MSc Data Science 2024-25.xlsx

	B	C	D	E	G	I	J	K	L	O
1	ACADEMIC QUALITY TEAM
2	Programme Specifications 2024-25
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5	Programme Title			MSc Data Science
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7	This document applies to students who commenced the programme(s) in:			2024		Award type			MSc
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9	What level is this qualification?			7		Length of programme			1 year
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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			Chemistry
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17	Lead department			Chemistry		Other contributing departments			PET, Biology, Environment and Geography, HYMS
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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
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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	University frameworks: FPD(PGT), QAA(Credit framework). We have also aimed to align the content with the certification requirements for the Alliance for Data Science Professionals with the aim that after some work experience students should be in a position to apply for certification.
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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	None
41	None
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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:	No			Transfers out:	No
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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	Data science skills are now in huge demand across every field of science from particle physics to ecology, from drug design to climate change. Data driven innovation makes a substantial contribution to the UK economy and data science skills are highly valued. The aim of this programme is to equip students from diverse educational and social backgrounds to take on data science roles, and to be agile enough to adapt to new fields and challenges as economic and scientific demands change. The programme will concentrate on the skills which data scientists apply on a daily basis to perform data analyses and to rigorously evaluate the results of those analyses, rather than focussing on theoretical bases or algorithmic structure. Through this approach, we aim to make data science skills available to a wider range of students, particularly those with less formal mathematical or computational backgrounds. We also hope to increase access for students from groups who have traditionally been marginalised in the computational sciences, for example on the basis of gender or race. You will learn to gather, organise, process and analyse data using a range of tools from simple graphical representations to modern machine learning algorithms. You will get experience working with data users from different scientific disciplines. You will learn to communicate the results of your work to a range of audiences, including stakeholders in the data, other data scientists and non expert audiences. You will study data science problems in at least two scientific areas, and perform a research project in the field of one of the contributing departments. The programme will prepare you for a career in data science or machine learning, whether in industry, e-commerce or academia. You will also build skills which are relevant to a range of neighbouring disciplines from data modelling to software development. The programme will also teach you how to apply your skills in new subject domains so that you can move with the rapidly changing information economy.
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62	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).
63	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
64	Postgraduate Certificate in Data Science			No	Programming and data analysis on a well determined problem. Read scientific literature from a new field and assess areas of certainty and uncertainty.			60 credits including pass marks in all three core modules in semester 1. This will provide a grounding in the skills and techniques of data science.
65	Postgraduate Diploma in Data Science			No	Programming and data analysis on a well determined problem, read scientific literature from a new field and assess areas of certainty and uncertainty. Be able to apply data science methods in two different fields of science. Be able to plan a research project and perform a literature review.			120 credits including pass marks in all three core modules in semester 1 and at least one option module in semester 2. In addition to the PG certificate, this ensures that students will have dveloped an understanding of how data science is applied in at least one application area.
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67	Programme Learning Outcomes
68	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...'
69	1	Design, implement and document computer programs to manage, manipulate and visualise digital data, working in both individual and team environments.
70	2	Identify and implement appropriate data science and machine learning methods to analyse complex datasets, and to communciate the results to both technical and non-technical audiences.
71	3	Develop and evaluate training and testing datasets and methods and use them to critically evaluate the validity, accuracy and limitations of data analyses.
72	4	Assimilate the data, language, and techniques of previously unfamiliar scientific disciplines; read and critically evaluate the literature of those disciplines, and communicate effectively with scientists in those disciplines.
73	5	Create project proposals which align to professional and ethical standards of a field and apply consultancy skills to provide data science services to scientists in a field.
74	6	Design, perform and evaluate the outcomes of an independent research project in a data science application area.
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76	Diverse entry routes
77	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.
78	The programme is designed from inception to be accessible to students from a diverse range of backgrounds, from first degrees in maths or a physical science to medical students on academic intercalation years, although some background in a quantitative science is required. We manage this in several ways: We teach the practical skills that data scientists use everyday rather than the theoretical bases, characterised by the slogan "no proofs, rigorous validation". We teach the students how to read the literature from and learn a new discipline, enabling them to apply their skills across multiple disciplines or transition fields as required by future career opportunities. We use example datasets from diverse disciplines so that all students will be able to identify their own field background in the teaching material. All of these approaches also serve to mitigate many of the elite-preserving biases in the computational sciences, in which the social construction of the "programmer" identity works in tandem with teaching methods which front-load complexity in order to exclude students who don't align with the programmer identity. Academic and writing skills and academic integrity are embedded deeply in our skills module in understanding how science works and in training in writing for a range of audiences. Disability support will build on the work already in progress in chemistry, where we have strong support on neurodiversity (including neurodivergent programme and module leads for the course). Physical disabilities will be supported through use of VLE tools and DSA support for assistive technologies. The most challenging case for us would be classes of visual impairment which are not well addressed by existing adaptations and programming tools: this could be an interesting area for future development of the course but would entail a significant research element. Language skills will be supported through standard university support courses.
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87	Inclusion
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89	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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91	Employability
92	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.
93	In 2020 the Emerging Job report from LinkedIn listed data scientist at #7, highlighting the demand for this field. This programme will prepare students with the essential professional skills and tools that data scientists apply on a daily basis. Students will learn to work across disciplines and to communicate with non specialists, allowing them to move with the demands of the sector. They will learn about different types of data, the ethical and practical issues of handling such data, and how to articulate these skills to employers. Students will learn to critically evaluate their own preconceptions, to enable them to work in the diverse environments of different job markets.
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