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Study information

Applied Data Science and Statistics (2023)

1. Programme Title:

Applied Data Science and Statistics

NQF Level:

7

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

MSc Applied Data Science and Statistics is aimed at non-specialists, allowing students to develop the advanced-level mathematical and statistical skills to enable them to draw and utilize insights from data sets to inform business decisions.

Training will be provided in the technical aspects of both Data Science and Statistics, including statistical modelling; machine learning; uncertainty quantification; data acquisition and management and high-performance computing, together with the societal and ethical issues related to the use of data in contemporary society. Throughout, the emphasis will be on the application of the methods that are learnt in a variety of areas including industry, medicine and healthcare environment and climate.

Data science 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.

3. Educational Aims of the Programme

The MSc Applied Data Science and Statistics will provide comprehensive training in the application of data science and statistical modelling to practical problems in a variety of settings. A wide variety of statistical modelling and machine learning methods will be covered and throughout the course, the emphasis is on learning methods and techniques through their application using real-world examples. Specific focus will be on the communication of results and understanding the implications that different data generating mechanisms can have on interpretation. Course content will range from introductory material covering the basic mathematical and coding techniques that will be required through to the application of cutting-edge methods for analysing complex patterns in data, and the social and legal context for data analytics.

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

4. Programme Structure

The MSc in Applied Data Science and Statistics is a 1-year full-time programme of study at National Qualification Framework (NQF) level 7 (as confirmed against the FHEQ). 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

The following tables describe the programme and constituent modules. Constituent modules may be updated, deleted or replaced as a consequence of the annual review of this programme.

Stage 1

Code Title Credits Compulsory NonCondonable
MTHM501Working with Data15YesNo
MTHM502Introduction to Data Science and Statistical Modelling15YesNo
MTHM503Applications of Data Science and Statistics15YesNo
MTHM507Communicating Data Science15YesNo
MTHM017Advanced Topics in Statistics15YesNo
MTHM506Statistical Data Modelling15YesNo
MTHM505Data Science and Statistical Modelling in Space and Time15YesNo
SOCM033Data Governance and Ethics15YesNo
MTHM504Applied Data Science and Statistics Project60YesYes

Part-time Structure:

Year 1

     

Code

Title

Term

Credits

MTHM501

Working with Data

1

15

MTHM502

Introduction to Data Science and Statistical Modelling

1

15

MTHM017

Advanced Topics in Statistics

2

15

SOCM033

Data Governance and Ethics

2

15

   

Total

60

Year 2

     

Code

Title

Term

Credits

MTHM503

Applications of Data Science and Statistics

1

15

MTHM506

Statistical Data modelling

2

15

MTHM507

Communicating Data Science

2

15

MTHM505

Data Science and Statistical Modelling in Space and Time

2

15

   

Total

60

       

Code

Title

Term

Credits

MTHM504

Applied Data Science and Statistics project

2 & 3 or 3 & 1 of Jan 2022*

60

MTHM504 Can be split over year 1 or 2, or if preferred can be taken in year 2 only

   

 

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

1 Select appropriate statistical and machine learning methods to detect, model and understand patterns in data

2 Apply a range of statistical modelling and machine learning techniques to real-world problems

3 Communicate the results of complex analyses with an understanding of how the source of data, and how it was collected, can have an effect on subsequent analyses.

4 Understand the social context of data science, including key aspects of data governance, legal requirements, and ethical considerations.

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:

Closed book examinations (1), written reports (1, 2,3, 4),practical exercises in coding and data analysis (1, 2), presentations (2, 3, 4)

B Academic Discipline Core Skills & Knowledge

5. Understand the methodology, and practical use, of statistical regression modelling

6. Select appropriate methods based on the problem being addressed

7. Perform critical appraisal of relevant academic and technical literature.

8. Understand the technical details behind new methods and appraise their suitability before applying them

9. Effectively handle large and complex datasets and prepare them for analysis.

10. Understand the importance of data visualisation within data analysis and the communication of results, and be able to select and apply appropriate methods.

11. Understand the consequences of 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:

Closed book examinations (5), written reports (5, 6,7, 8, 9, 10, 11), practical exercises in coding and data analysis (6, 8, 9, 10), presentations (5, 6, 7, 10, 11)

C Personal / Transferable / Employment Skills & Knowledge

12. Effectively communicate methods and results based on analysis of complex datasets in both written reports and oral presentations.

13. Demonstrate awareness of tools and technologies relevant to data science and statistical modelling.

14. Design and manage a data analysis project from initiation to final report.

16. 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:

Closed book examinations (1), written reports (12, 13, 14, 16),practical exercises in coding and data analysis (13), presentations (12, 13, 14, 15)

7. Programme Regulations

Credit

Postgraduate (PG) Programmes: The programme consists of 180 credits. In total, participants must take at least 150 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:

  1. 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).
  1. You must pass the modules marked with a 'Yes' in the 'non-condonable' column in the tables above.
  1. You must achieve an average mark of at least 50% across the full 180 credits of assessment in the stage, including any failed and condoned modules.
  1. Condonement can only be applied to failed modules where a mark of 0 – 49 has been achieved.   
     

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:

Postgraduate Degrees

Distinction   70%+

Merit            60-69%

Pass            50-59%

Full details of 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 IT 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).

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.

Entry requirements

Candidates will be required to have at least a 2:2, or equivalent qualification.

Successful applicants will usually have at least an A-level or equivalent in mathematics and/or have received quantitative skills training as part of their undergraduate programme or professional experience.

Prior experience of coding is not necessary on this programme.

IELTS overall score 6.5. No less than 6.0 in any section. 

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). This programme is not subject to any such requirements.

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) Stats1
20 NQF Level of Final Awards(s): 7
21 Credit (CATS and ECTS) 180 credits (90)
22 QAA Subject Benchmarking Group (UG and PGT programmes)
23 Origin Date February 8th 2023 Last Date of Revision: February 8th 2023