional english language courses. This means you can continue to improve your English language skills and gain a higher level of English.

Fees and scholarships

Fees (2021/22 academic year)

UK/EU students £9,000 per year

International students £16,500 per year

Office for Students Funded Scholarship for the MSc in Artificial Intelligence and Data Science

About

In 2020, Keele University was one of 18 Universities awarded a share of £13 million to boost the number and diversity of graduates in AI and data science technologies over the next three years. This followed a competitive bidding process run by the Office for Students (OfS) on behalf of the Department for Digital, Culture, Media and Sport (DCMS), Department for Business, Energy and Industrial Strategy (BEIS) and the Office for Artificial Intelligence (OAI).

Our programme offers Scholarships of £10,000 that are available for underrepresented students listed in the following eligibility criteria:

• Black students (Defined in line with the HESA student ethnicity codes) 
  • 21 Black or Black British – Caribbean
  • 22 Black of Black British – African
  • 29 Other Black background
  • 41 Mixed – White and Black Caribbean
  • 42 Mixed – White and Black African
• Disabled students (as defined under the Equality Act 2010)
• Female students

For 2021 entry, there are 8 Scholarships available. 

Further details as to why these underrepresented groups have been selected can be found on the OfS website.

Prioritising criteria in case of oversubscription

Students with 2 or more underrepresented group criteria will be prioritised initially. Applicants will then be considered by the selection panel where the primary consideration is the candidate’s personal statement (submitted via the form linked below).

Panel membership comprises of the Head of School, Director of Education, School Athena Swan Lead and University Athena SWAN Support Officer.

Eligible countries

Students from all countries, who meet the criteria, are eligible.

Payment amount & frequency

Successful applicants will receive £10,000 over the course of the academic year. Payment dates to be confirmed.

How to apply

In order to apply for the Scholarship, you must have already applied for a place on the University's MSc in Artificial Intelligence and Data Science course (specific entry requirements can be found on the course page). You can then apply for the Scholarship via the form below:

Application form: https://forms.gle/PjAeXfD9o7Xqf5Ji9

Closing date: 30th July 2021.

Please note that this is a competitive process and not all applications will be successful. The Scholarship is available only to students who register for and attend the degree programme. There is no appeals process if you are not successful as the panel’s decision is final; also, please note that we will not be able to offer feedback. You are welcome to reapply for the next intake if you are unsuccessful.

For September 2021 intake, notifications will be sent at the latest by 13th August 2021.  

For further information on any aspect of completing your application, please email: e.de.quincey@keele.ac.uk

Our expertise

The MSc in Artificial Intelligence and Data Science is taught in the School of Computing and Mathematics.

The School has highly rated research in cutting-edge areas including machine learning & computational intelligence, computational neuroscience and biomedical engineering, evolutionary systems, software and systems engineering and regularly wins funding from the leading UK research funding agencies, ESPRC and BSRC as well as prestigious funding bodies such as the Leverhulme Trust.

In addition, the School hosts monthly research seminars where we invite experts from outside the School to present their latest research to our students and staff.

The School also has several collaborative projects with industry partners including a Knowledge Transfer Partnership with the global automotive company, Bentley Motors Ltd which aims to develop innovative data mining processes that will allow Bentley to exploit the value hidden in the data it owns and collects.

The MSc in Artificial Intelligence and Data Science programme combines these research strengths with our industrial expertise.

Academic staff in the School of Computing and Mathematics include:

• Professor Peter Andras, Head of School
• Professor Fiona Polack, Research Centre Lead
• Dr Theo Kyriacou, Postgraduate Course Director
• Dr Ed de Quincey, Senior Lecturer (Computing)
• Dr Alastair Channon, Reader (Computing)
• Dr Sandra Woolley, Senior Lecturer (Computing)
• Dr Thomas Neligwa, Lecturer (Computing)

Careers

Through our close collaboration with industrial partners, we continually seek to ensure that the MSc in A.I. and Data Science gives our students appropriate technical skills and knowledge - as well as the consultancy, team and project management skills that are required for success in careers where A.I. and Data Science are being developed or used.

The University has a Careers and Employability team who can provide you with advice and guidance about your future career. The team offers specialist workshops, for example 'Options with Postgraduate Study' and Moving on with your PhD', and can also meet you individually to give guidance and support to help you develop your career. We also hold regular careers fairs on campus and informal events where you can meet employers. You will also have access to our database of job opportunities and digital resources such as online psychometric testing.