Page 1 of 10
PROGRAMME APPROVAL FORM: TAUGHT PROGRAMMES SECTION 1 – THE PROGRAMME SPECIFICATION 1. Programme title and designation
MSc in Data Science For undergraduate programmes only
Single honours Joint Major/minor
2. Final award
Award Title Credit
value
ECTS equivalent
Any special criteria
MSc Data Science 180 90
3. Nested award
Award Title Credit
value
ECTS equivalent
Any special criteria
4. Exit award
Award Title Credit
value
ECTS equivalent
Any special criteria
PgDiploma Data Science 120 60 At least 60 credits
should be from the list below
PgCertificate Data Science 60 30 At least 30 credits should be from the list below
List of exit award special criteria: to be taken from core, module, compulsory modules or 7CCSMCS06, 7CCSMDSP, 7CCSMPNN, 7CCSMRVA, 7CCSMFM05, 7CCSMTSP and 7CCSMATD. 5. Level in the qualifications framework 7
6. Attendance
Full-time Part-time Distance learning
Mode of attendance Yes n/a n/a
Minimum length of programme 1 year n/a n/a
Maximum length of programme 6 years n/a n/a
7. Awarding institution/body King’s College London 8. Teaching institution King’s College London 9. Proposing department Informatics/Mathematics 10. Programme organiser and contact
Details
Professor Kaspar Althoether & Dr Elizabeth Sklar
[email protected] Ext 2431 [email protected] Ext 2236 11. Relevant QAA subject benchmark/
Professional, statutory and regulatory body guidelines
Page 2 of 10 (UG: http://www.qaa.ac.uk/ASSURINGSTANDARDSANDQUALITY /SUBJECT-GUIDANCE/Pages/Honours-degree-benchmark-statements.aspx PGT: http://www.qaa.ac.uk/AssuringStandardsAndQuality/subject- guidance/Pages/Master%27s-degree-benchmark-statements.aspx)
12. Date of production of specification November 2014 13. Date of programme review 2 Years
14. Educational aims of the programme
i.e. what is the purpose of the programme and general statements about the learning that takes place over the duration of the programme
The programme aims to equip students with:
In-depth understanding of the general principles of the computational and
statistical methodologies and methods used in data science, and their underlying assumptions and limitations.
The practical skills needed to effectively assemble, collate, store, manage, and retrieve the data required for data science projects and organise the data in a form that is suitable for analysis
The critical judgement to decide, given typical project objectives and constraints, the appropriate statistical and computational data exploration or analysis
techniques to employ data management, analysis and decision-making, and to evaluate critically data science activities and projects.
The ability to select, to employ, and to develop appropriate software tools to apply computational and statistical data analysis techniques.
The skill set required to plan, undertake, manage, and critically assess a data science project.
An appreciation for the professional, ethical and legal responsibilities of the data scientist and the societal and ethical implications of data science projects on stakeholders and on society at large.
An appreciation of standard conceptual or scientific models in at least one domain of application of data science techniques.
15. Educational objectives of the programme/programme outcomes (as relevant to the SEEC Credit Level Descriptors)
The programme provides opportunities for students to develop and demonstrate knowledge and understanding and skills in the following areas:
Knowledge and understanding The programme provides a knowledge
and understanding of the following: 1. The general principles of the
computational and statistical techniques that are fundamental to data science, and how these affect the data science practice.
2. Methodologies employed in large-scale data science projects. 3. The modelling methods used to
formulate data science problems.
These are achieved through the following teaching/learning methods and strategies:
Page 3 of 10 4. In-depth understanding of statistical
analysis techniques, computer programming, simulation, database systems, data warehousing,
information retrieval, data preparation, data visualisation, data mining and machine learning.
5. The assumptions and limitations underlying methods and techniques and how these affect their application. 6. Advanced computational, statistical or
mathematical techniques for data science.
7. Data science practice and applications in a specific domain of application. 8. The professional, ethical and legal
responsibilities of the data scientist, as well as the technical, societal, legal, ethical and management dimensions of data science projects.
All objectives are acquired through a combination of lectures, coursework and the individual project.
Assessment:
Coursework, written examinations, and an individual project. The latter
involves an assessment of written reports, the creation and presentation of data science artefacts (such as data sets, models, and computer program source code) and oral presentations.
Skills and other attributes Intellectual skills:
1. Plan, conduct and report a programme of original research. 2. Analyse critically and solve data
science problems.
3. Choose computational and statistical data science modelling and analysis methods with critical judgement. 4. Be creative in the modelling, analysis
and solution of data science problems. 5. Integrate and evaluate information
and data from a variety of sources. 6. Take a holistic approach in tackling
data science problems, applying professional judgements to balance desired decision objectives, risks, costs, benefits, efficiency,
effectiveness, safety, reliability, aesthetics, and environmental impact. 7. Make decisions in complex and
unpredictable situations.
8. Be self-directed and original in solving problems, and act autonomously in undertaking research.
These are achieved through the following teaching/learning methods and strategies:
Intellectual skills are developed through a combination of lectures, coursework and the individual project.
Analysis and problem solving skills are further developed through coursework and supervision of project work.
Decision making skills and independent exercise of judgement are particularly developed through the individual project.
Assessment:
Analysis and problem solving skills are assessed through unseen written examinations and coursework. Research and design skills are
assessed through coursework reports, project reports and presentations. Practical skills:
1. Critical evaluation of the methodology and methods used in data science projects.
2. Effective data management, including collection, cleaning, collation,
organisation, storage, and retrieval of data.
These are achieved through the following teaching/learning methods and strategies:
Practical skills are developed through a combination of lectures, coursework and the individual project.
Page 4 of 10 3. Organise and manage data science
projects.
4. Identification and definition of research ideas.
5. Preparation of technical presentations. 6. Production of technical reports and
documentation.
7. Giving oral presentations.
8. Effective use of the scientific literature. 9. Effective note-taking.
10. Effective use of computational tools and statistical software and packages.
Practical skills are assessed through coursework reports, individual project reports and presentations.
Skill 10 is not explicitly assessed.
Generic/transferable skills:
1. Communicate effectively (in writing, verbally, and through diagrams and graphs) with specialist and non-specialist audiences.
2. Apply computational and mathematical skills.
3. Transfer techniques and solutions from one problem domain to another. 4. Use information technology.
5. Retrieve information using catalogues and search engines.
6. Manage resources and time. 7. Learn independently in familiar and
unfamiliar situations with
open-mindedness and in the spirit of critical enquiry.
8. Learn effectively for the purpose of continuing professional development and further research in a wider context throughout their career.
9. Make decisions in complex and
unpredictable situations systematically and creatively.
10. Exercise initiative and personal responsibility.
These are achieved through the following teaching/learning methods and strategies:
Transferable skills are developed through a combination of lectures, tutorials, small group supervision, supervised laboratory classes, coursework and individual projects throughout the year of the programme. Skills 6, 7, 8, 10 are developed through most of the curriculum.
Skill 2 is taught through lectures and coursework.
Skills 3, 4, 5, 6, 7, 8, 9, 10 are developed mostly through individual project work.
Assessment:
Skill 1 is assessed through coursework reports, presentations and oral and written examinations.
Skill 2 is assessed primarily through examinations, coursework and project work.
Skills 3, 6 and 7 (in part), 9 and 10 are assessed mostly in the context of the individual project.
The other skills are not formally assessed.
16. Statement of how the programme has been informed by the relevant subject benchmark statement(s)/professional, statutory and regulatory body guidelines (UG: http://www.qaa.ac.uk/ASSURINGSTANDARDSANDQUALITY/SUBJECT-GUIDANCE/Pages/Honours-degree-benchmark-statements.aspx
PGT: http://www.qaa.ac.uk/AssuringStandardsAndQuality/subject-guidance/Pages/Master%27s-degree-benchmark-statements.aspx)
Page 5 of 10
17. In cases of joint honours programmes please provide a rationale for the particular subject combination, either educational or academic
n/a
Page 6 of 10 18. Programme structure
See Programme Handbook for modules to be taken.
If a Master’s programme, are level 6 credit levels permitted within the programme? No
Maximum number of credits permitted with a condoned fail (core modules excluded) 30
Are students permitted to take any additional credits, as per regulation located in A3? Yes
Are students permitted to take a substitute module, as per regulation located in A3? Yes
Are there are any exceptions to the regulations regarding credits, progression or award requirements? (where relevant the information should also differentiate the particular requirements of pathways within a programme or nested/exit awards)
No
Other relevant information to explain the programme structure
Please note that new students enrolling on the information provided on this section of the PAF will have these regulations stipulated throughout their programme of study. The only exception to this will be if there are changes made by Professional, Regulatory or Statutory Bodies that are noted to this programme.
Where a student cannot take a module within the programme, including core and compulsory modules, in order to comply with regulations A3:
Unless the programme specification makes explicit provision as part of the requirements of reassessment, a student may not enrol on a module that has already taken and passed at either undergraduate or postgraduate level. Neither may a student enrol for a module that overlaps with another module that the student has already taken and passed. Modules will be deemed to overlap if both the content and the level of complexity of the two modules are similar.
Page 7 of 10 19. Marking criteria
The marking criteria will be those that currently apply to MSc programmes in NMS Faculty. The pass mark will be 50%.
20. Will this Programme report to an existing Board, and if so which one? If a new Programme Board of Examiners is to be set up please note name of Board here This programme will require a new Board of Examiners, comprising all lecturers teaching modules on the degree, and all staff supervising the individual projects. The Board will be serviced by the existing joint Informatics & Mathematics Departmental Office (IMDO).
21. Please confirm that the process for nominating External Examiners has
commenced, and if known, note whom the nominated External Examiner(s) may be An informal approach has been made to a potential External Examiner. External examiner arrangements will be confirmed in good time before the start of 2015/16.
22. Measures to help ensure that the programme is inclusive to all students
Anticipatory:
All students who receive an offer will be provided with information on the support services offered by the College and those who have indicated that they have a disability in their application will receive a letter from the Faculty Disability Advisor offering the applicant the opportunity to discuss their requirements
Flexible:
A range of teaching methods will be utilised, with assistance as appropriate in lab environments.
Collaborative:
Individuals are assessed with the Faculty Disability Officer and the Department Education Co-ordinator or their nominee to identify what adjustments can be made to facilitate students’ educational experiences. Feedback on how this is working can be managed via the personal tutor system.
Transparent:
Double marking is carried out for individual assessments components that account for 15% or more of the total marks, along with marks being checked by external examiners. Students may also formally request to see their marked exam papers.
Equitable:
Page 8 of 10
PROGRAMME APPROVAL FORM
SECTION 3 – SUPPLEMENTARY INFORMATION
Not all of the information in this section will be relevant for all programmes and for some programmes this section will not be relevant at all
1. Programme name MSc Data Science
2. Is this programme involved in collaborative activity?
Yes No
If yes what type of Collaborative Provision is it (tick appropriate box)? Does the programme have an access/feeder
Programme for entry into it?
Does the programme have an articulation/ progression agreement for entry into it? Dual Award
Franchised Provision
Joint Award
Multiple Award
Partnership Programme
Recognition of Study or Award of Credit through off-campus study or placement
Placements, including those in industry, those required for teacher education, experience necessary for qualifications in the health professions and continuing professional development Staff and student exchange
Provision of learning support, resources or specialist facilities
Validated provision
Distance learning and online delivery involving work with delivery organisations or support providers
Page 9 of 10
Have the relevant stages and appropriate paperwork been approved and the paperwork forwarded onto QAS Office?
Yes No Not applicable
3. If the programme is a joint award with an institution outwith the University of
London, validated provision or franchised provision, has the necessary approval been sought from College Education Committee?
Yes No Not applicable
Please attach a copy of Part 1 of the Partner Profile and checklist submitted to the College Education Committee
4. Partnership programme - in cases where parts or all of the programme are
delivered away from one of the College campuses by a body or bodies external to the College please provide the following details
Name and address of the off-campus location and external body
Percentage/amount of the programme delivered off-campus or by external body
Nature of the involvement of external body
Description of the learning resources available at the off-campus location
What mechanisms will be put in place to ensure the ongoing monitoring of the delivery of the programme, to include monitoring of learning resources off-site or by the external body?
Please attach the report of the visit to the off-campus location
5. Recognition of study or award of credit through off-campus study or placement - please indicate how the time will be spent, the length of time out, the amount of credit and whether it is a compulsory or optional part of the programme
Page 10 of 10
Time spent ………Credit amount ………..Compulsory/optional.……… 6. Please provide a rationale for any such time outside the College, other than that which is a requirement of a professional, statutory or regulatory body
N/A.
7. Please give details if the programme requires validation or accreditation by a professional, statutory or regulatory body
There is no relevant professional, statutory or regulatory body as yet, although there may well be one in the future (eg. The British Computer Society, the Royal Statistical Society, or the Market Research Society).
Name and address of PSB
Date validation/accreditation commenced:
Frequency of validation/ accreditation
Date of last validation/accreditation Date of next validation/ accreditation
