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1. Admissions/ Management Information
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Title of the new programme – including any lower awards
Please provide the titles used for all awards relating to this programme. Note: all programmes are required to have at least a Postgraduate Certificate exit award.

See guidance on programme titles in:
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Masters MSc Computer Science with Data Analytics
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Postgraduate Diploma Postgraduate Diploma in Computer Science with Data AnalyticsPlease indicate if the Postgraduate Diploma is available as an entry point, ie. is a programme on which a student can register or as an exit award, ie. that are only available to students exiting the masters programme early, or both.Exit
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Postgraduate Certificate Postgraduate Certificate in Computer SciencePlease indicate if the Postgraduate Certificate is available as an entry points, ie. is a programme on which a student can register, or as an exit award, ie. that are only available to students exiting the masters programme early, or both.Exit
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Level of qualification.
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This document applies to students who commenced the programme(s) in:2024
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Awarding institutionTeaching institution
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University of York University of York
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Department(s):
Where more than one department is involved, indicate the lead department		Board of Studies
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Lead Department Computer ScienceComputer Science
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Other contributing Departments:
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Route code
(existing programmes only)
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Admissions criteria
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● 2ii (or equivalent) degree in any subject.
● Students whose first degree was not taught in English should also have an appropriate English language qualification.
○ Minimum acceptable qualifications are IELTS 6.5 with a minimum score of 6.0 in all components.
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Length and status of the programme(s) and mode(s) of study
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ProgrammeLength (years/ months) Status (full-time/ part-time)
Please select		Start dates/months
(if applicable – for programmes that have multiple intakes or start dates that differ from the usual academic year)		Mode
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Face-to-face, campus-basedDistance learningOther
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MSc Computer Science with Data AnalyticsThe minimum length will be 24 months and the maximum length is 6 years. The standard length for completion is expected to be 2 years although the design of the carousel means that once the introductory module is completed, students can determine the length of their study as long as they complete within 6 years.Part-time6 intakes a year.Please select Y/NNoPlease select Y/NYes
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Please select Y/NPlease select Y/N
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Please select Y/NPlease select Y/N
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Language(s) of study
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English
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Language(s) of assessment
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English
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2. Programme accreditation by Professional, Statutory or Regulatory Bodies (PSRB)
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2.a. Is the programme recognised or accredited by a PSRB
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Please Select Y/N: Noif No move to section 3
if Yes complete the following questions
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3. Additional Professional or Vocational Standards
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Are there any additional requirements of accrediting bodies or PSRB or pre-requisite professional experience needed to study this programme?
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Please Select Y/N: Noif Yes, provide details
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4. Programme leadership and programme team
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4.a. Please name the programme leader for the year to which the programme design applies and any key members of staff responsible for designing, maintaining and overseeing the programme.
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Lilian Blot (PL)
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4.b. How are wider stakeholders such as students/ alumni, professional bodies and employers involved in the design of the programme and in ongoing reflection on its effectiveness?
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The programme is new and has been developed in consultation with an external partner organisation who has considerable experience of the design of on-line programmes in the US. Ongoing reflection will be aided by student feedback, external stakeholders (i.e. Careers, employers) and the departmental industrial advisory board.
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5. Purpose and learning outcomes of the programme
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5.a. Statement of purpose for applicants to the masters programme
Please express succinctly the overall aims of the programme as an applicant facing statement for a prospectus or website. 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.
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Data is being collected at an unprecedented speed and scale and becoming an ever-increasing part of modern life, but “big data” is of little use without “big insight”. The skills required to develop such insights are in short supply and the talent to extract information and value from big data is scarce.

This online MSc programme is aimed at working professionals and graduates from other disciplines who are seeking to move into a career in computer science.

The programme is designed to develop your theoretical and foundational understanding of Artificial Intelligence (AI). You will develop specialist skills and knowledge in machine learning, data analytics, data mining and text processing via specialised modules and an independent data analytics project. You will also develop your core computer science skills, including computational thinking, computational problem solving, software development and broad knowledge of computer science. The exploration of theory and practical exercises will complement and reinforce the knowledge and skills you build as you work your way through the programme. You will enhance your written communication skills, and continue to expand and deepen your knowledge through critical engagement with the discipline. As a graduate of the programme you will be able to apply your knowledge and skills to the workplace. The Department has a long-established and successful track record of training for industry, ensuring that our programmes remain current to the needs of industry.

This is an online programme and has been designed to be flexible. The programme can be completed within a minimum of 2 years, but must be completed within a 6 year period. Each module is of 8 weeks duration and you can choose to 'step-out' between modules to accommodate your work or personal commitments. The programme is designed around an asynchronous learning study model which means students can learn from different locations and time zones. Your tutors will guide you through the weekly learning material, providing additional engagement in the subject area, and supporting your progress towards a single final assessment. Helping you balance the requirements of postgraduate studies with your other responsibilities.

The University is a member of the Russell Group of research-intensive universities. Our learning and teaching is research-led.


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5.b.i. Programme Learning Outcomes - Masters
Please provide six to eight statements of what a graduate of the Masters programme can be expected to do.
If the document only covers a Postgraduate Certificate or Postgraduate Diploma please specify four to six PLO statements for the PG Certificate and four-eight for the PG Diploma in the sections 5.b.ii and 5.b.iii as appropriate.
Taken together, these outcomes should capture the distinctive features of the programme. They should also be outcomes for which progressive achievement through the course of the programme can be articulated, and which will therefore be reflected in the design of the whole programme.
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PLOOn successful completion of the programme, graduates will be able to:
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1Apply computational thinking to big data problems, using skills in analysis, design and implementation of computing systems, drawing on the foundations of data analytics and computer science and the current research literature. [Computational thinking]
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2Analyse a big data problem from a written description, derive requirements and specifications from an understanding of problems, and create and/or justify designs to satisfy given requirements, applying knowledge of machine learning, data analytics, data mining and system analysis and design. [Evaluation and Synthesis]
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3Build computer-based systems of varying levels of complexity to serve the needs of users, making effective use of the variety of physical implementations on which that software may be running, and applying the theory and practice of programming and software engineering. [Software and hardware; Users]
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4Critically evaluate and effectively apply data mining tools and algorithms for use to address a complex problem including big data, underpinned by a knowledge of how those systems work [Application]
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5Scope, plan, manage and execute an individual research project of significant size in data analytics, demonstrating critical engagement with the discipline. [Independence]
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6Communicate complex computational problems and their solutions in written format to technical and non-technical professional colleagues, in a clear and organised manner and using compelling and convincing arguments drawn from relevant evidence. [Communication]
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7Operate as responsible Computer Science professionals, by maintaining awareness of key legal and ethical issues and risk management. [Professionalism]
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8
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5.b.ii. Programme Learning Outcomes - Postgraduate Diploma
Please provide four to eight statements of what a graduate of the Postgraduate Diploma programme can be expected to do.
Taken together, these outcomes should capture the distinctive features of the programme. They should also be outcomes for which progressive achievement through the course of the programme can be articulated, and which will therefore be reflected in the design of the whole programme.
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5.c. Explanation of the choice of Programme Learning Outcomes
Please explain your rationale for choosing these PLOs in a statement that can be used for students (such as in a student handbook). Please include brief reference to:
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i) Why the PLOs are considered ambitious or stretching?
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The programme focuses on developing a broad knowledge of computer science, while simultaneously building skills in Data Analytics. We expect students to develop an understanding of both the engineering and science sides of the discipline, to be enabled to communicate with both technical and non-technical audiences and to carry out independent research in order to advance their computer science literacy. It is not enough to learn just about the various technologies, but graduates need to understand that computer scientists have to act in a professional way, aware of the impact of their work on society. The PLOs are ambitious because this is a conversion programme, meaning that students can join the the programme with no previous computer science qualifications at UG level. It is will not be easy to achieve all of these outcomes, and graduates from this programme will be well-prepared to advance their career prospects within computing and related sectors.
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ii) The ways in which these outcomes are distinctive or particularly advantageous to the student:
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The learning outcomes are distinctive in at least two aspects: the balance of software and hardware, and the balance of scientific theory and engineering practice. Through critical engagement and complex problem solving, our graduates will be equipped with the skills to adapt in a fast changing sector. Whilst technologies may become outdated within a few years, the skills developed on the programme will enable students to remain current. In particular, PLO1 will be advantageous to the students for the development of their computational thinking as they will come from various disciplines and the ways of their thinking in their original disciplines will be significantly different. A further advantage will be that the PLOs can be directly related to the student’s workplace and reflected upon in that context, as the students will be mostly in work; even if they are from a non-computer science workplace, they will be able to find ways of applying computer science thinking in their own work context. A final advantage of the design is that most modules reflect the particular research interests in the department, such as artificial intelligence, data analytics and critical systems.
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iii) Please 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
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All students start with the initial module, Algorithms and Data Structures. Whatever their background, this will provide them with a solid foundation in algorithms and programming and enable them to progress to the next stage of their study. The module does not assume prerequisite knowledge in computer science. All modules are designed so that the students can study online material, interact with their peers and tutors in learning activities, and complete guided practical exercises to ensure a well-rounded learning experience that is inclusive and will allow students to demonstrate, and get feedback on their understanding and progress. A variety of online and offline activities, will be used to stimulate and support the completion of the practical exercises. Through interactions with their peers and tutors, such as discussion and the sharing of solutions to real world problems, the students will become conversant in the conventions of computer science. Prior to commencing the programme, the students are given an online induction into the programme. The induction resources include self-study modules on academic skills. Following this, the students have the opportunity to seek further support on their academic writing, mathematics, statistics and numeracy, library and information skills from the University’s study skills service.
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iv) Please explain how the design of the programme enables students to progress through to the end of the award? For example, in terms of the development of research skills, enabling students to complete an independent study module, developing competence and confidence in practical skills/ professional skills, [add link to QAA masters characteristics document].
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The defined programme learning outcomes (PLOs) make clear the standard students should achieve in terms of knowledge and skills by the end of the programme. The PLOs are mapped across the modules as Module Learning Outcomes, and this relationship will be made explicit to the students in the module pages in the VLE. From the solid base provided by the Algorithms and Data Structures module, students will be in a position to monitor their development of knowledge and skills as they complete each of the modules. The students will be able to identify their practical and skills as they progress through the modules. The Research Methods and Research Proposal modules will facilitate the systematic development of their research skills in a supported environment. These will prepare the students for their Independent Research Project (IRP) which they will complete at the end of the programme.
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v) How the programme learning outcomes develop students’ digital literacy and use technology-enhanced learning to achieve the discipline and pedagogic goals which support active student learning through peer/tutor interaction, collaboration and formative (self) assessment opportunities (reference could be made to such as blogging, flipped classrooms, response 'clickers' in lectures, simulations, etc).
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The programme is taught wholly online and thus offers many opportunities for the development of digital literacy skills and for using technology-enhanced tools to support learning. Tutors will provide students with a structured knowledge base on which to draw; these include text, worked examples, case studies and pre-recorded micro-lectures. Students will participate in a variety of tutor-supported activities which will provide them with the opportunity to reflect on, utilise and re-construct this knowledge in the light of their own work-based experience and discussions with and feedback from peers and tutors. Students will be given opportunities to apply their conceptual learning both to their own organisation and to other organisations in which they have an interest.Students will study in a Virtual Learning Environment (VLE) and will communicate and collaborate within the VLE using digital tools, for example online discussion forums, as well as being encouraged to make best use of third party and professional online communication tools to enhance their ability to operate as global computer science professionals. The completion of the Independent Research Project will develop their information literacy and digital scholarship skills as they will need to find, use, manage and evaluate online sources and other information. Students will have the opportunity to use a variety of module-specific tools and simulations to support their studies, for example for programming, analysis, modelling and testing. Assessments will be carried out online, with all assessments submitted in digital form.
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vi) How the PLOs support and enhance the students’ employability (for example, opportunities for students to apply their learning in a real world setting)?
The programme's employability objectives should be informed by the University's Employability Strategy:
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For working students there will be opportunities for them to reflect on the course content and find ways to apply what they have learned into the workplace. For students whose work context does not allow them to apply their learning, there are opportunities to address their knowledge to case-based scenarios (see below. The Independent Research Project in particular gives student the opportunity to work on a problem specifically related to AI ina workplace setting.

Employability will be supported through the development of key employability skills which run throughout the course. In particular, PLO1, PLO2, PLO3, PLO4 and PLO7 are concerned with the foundational and technical knowledge and skills they will acquire, with PLO2, PLO3 and PLO4 specifically concerned with practical skills in this regard. Practical exercises and case studies are designed closely based on real-world industrial problems, and the students will get the chance to, in part or fully, analyse, design, implement and test their solutions in a practical environment. Presentation of complex concepts in various forms (online discussion forums, reports, blogging) will develop the personal and social skills required in future employment. In particular, PLO5 and PLO6 are concerned with such skills, including communication, independence and creativity.
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vii) Consultation with Careers
The programme proposal should be discussed with Careers. Please contact your Faculty Employability Manager.
Please provide details of Careers' comments and your response.
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Careers and Placements already offer online support to distance learners on other programmes and have experience working with students in employment, studying for professional development or for a possible career change. Through guidance on the careers web pages and online resources as well as guidance given by personal supervisors, students will be aware and have access to support that is available from Careers and Placements. Students on this programme will have access to one to one careers support virtually through the usual booking system.
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viii) How is teaching informed and led by research in the department/ centre/ University?
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The programme has been aimed to be a ‘conversion’ course, and so its PLOs are aligned with the current state of art in computer science and AI research. Some modules contain a fairly standard core computer science curriculum, with the specialist modules being informed by staff members' with research specialisms in these areas. Modules are delivered by a team of tutors, experience in research and the teaching of computer science. In addition, final-year ISMs will be supervised by staff with experience in managing research projects, and interests or research experience in the proposed project area.

Each module provides students with the key concepts, frameworks, and relevant literature in the discipline area supported by guided reading which draws on academic literature. Across the programme students will be required to engage with and synthesise relevant research based literature and to consider how this material is relevant to their own practice.

A module team being responsible for reviewing and refreshing this material each time the module runs to maintain the currency of material. Programme leaders will work with module teams to plan and discuss the module and review research-based content to ensure that it remains up to date and reflects current debates in the subject area. The programme reflects the departmental research theme in Analytics, and Staff members' with research specialisms in theis area will provide consultation and review of modules prior to going live. All students will be subject to research-based teaching as part of their programme..
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5.d. Progression
For masters programmes where students do not incrementally 'progress' on the completion of a discrete Postgraduate Certificate and Postgraduate Diploma, please summarise students’ progressive development towards the achievement of PLOs, in terms of the characteristics that you expect students to demonstrate at the end of the set of modules or part thereof.
This summary may be particularly helpful to students and the programme team where there is a high proportion of option modules and in circumstances where students registered on a higher award will exit early with a lower one.

Note: it is not expected that a position statement is written for each masters PLO, but this can be done if preferred.
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On completion of modules sufficient to obtain a Postgraduate Certificate students will be able to:
If the PG Cert is an exit award only please provide information about how students will have progressed towards the diploma/masters PLOs. Please include detail of the module diet that students will have to have completed to gain this qualification as an exit award.
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Students exiting with the Postgraduate Certificate will have made some progress towards PLOs 1,2,3,4,6 and 7. They will be able to apply computer thinking to address computational problems and tools to analyse, design and implement them. They will be able to communicate computational problems in writing and operate as responsible Computer Science professionals.

Students exiting with the PG Certificate award will have completed the Algorithms and Data Structure module and successfully completed taught modules which total at least 60 credits (from the 135 credits taught modules including research methods). As the modules that a student will have completed will vary, the PG Certificate has the generic title of Computer Science rather than one that mirrors the title of the full Masters programme.
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On completion of modules sufficient to obtain a Postgraduate Diploma students will be able to:
If the PG Diploma is an exit award only please provide information about how students will have progressed towards the masters PLOs. Please include detail of the module diet that students will have to have completed to gain this qualification as an exit award.
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Students exiting with the Postgraduate Diploma award will be able to:
1) Apply computational thinking to big data problems, using skills in analysis, design and implementation of computing systems, drawing on the foundations of data analytics and computer science and the current research literature. [Computational thinking]

2) Analyse a big data problem from a written description, derive requirements and specifications from an understanding of problems, and create and/or justify designs to satisfy given requirements, applying knowledge of machine learning, data analytics, data mining and system analysis and design. [Evaluation and Synthesis]

3) Build computer-based systems to serve the needs of users, making effective use of the variety of physical implementations on which that software may be running, and applying the theory and practice of programming and software engineering. [Software and hardware; Users]

4) Critically evaluate and effectively apply data mining tools and algorithms for use to address a computational problem including big data, underpinned by a knowledge of how those systems work [Application]

5) Communicate computational problems and their solutions in written format to technical and non-technical professional colleagues, in a clear and organised manner and using compelling and convincing arguments drawn from relevant evidence. [Communication]

6) Operate as responsible Computer Science professionals, by maintaining awareness of key legal and ethical issues and risk management. [Professionalism]

Students exiting with the Postgraduate Diploma will have completed the Algorithms and Data Structure module and successfully completed taught modules which total at least 120 credits (from 135 credits taught modules including research methods). The 120 credits will include 3-4 data analytics related modules such as Artificial Intelligence and Machine Learning, Advanced Programming, Big Data Analytics, and Data Mining and Text Analysis. The PG Diploma is therefore titled Computer Science with Data Analytics.

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5.e. Other features of the programme
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i) Involvement of partner organisations
Are any partner organisations involved in the delivery of the programme?
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Please Select Y/N: Noif Yes, outline the nature of their involvement (such as contributions to teaching, placement provision). Where appropriate, see also the:
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University guidance on collaborative provision
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ii) Internationalisation/ globalisation
How does the programme promote internationalisation and encourage students to develop cross-cultural capabilities?
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All modules are set in a global context. The programme is accessible to a broad range of students from across the world as location is not a barrier to participation. As the programme is accessible to students from across the world it is expected to attract many nationalities and cultures. Interacting with these students, in a supported on-line environment, will help broaden their understanding of international perspectives and improve their cross-cultural communication skills.
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iii) Inclusivity
How will good practice in ensuring equality, diversity and inclusion be embedded in the design, content and delivery of the programme?
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This refers to the protected characteristics and duties on the University outlined in the Equality Act 2010
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The learning will be delivered through an accessible learning platform and be designed specifically to cater for learners with flexible learning requirements. The mode of on-line only delivery used for these programmes gives learners control over time, place, and payment options for learning and will enable many learners to access Masters level education who would not otherwise have been able, allowing them to fit their study around family, work, health and other commitments.
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6. Reference points and programme regulations
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6.a. Relevant Quality Assurance Agency benchmark statement(s) and other relevant external reference points
Please state relevant reference points consulted (e.g. Framework for Higher Education Qualifications, National Occupational Standards, Subject Benchmark Statements or the requirements of PSRBs): See also Taught Postgraduate Modular Scheme: Framework for Programme Design:
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The QAA Masters degree charateristics particularly catagory 2 was used as a reference point for the design of PLOs. The QAA subject benchmark statement for Master's Degrees in Computing was also used as a reference point, in particular section 2, 5 and 7 were used to inform PLOs and content design.
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6.b. University award regulations
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The University’s award and assessment regulations apply to all programmes: any exceptions that relate to this programme are approved by University Teaching Committee and are recorded at the end of this document.
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7. Programme Structure
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7.a. Module Structure and Summative Assessment Map
Please complete the summary table below which shows the module structure and the pattern of summative assessment through the programme.

IMPORTANT NOTE:
If the structure of your programme does not fit the usual academic year (for instance students start at the beginning of September or in January) please contact your Academic Quality Team contact in the Academic Support Office for guidance on how to represent the structure in an alternative format.

To clearly present the overall programme structure, include the name and details of each individual CORE module in the rows below. For OPTION modules, ‘Option module’ or 'Option from list x' should be used in place of specifically including all named options. If the programme requires students to select option modules from specific lists by term of delivery or subject theme these lists should be provided in the next section (7.b).

From the drop-down select 'S' to indicate the start of the module, 'A' to indicate the timing of each distinct summative assessment point (eg. essay submission/ exam), and 'E' to indicate the end of teaching delivery for the module (if the end of the module coincides with the summative assessment select 'EA'). It is not expected that each summative task will be listed where an overall module might be assessed cumulatively (for example weekly problem sheets).

Summative assessment by exams should normally be scheduled in the spring week 1 and summer Common Assessment period (weeks 5-7). Where the summer CAP is used, a single ‘A’ can be used within the shaded cells as it is understood that you will not know in which week of the CAP the examination will take place. (NB: An additional resit assessment week is provided in week 10 of the summer term for postgraduate students. See Guide to Assessment, 5.4.a)
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Full time structure
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CreditsModuleModules will be delivered one by one on a 7 week carousel. Assessment will take place at the end of week 8. Algorithms and Data Structures must be taken as the first module. With the exception of Research Methods, the Research Proposal and the Independent Research Project modules can be delivered in any order (after Algorithms and Data Structures has been completed). Any one module will therefore have students enrolled from a number of different cohorts.
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CodeTitle1234567812345678123456781234567812345678
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15Algorithms and Data StrcturesSEA
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15Advanced ProgrammingSEA
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15Computer Architecture and Operating SystemsSEA
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15Artificial Intelligence and Machine LearningSEA
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15Software EngineeringSEA
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15Research methodsSEA
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15Computer and Mobile NetworksSEA
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Data Mining and Text Analysis
SEA