BSc (Hons) Data Science Programme Specification 2024-25

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1	ACADEMIC QUALITY TEAM
2	Programme Specifications 2024-25
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5	Programme Title			BSc (Hons) Data Science (with or without a Year in Industry).
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7	This document applies to students who commenced the programme(s) in:			2024			Award type			BSc
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9	What level is this qualification?			6			Length of programme			3 years (4 with a year in industry)
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11	Mode of study (Full / Part Time)			Full-time
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13	Will the programme use standard University semester dates?			Yes			For York Online programmes, will standard dates for such programmes be used?			N/A
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15	Awarding institution			University of York			Board of Studies for the programme			Computer Science
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17	Lead department			Computer Science			Other contributing departments			Mathematics
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19	Language of study and assessment			English			Language(s) of assessment			English
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21	Is this a campus-based or online programme?			Campus-based
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23	Partner organisations
24	If there are any partner organisations involved in the delivery of the programme, please outline the nature of their involvement. You may wish to refer to the Policy on Collaborative Provision
25	NA
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27	Reference points
28	Please state relevant reference points consulted in the design of this programme (for example, relevant documentation setting out PSRB requirements; the University's Frameworks for Programme Design (UG or PGT); QAA Subject Benchmark Statements; QAA Qualifications and Credit Frameworks).
29	The programme uses the accreditation requirements recommended by the ACM Data Science Task Force for a UG data science curriculum and expert judgement as reference points.
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31	Credit Transfer and Recognition of Prior Learning
32	Will this programme involve any exemptions from the University Policy and Procedures on Credit Transfer and the Recognition of Prior Learning? If so, please specify and give a rationale
33	No
34	No
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36	Exceptions to Regulations
37	Please detail any exceptions to University Award Regulations and Frameworks that need to be approved (or are already approved) for this programme. This should include any that have been approved for related programmes and should be extended to this programme.
38	No
39	No
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41	Internal Transfers
42	Please use the boxes below to specify if transfers into / out of the programme from / to other programmes within the University are possible by indicating yes or no and listing any restrictions. These boxes can also be used to highlight any common transfer routes which it would be useful for students to know.
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44		Transfers in:	Yes				Transfers out:	Yes
45			Yes					Yes
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47	Statement of Purpose
48	Please briefly outline the overall aims of the programme. This should clarify to a prospective student why they should choose this programme, what it will provide to them and what benefits they will gain from completing it.
49	The BSc in Data Science produces multi-skilled, highly competent graduates who are equipped to make significant contributions to their career field and who understand the implications of their work, both for themselves and for society as a whole. The programme has three integrated strands which develop mathematical foundations, computational thinking, and engineering skills. It is the combination of these three areas that make graduates attractive to employers, enabling an immediate contribution when they move into employment. The programme provides a solid foundation in the principles and practices of Data Science, including the relevant components from Computer Science such as coding and machine learning, and from Mathematics such as probability, statistics and calculus. Key aspects of modern Data Science are covered, from the theoretical building blocks of linear algebra and algorithmics, to real-world implementations and discussion of the wider impacts that Data Science has on society. Students will develop the technical skills needed to critically analyse, mine, and manage different kinds of data in order to learn and discover interesting patterns. The programme teaches how to make actionable conclusions from those assessments, while participating effectively in multidisciplinary teams. Study culminates in a focussed research project in the field of Data Science which develops the skills to contribute professionally to solving complex commercial and industrial engineering problems.
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60	If there are additional awards associated with the programme upon which students can register, please specify the Statement of Purpose for that programme. This will be most relevant for PGT programmes with exit awards that are also available as entry points. Use additional rows to include more than one additional award. Do not include years in industry / abroad (for which there are separate boxes).
61	Exit Award Title			Is the exit award also available as an entry point?	Outcomes: what will the student be able to do on exit with this award?				Specify the module diet that the student will need to complete to obtain this exit award
62	TBC			TBC	TBC				TBC
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64	Programme Learning Outcomes
65	What are the programme learning outcomes (PLOs) for the programme? (Normally a minimum of 6, maximum of 8). Taken together, these outcomes should capture the distinctive features of the programme and represent the outcomes that students progressively develop in the programme and achieve at graduation. PLOs should be worded to follow the stem 'Graduates will be able to...'
66	1	Apply computational, mathematical and statistical theory to solve data science problems, using skills in problem analysis, representation, abstraction, programming and software engineering. [Computational and Mathematical thinking]
67	2	Critically analyse statements, arguments or conjectures that underpin the theory of mathematics, statistics and computer science and justify the principles chosen for such critiques. [Analytical skills]
68	3	Develop computational and mathematical techniques for critical analysis, mining, and management of data to learn and discover meaningful patterns and knowledge beneficial for real world applications. [Computational and Mathematical Data Science]
69	4	Communicate ideas in computational and mathematical data science and the discovered knowledge in a clear and organised manner, at a level appropriate for the intended recipients and present an effective summary of these ideas. [Communication]
70	5	Adapt to new and unfamiliar challenges in computational and mathematical data science, recognising appropriate ideas and approaches drawn from a range of technologies, languages, paradigms, models and mathematical theories. [Adaptability]
71	6	Apply computational and mathematical data science skills to practical problems and real-world datasets collected in collaboration with industrial partners, in a safe, ethical, and secure way. [Professionalism]
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73	Diverse entry routes
74	Detail how you would support students from diverse entry routes to transition into the programme. For example, disciplinary knowledge and conventions of the discipline, language skills, academic and writing skills, lab skills, academic integrity.
75	While the programme does rely on students having taken an A-level in Mathematics (or equivalent), we do not have requirements for any student to have qualifications in Computer Science, Programming, or any other specific technical skill. We presume no knowledge specific to computing or data science, and use the first modules of the programme to bring all students to the same level. To this end, we teach a wide variety of mathematics, technical skills and programming languages. The first year students get supported by a transition officer for smoothly transitioning to university study. The maths skills centre organises regular study skills sessions to impart relevant skills. Equally, we make heavy use of peer-learning to allow students to model good learning to each other. Every module with practical aspects in the programme uses lab sessions in which students work together on problems, and a we have extensive group coursework assessments. Early modules in stage 1 teach and assess academic writing skills, which helps students to develop this key skill and also supports students for whom English is an additional language. This is continued through into later stages, culminating in the final stage project which has a significant writing component.
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77	Inclusion
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79	Please confirm by ticking the box on the right that the design, content and delivery of the programme will support students from all backgrounds to succeed. This refers to the University's duties under the Equality Act 2010. You may wish to refer to the optional Inclusive Learning self-assessment tools to support reflection on this issue.										TRUE
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81	Employability
82	Please give a brief overview - no more than 5 sentences - of how the programmes helps develop students' employability. Your Faculty Employability Manager can help reflection on this issue. This statement will be used by Marketing as the basis for external content with respect to employability.
83	Skills for employability are embedded throughout the programme, with many opportunities for students to return to skills. Industrial case studies are used in several modules. Some modules (eg Engineering 1) base teamwork projects on realistic scenarios where students are exposed to managed risks and project management. Many modules (eg those in the statistics stream in maths and data in computer science) use real-world data sets to inform and motivate the material presented. Several optional modules (e.g. Statistical Pattern Recognition, Operations Research) will have invited talks from graduate employers.
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87	[For Undergraduate and Integrated Masters Programmes Only]
88	Are you offering any variations of this programme, such as additional years abroad or industry?
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90	Year abroad			TRUE		Will the year abroad programme be available directly via UCAS; for students to transfer in having entered the main programme; or both?				N/A
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92	Year in industry			TRUE		Will the year in industry programme be available directly via UCAS; for students to transfer in having entered the main programme; or both?				Both
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94	Year in enterprise			TRUE
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96	Placement year			TRUE
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99	Description of Structure
100	Provide a BRIEF description of the structure of the first stage (UG) or programme (PGT): this is only necessary if this is not evident from the tables below. For instance, an entry might be 'students choose X modules in Autumn Semester from List A and Y modules from List B'. For York Online programmes using the 'carousel' model, the description should include whether any modules have to be taken in a particular order (e.g. if there is an introductory module and/or any constraints on the timing of option and/or ISM or ISM-related modules).