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Study information

Data Science with Artificial Intelligence (2024)

1. Programme Title:

Data Science with Artificial Intelligence

NQF Level:

7

2. Description of the Programme (as in the Business Approval Form)

The MSc Data Science with Artificial Intelligence is an innovative inter-disciplinary taught course designed with industry and aimed at students wishing to work or research in data science. The course will cover the core areas of data science (e.g. machine learning, statistics, high-performance computing, visualisation) as well as specific applications (e.g. network analysis, text analysis, machine vision) and social context (e.g. governance, ethics, business applications).   It will also cover core areas of Artificial Intelligence (AI)  such as machine learning, natural language processing, computer vision, and evolutionary algorithms. Increasingly data science is underpinned by methods in AI. A research project allows you to develop research skills in an area of interest, guided by an academic supervisor.

Data science with Artificial Intelligence is a growth area with excellent career development potential. University of Exeter is a world-class research active institution which regularly features in UK Top-10 and Global Top-100 rankings. The University is making significant new investment in data science and in AI.
 

3. Educational Aims of the Programme

The MSc Data Science with Artificial Intelligence will provide outstanding training in data science. Course content will cover the fundamental mathematical and computational techniques underpinning data science applications, with extensive coverage of machine learning and statistical modelling, tools for handling large and complex datasets, image and text analysis, digital media, and the social and legal context for data analytics.

Content will be delivered through a combination of lectures, workshops, individual self-study, and group work on Exeter’s Streatham campus.

MSc Data Science with Artificial Intelligence will include content relevant to artificial intelligence, including computer vision, knowledge representation, bio-inspired computation and optimisation.

4. Programme Structure

The MSc Data Science with Artificial Intelligence programme is a 1-year (full-time) programme of study at National Qualification Framework (NQF) Level 7 (as confirmed against the FHEQ).  This programme is divided into ‘Stages’. Each Stage is normally equivalent to an academic year. The programme is divided into units of study called ‘modules’ which are assigned a number of ‘credits’. The credit rating of a module is proportional to the total workload, with 1 credit being nominally equivalent to 10 hours of work. The  programme comprises 180 credits in total.

Interim Awards

If you do not complete the programme, you may be able to exit with a lower qualification.

Postgraduate Diploma: At least 120 credits of which 90 or more must be at level M.

Postgraduate Certificate: At least 60 credits of which 45 or more must be at level M.

5. Programme Modules

Stage 1

Code Title Credits Compulsory NonCondonable
COMM514Research Project 60YesNo
COMM108Data Systems15YesNo
ECMM422Machine Learning15YesNo
ECMM443Introduction to Data Science15YesNo
ECMM445Learning from Data15YesNo
Select 60 credits (See Stage Notes below*):
ECMM409Nature-Inspired Computation15NoNo
ECMM423Evolutionary Computation & Optimisation15NoNo
ECMM426Computer Vision15NoNo
COMM511Statistical Data Modelling15NoNo
ECMM410Research Methodology15NoNo
MTHM047Bayesian Statistics, Philosophy and Practice 15NoNo
ECMM447Social Networks and Text Analysis15NoNo
ECMM450Stochastic Processes15NoNo
MTHM033Statistical Modelling in Space and Time15NoNo
COMM510Multi-Objective Optimisation and Decision Making15NoNo
SOCM033Data Governance and Ethics15NoNo
ECMM461High Performance Computing 15NoNo
BEMM190Digital Transformation15NoNo

*Students must choose at least two from the following modules, and any other two modules: ECMM409; ECMM423; ECMM426

Part time students will follow:

Year 1

You must complete at least 4 modules (60 credits) which must include ECMM443 Introduction to Data Science and COMM108 Data Systems.

Year 2

You must complete at least 4 modules (60 credits) one of which must be COMM514 Research Project.

Students may choose up to 30 credits of NQF Level 7 modules which are not listed above, subject to approval, timetabling and satisfaction of prerequisites.

Not all modules will be available every year, and new modules may be made available from time to time.

6. Programme Outcomes Linked to Teaching, Learning & Assessment Methods

On successfully completing the programme you will be able to: Intended Learning Outcomes (ILOs) will be accommodated & facilitated by the following learning & teaching and evidenced by the following assessment methods:

A Specialised Subject Skills & Knowledge

On successfully completing the programme, you should be able to:

1) Demonstrate knowledge and be able to use methods for artificial intelligence to find patterns and relationships in complex datasets;

2) Demonstrate knowledge and be able to use methods for statistical inference and data modelling;

3) Show awareness of the social context of data science, including key aspects of data governance, legal requirements, and ethical considerations;

4) Show awareness of the organisational context of data science, including the role and applications of data science to business practices;

5) Apply computational methods for analysis of large and complex datasets, including network analysis, image and text analysis, and high-performance computing.

Learning & Teaching Activities

Lectures, workshops, seminars, practicals, online materials and formal training. Each module also has core and supplementary texts, or material recommended by module deliverers, which provide in-depth coverage of the subject and go beyond the lectures.

Assessment Methods

The assessment strategy for each module is explicitly stated in the full module description given to students. Group and team skills are addressed within modules dealing with specialist and advanced skills.

Assessment methods will include essays, technical reports, closed book tests, practical exercises in programming and data analysis, project work, and individual and group presentations.

B Academic Discipline Core Skills & Knowledge

1) Critically analyse and interpret relevant academic and technical literature;

2) Demonstrate competence in underpinning mathematical and computational techniques, including linear algebra, probability, calculus, programming and programming tools such as notebooks and integrated development environments;

3) Effectively handle large and complex datasets and prepare them for analysis;

4) Use appropriate methods for data visualisation and presentation of data;

5) Construct data analysis pipelines to test hypotheses or deliver particular goals;

6) Use appropriate statistical and machine learning methods to find patterns in complex datasets;

7) Appreciate the basic legal and regulatory requirements for data privacy, ethical use of data, and data governance.

Learning & Teaching Activities

Lectures, workshops, seminars, practicals, online materials and formal training. Each module also has core and supplementary texts, or material recommended by module deliverers, which provide in-depth coverage of the subject and go beyond the lectures.

Assessment Methods

The assessment strategy for each module is explicitly stated in the full module description given to students. Group and team skills are addressed within modules dealing with specialist and advanced skills.

Assessment methods will include essays, technical reports, closed book tests, practical exercises in programming and data analysis, project work, and individual and group presentations.

C Personal / Transferable / Employment Skills & Knowledge

1) Effectively communicate methods and results based on analysis of complex datasets in both written reports and oral presentations;

2) Demonstrate awareness of tools and technologies relevant to data science;

3) Design and manage a data analysis project from initiation to final report;

4) Work effectively independently or in a team.

Learning & Teaching Activities

Lectures, workshops, seminars, practicals, online materials and formal training. Each module also has core and supplementary texts, or material recommended by module deliverers, which provide in-depth coverage of the subject and go beyond the lectures.

Assessment Methods

The assessment strategy for each module is explicitly stated in the full module description given to students. Group and team skills are addressed within modules dealing with specialist and advanced skills.

Assessment methods will include essays, technical reports, closed book tests, practical exercises in programming and data analysis, project work, and individual and group presentations.

7. Programme Regulations

Credit

Postgraduate (PG) Programmes: The programme consists of 180 credits at NQF level 7. The pass mark for award of credit in PG modules (NQF level 7) is 50%.

Progression

Condonement is the process that allows you to be awarded credit (and so progress to the next stage or, in the final stage, receive an award), despite failing to achieve a pass mark at a first attempt. You are not entitled to reassessment in condoned credit.

Postgraduate (PG) Programmes: Up to (45/30/20) credits of failure can be condoned on the following conditions:

You must have completed and been assessed in modules amounting to sufficient credit for the final award (i.e. 180 credits for a Masters; 120 credits for a PGDip; and 60 credits for a PGCert).

You must pass the modules marked with a 'Yes' in the 'Non-condonable' column in the tables above.

You must achieve an average mark of at least 50% across the full 60 or 90 credits of assessment in the stage, including any failed and condoned modules.

Condonement can only be applied to failed modules which have a mark of 0–49%.

Assessment and Awards

The award will normally be based on at least 180 credits of which 150 or more must be at NQF level 7

Classification

The marking of modules and the classification of awards broadly corresponds to the following marks:

Undergraduate Degrees                                         Postgraduate Degrees

Class I    70% +                                                         Distinction   70%+

Class II   Division I 60-69%                                       Merit            60-69%

Class II   Division II 50-59%                                      Pass            50-59%

Class III  40-49%

Full details of assessment regulations for UG programmes and PGT programmes assessment regulations can be found in the Teaching Quality Assurance Manual (TQA) on the University of Exeter website. Generic marking criteria are also published here.

Please see the Teaching and Quality Assurance Manual for further guidance.

8. College Support for Students and Students' Learning

In accordance with University policy, a system of personal tutors is in place for all students on this programme. A University-wide statement on such provision is included in the University's TQA Manual. As a student enrolled on this programme, you will receive the personal and academic support of the Programme Coordinator and will have regular scheduled meetings with your Personal Tutor; you may request additional meetings as and when required. The role of personal tutors is to provide you with advice and support for the duration of the programme and extends to providing you with details of how to obtain support and guidance on personal difficulties, such as accommodation, financial difficulties and sickness. You can also make an appointment to see individual teaching staff.

Information Technology (IT) Services provide a wide range of services throughout the Exeter campuses including open access computer rooms, some of which are available 24 hours, 7 days a week. Help may be obtained through the Helpdesk, and most study bedrooms in halls and flats are linked to the University's campus network.

Additionally, the College has its own dedicated T support staff, helpdesk and computer facilities which are linked to the wider network, but which also provide access to some specialised software packages. Email is an important channel of communication between staff and students in the College and an extensive range of web-based information (see https://intranet.exeter.ac.uk/emps/) is maintained for the use of students, including a comprehensive and annually revised student handbook.

The Harrison Learning Resource Centre is generally open during building open hours. The Centre is available for quiet study, with four separate rooms that can be booked for meetings and group work. Amongst its facilities, the Learning Resource Centre has a number of desks, four meeting rooms with large LCD screens, and free use of a photocopier. Also available are core set texts from your module reading lists, and undergraduate and MSc projects from the past two years.

Online Module study resources provide materials for modules that you are registered for, in addition to some useful subject and IT resources. Generic study support resources, library and research skills, past exam papers, and the 'Academic Honesty and Plagiarism'; module are also available through the student portal (http://vle.exeter.ac.uk).

The Student/Staff Liaison Committee enables students & staff to jointly participate in the management and review of the teaching and learning provision.

10. Admission Criteria

All applications are considered individually on merit. The University is committed to an equal opportunities policy with respect to gender, age, race, sexual orientation and/or disability when dealing with applications. It is also committed to widening access to higher education to students from a diverse range of backgrounds and experience.

Candidates must satisfy the general admissions requirements of the University of Exeter.

Candidates will be required to have at least a 2:1 degree in a numerate subject, usually Computer Science or a closely related discipline, and must be able to show evidence of good programming and software development ability in recognised modern computer languages.

Candidates may be interviewed by Skype or similar to assess their programming ability and suitability for the course.

11. Regulation of Assessment and Academic Standards

Each academic programme in the University is subject to an agreed College assessment and marking strategy, underpinned by institution-wide assessment procedures.

The security of assessment and academic standards is further supported through the appointment of External Examiners for each programme. External Examiners have access to draft papers, course work and examination scripts. They are required to attend the Board of Examiners and to provide an annual report. Annual External Examiner reports are monitored at both College and University level. Their responsibilities are described in the University's code of practice. See the University's TQA Manual for details.

12. Indicators of Quality and Standards

Certain programmes are subject to accreditation and/or review by Professional and Statutory Regulatory Bodies (PSRBs).

14 Awarding Institution University of exeter
15 Lead College / Teaching Institution College of Engineering, Mathematics and Physical Sciences
16 Partner College / Institution
17 Programme accredited/validated by
18 Final Award(s) MSc
19 UCAS Code (UG programmes) DAT
20 NQF Level of Final Awards(s): 7
21 Credit (CATS and ECTS) 180 (90 ECTS)
22 QAA Subject Benchmarking Group (UG and PGT programmes) Data Science
23 Origin Date May 22nd 2024 Last Date of Revision: June 18th 2024