MSci 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			MSci 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			MSci
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9	What level is this qualification?			7		Length of programme			4 years (5 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	N/A.
26	N/A.
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28	Reference points
29	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).
30	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. The departments of computer science and mathematics together are able to offer all the basic modules required to deliver the programme. These modules already exist and are proven in action. There are plans to also introduce new advanced modules at later stages. The programme has been designed based on the current modularisation and semesterisation guidance.
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33	Credit Transfer and Recognition of Prior Learning
34	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
35	No
36	No
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38	Exceptions to Regulations
39	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.
40	N/A
41	N/A
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43	Internal Transfers
44	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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46		Transfers in:	Yes			Transfers out:	Yes
47			Yes				Yes
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49	Statement of Purpose
50	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.
51	The MSci 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. Through the programme, you will see three integrated strands of work which help you to develop your mathematical foundations, computational thinking, and your skills as an engineer. It is the combination of these three areas that will make you attractive to employers, enabling you to make an immediate contribution when you move into employment. The programme will provide you with a solid foundation in the principles and practices of Data Science, including the relevant components from Computer Science such as coding, machine learning and basic engineering, and Mathematics such as probability, statistics and calculus; and with specialist training in the focussed area of Data Science. Key aspects of modern Data Science are covered, from the theoretical building blocks of linear algebra and algorithms, through to real-world implementations, and discussion of the wider impacts that Data Science has on society. You will develop the technical skills needed to critically analyse, mine, and manage different kinds of data in order to learn and discover interesting patterns, and make actionable conclusions from those assessments, while participating effectively in multidisciplinary teams. You will also develop the skills to contribute professionally to solving complex commercial and industrial engineering problems having undertaken a focussed project in the field of Data Science. You will demonstrate these skills through a stage four project where you work for a customer (academic or industrial) in solving a real-world problem.
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53	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).
54	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
55	BSc (Hons) Data Science (with or without a Year in Industry)			Entry point (see relevant programme spec) & Exit award	Partial PLOs met (see relevant programme BSc spec).			Same as the standard programme (must include modules GODS and CBDA studied at honours level)
56	BSc (Hons) Data Systems (with or without a Year in Industry)			No	Partial PLOs met (see relevant programme BSc spec).			Same as the standard programme (does not include modules GODS and CBDA)
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58	Programme Learning Outcomes
59	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...'
60	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]
61	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]
62	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]
63	4	Communicate complex 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]
64	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]
65	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]
66	7	Apply theoretical and practical knowledge of chosen areas of cutting-edge computer science and available commercial technology to new or unfamiliar problems they encounter in employment or further study, and communicate the results in a significant technical report or other appropriate medium. [Cutting-edge of CS research and applications]
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68	Diverse entry routes
69	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.
70	While our 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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72	Inclusion
73	Please confirm by ticking the box 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.
74										TRUE
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76	Employability
77	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.
78	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 (e.g. ENG1) 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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92	Description of Structure
93	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).
94	All Stage 1 Modules are core.
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98	Part-Time Structure (Normally PGT Only): For part-time variants of programmes, please use the box below to specify which modules will be taken in year 1 and which will be taken in year 2 (and so on if more than 2 years).
99	NA
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101	Can a York Language Elective module be taken ab initio (ie beginner level) in Stage 1?										No
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103	Are students on this programme permitted to take elective modules (in place of any of their option module choices; subject to limits set out in the UG and PGT programme design policies)?										Yes
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105	Module List & Programme Structure
106	Please use the following tables to indicate the programme structure by listing the modules on the programme. There are separate tables for each stage of the programme. For PGT programme or other programmes that only have one stage, simply use the first table and leave the rest blank. As far as possible, please order modules by semester. Additional rows can be added as needed (copy and paste an existing row to retain formatting). Note: It is suggested that optional modules that are grouped (e.g. where students have to select from lists grouped by term or theme) be listed under a generic title for the group under the 'title of module' column, with titles of individual specific modules in that group in the 'further title' column. Where modules are grouped, it is not necessary to complete each row against every column for every module in the group if the information is the same for all modules in that group.
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110	Stage 1 (UG):
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