UDD MEng Comp Sci including YinI 2018 PDD

	A	C	F	G	I	K	L	M	N	O	P	Q	R	S	T	U	V	W	X	Y	Z	AA	AB	AC	AD	AE	AF	AG	AH	AI	AJ	AK	AL	AM	AN
1	Programme Information & PLOs
2	Title of the new programme – including any year abroad/ in industry variants
3	MEng in Computer Science (and 'with a year in industry' variant)
4	Level of qualification
5	Please select:				Level 7
6	Please indicate if the programme is offered with any year abroad / in industry variants																				Year in Industry Please select Y/N								Yes
7																					Year Abroad Please select Y/N								Yes
8	Department(s): Where more than one department is involved, indicate the lead department
9	Lead Department		Computer Science
10	Other contributing Departments:		N/A
11	Programme Leader
12	Dr Chris Power
13	Purpose and learning outcomes of the programme
14	Statement of purpose for applicants to the programme
15	The MEng in Computer Science produces multi-skilled highly competent graduates who are equipped to become leaders in 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 two integrated strands of work which help you to develop both your computational thinking and your skills as an engineer. It is the combination of these two areas that will make you attractive to employers, enabling you to make an immediate contribution when you move into employment. By choosing the Integrated Masters (MEng) programme, rather than a Bachelors (BSc/BEng), you will have the opportunity to study a larger number of optional modules, allowing a broader exploration of the discipline, and to work on a larger final-year project, enabling greater depth of independent study in an area that you have chosen yourself. The programme will provide you with a solid foundation in the principles and practices of computer science, including coding, mathematics and basic engineering; with breadth in computer science and related technical disciplines; and with advanced training in focussed areas of your choice. This solid theoretical foundation will allow you to take full advantage of the new technologies and languages which are bound to appear during the course of your career. You will understand engineering trade-offs that cross disciplines, for example between hardware and software, and you will be able to participate effectively in multidisciplinary teams. You will also develop the skill to contribute professionally to solving complex commercial and industrial engineering problems. The programme is accredited by both the Institution of Engineering and Technology (IET) and the BCS (the Chartered Institute for IT) – both professional bodies of computing and engineering.
16	Programme Learning Outcomes Please provide six to eight statements of what a graduate of the 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.
17	PLO	On successful completion of the programme, graduates will be able to:
18	1	Apply computational thinking to problems they encounter, using skills in problem analysis, representation and abstraction, and in algorithm selection, at different scales in complex situations, drawing on the foundations of computer science but with an awareness of current research issues and areas of commercial development. [Computational thinking].
19	2	Adapt to new technologies, languages, paradigms, terminologies and models as they become available, being confident to use cutting-edge techniques and tools in their practice, informed by self-directed study of current research and scholarship, and by awareness of open-source systems and tools. [Adaptability].
20	3	Design and build computer-based systems to serve the needs of users and the commercial imperatives of an employer, with the most appropriate combination of software and hardware, by applying the theory and practice of programming and software engineering, while making effective use of the variety of physical implementations on which that software may be running. [Software and hardware; Users].
21	4	Engineer solutions to problems in which computation forms a significant part and where information may be limited or incomplete, by using skills from the whole breadth of Computer Science across all parts of the development lifecycle, with deeper skills in chosen areas. [Engineering; Breadth and depth].
22	5	Make immediate and effective contributions as part of multidisciplinary teams in industry, consultancy or education, by organising themselves to manage workloads, optimise resources and meet deadlines, using experiences from team projects. [Team working].
23	6	Communicate and negotiate about complex computational problems and their solutions with specialist audiences and associated stakeholders in a clear and organised manner, with compelling and convincing arguments. [Communication].
24	7	Operate as responsible Computer Science professionals, by maintaining awareness of key legal and ethical issues, appreciating how computers and technology can impact on society and the importance of risk management, and by continuing to expand and deepen their knowledge through critical engagement with the discipline. [Professionalism}.
25	8	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 to communicate the results in a significant technical report or other appropriate medium. [Cutting-edge of of CS research and applications].
26	Programme Learning Outcome for year in industry (where applicable) For programmes which lead to the title ‘with a Year in Industry’ – typically involving an additional year – please provide either a) amended versions of some (at least one, but not necessarily all) of the standard PLOs listed above, showing how these are changed and enhanced by the additional year in industry b) an additional PLO, if and only if it is not possible to capture a key ability developed by the year in industry by alteration of the standard PLOs.
27	PLO2: Adapt to new technologies, languages, paradigms, terminologies and models as they become available, being confident to use cutting-edge techniques and tools in their practice, informed by self-directed study of current research and scholarship, by commercial awareness and by awareness of open-source systems and tools. [Adaptability]. PLO3: Design and build computer-based systems to serve the needs of users and the commercial imperatives of an employer, with the most appropriate combination of software and hardware, by applying the theory and practice of programming and software engineering, while making effective use of the variety of physical implementations on which that software may be running. [Software and hardware; Users]. PLO5: Make immediate and effective contributions as part of multidisciplinary teams in industry, consultancy or education, by organising themselves to manage workloads, optimise resources and meet deadlines, using experiences from team projects and appreciating how their own role relates to others and to the business of an employer or client. [Team working]. PLO9: Work to commercial standards by planning, implementing and monitoring their own work in relation to appropriate procedures and legislation. [Commercial standards].
28	Programme Learning Outcome for year abroad programmes (where applicable) For programmes which lead to the title ‘with a Year Abroad’ – typically involving an additional year – please provide either a) amended versions of some (at least one, but not necessarily all) of the standard PLOs listed above, showing how these are changed and enhanced by the additional year abroad or b) an additional PLO, if and only if it is not possible to capture a key ability developed by the year abroad by alteration of the standard PLOs.
29	n/a
30	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:
31	i) Why the PLOs are considered ambitious or stretching?
32	These PLOs are ambitious because they show how we expect our graduates to develop in many different ways. We teach both the theory and the practical application of computer science, and expect students to understand both the science and the engineering sides of the discipline. 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. Our graduates can communicate with a range of stakeholders and we expect them to work effectively in multidisciplinary teams. It is not easy to achieve all of these outcomes, and our graduates are well-prepared for employment. For Integrated Masters students, the additional PLO (PLO8) shows how we expect our graduates to be working at the cutting-edge of the discipline.
33	ii) The ways in which these outcomes are distinctive or particularly advantageous to the student:
34	The insistence that all our graduates need to have a basic grounding in both hardware and software is distinctive, and we are also keen to ensure that our graduates know the principles on which the discipline is based, rather than necessarily being experts in the latest technology (which may well have become outdated within a few years). Our graduates will be able to apply these principles to new technologies in the years ahead. Many of the option modules taken in later years reflect the particular research interests in the department, such as non-standard (quantum, evolutionary) computation or artificial intelligence or embedded systems. PLO5 reflects the prominence given to team-working throughout the programme: we expect our graduates to be able to work in teams, as this is likely to be a vital skill in their later careers.
35	iii) How the programme learning outcomes develop students’ digital literacy and will make appropriate use of technology-enhanced learning (such as lecture recordings, online resources, simulations, online assessment, ‘flipped classrooms’ etc)?
36	Graduates of this programme have been immersed in digital activities throughout, and we expect them to become not just consumers of digital resources but also creators. Technology-enhanced learning: departmental policy is that lecture capture is the default, unless there are specific reasons not to, such as Intellectual Property. All modules have VLE sites where resources such as lecture notes and recordings are stored, along with any module-specific tools, simulations etc. Where appropriate, assessments are carried out online, with all open assessments submitted in digital form.
37	iv) 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 employablity objectives should be informed by the University's Employability Strategy:
38	http://www.york.ac.uk/about/departments/support-and-admin/careers/staff/
39	Support for employability starts from stage 1, where the SKIL module explicitly looks at CVs, skill requirements for particular jobs and desirable competences on graduation. Throughout the programmes, industrial case studies are used, and several modules (eg SEPR and GPIG) base teamwork projects on scenarios from industrial clients.
40	vi) How will students who need additional support for academic and transferable skills be identified and supported by the Department?
41	In stage 1, the SKIL module uses small tutorial groups for teaching. Since much of the module content concerns academic and transferable skills, these small groups are ideal for identifying those in need of extra support, which will be provided by the supervisor, with assistance from specialised central services where appropriate.
42	vii) How is teaching informed and led by research in the department/ centre/ University?
43	Although stages 1 and 2 contain a fairly standard core curriculum, the option modules available in stages 3 and 4 are often based on staff members' research specialisms. In addition, final-year ISMs are mostly proposed by supervisors and arise from current research interests.
44	Stage-level progression Please complete the table below, to summarise students’ progressive development towards the achievement of PLOs, in terms of the characteristics that you expect students to demonstrate at the end of each year. This summary may be particularly helpful to students and the programme team where there is a high proportion of option modules. Note: it is not expected that a position statement is written for each PLO, but this can be done if preferred (please add information in the 'individual statement' boxes). For a statement that applies across all PLOs in the stage fill in the 'Global statement' box.
45	Stage 1
46	On progression from the first year (Stage 1), students will be able to:											apply basic computational thinking to straightforward problems; to understand and apply the mathematical principles underlying computing; to understand the foundations of electronics, systems architecture and programming as used in computer systems; to work as an individual and in a team; and to produce short reports and presentations.
47	Stage 2
48	On progression from the second year (Stage 2), students will be able to:											apply more sophisticated computational thinking to larger problems; to compare programming paradigms and apply the most appropriate; to work effectively in teams; to understand engineering tradeoffs in system development; to communicate with a variety of audiences in a range of formats.
49	Stage 3
50	(For Integrated Masters) On progression from the third year (Stage 3), students will be able to:											use specialised knowledge from a variety of option modules to engineer solutions to problems in which computation forms a significant part; to adapt to new technologies and languages by transferring understanding of previously-studied computational principles.
51	Programme Structure
52	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. ‘Option module’ can be used in place of a specific named option. If the programme requires students to select option modules from specific lists these lists should be provided in the next section. 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 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). If summative assessment by exams will be scheduled in the summer Common Assessment period (weeks 5-7) 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.
53	Stage 1
54	Credits	Module				Autumn Term										Spring Term										Summer Term
55		Code		Title		1	2	3	4	5	6	7	8	9	10	1	2	3	4	5	6	7	8	9	10	1	2	3	4	5	6	7	8	9	10
56	20	COM00003C		Human Aspects of Computer Science (HACS).			S								A					EA
57	20	COM00009C		Foundation in Electronics, Signals and Circuits (FESC).													S										A		E			A
58	15	COM00001C		Introduction to Computer Architecture (ICAR).			S										A												E			A
59	20	COM00005C		Mathematical Foundations of Computer Science (MFCS).			S									A													E			A
60	5	COM00008C		Skills, Knowledge and Independent Learning (SKIL).			S							A									E	A
61	10	COM00006C		Numerical Analysis (NUMA).													S												E			A
62	20	COM00007C		Theory and Practice of Programming (TPOP).			S																						E	A		A
63	10	COM00010C		Programming of Micro-controllers (PROM).																				S					E	A
64	Stage 2
65	Credits	Module				Autumn Term										Spring Term										Summer Term
66		Code		Title		1	2	3	4	5	6	7	8	9	10	1	2	3	4	5	6	7	8	9	10	1	2	3	4	5	6	7	8	9	10
67	10	COM00013I		Implementation of Programming Languages (IMPL).													S											E		A
68	20	COM00014I		Systems (SYST).													S											E		A	A
69	20	COM00005I		Principles of Programming Languages (POPL).			S																E		A							A
70	10	COM00002I		Computability & Complexity (COCO).			S								E	A
71	20	COM00001I		Artificial Intelligence (ARIN).													S											E				A
72	10	COM00009I		Vision & Graphics.			S								E	A
73	30	COM00012I		Embedded Systems Project (EMPR).			S																E		A			A
74		OR		OR
75	30	COM00008I		Software Engineering Project (SEPR).			S					A						A				A			E			A				A
76	Stage 3
77	Credits	Module				Autumn Term										Spring Term										Summer Term
78		Code		Title		1	2	3	4	5	6	7	8	9	10	1	2	3	4	5	6	7	8	9	10	1	2	3	4	5	6	7	8	9	10
79	40	COM00015H		Project: Computer Science (PRBX).													S											EA
80	10	COM00030H		Project Management for Computer Scientists (PMCS).			S								EA
81	10	COM00022H		Analysable Real-Time Systems (ARTS).			S								E	A
82	10	COM00031H		Design of Analysable Real-Time Systems (DART).													S												E			A
83	10	COM00027H		Computer Vision (CVIS).													S												E			A
84	20	COM00003H		Embedded Systems Design & Implementation (EMBS).			S							A						A					E				A
85	10	COM00026H		Computing by Graph Transformation (GRAT).													S												E			A
86	10	COM00024H		Information & Coding Theory: General Aspects (ICGA) (not running 2018/9).			S								E	A																A
87	10	COM00025H		Information & Coding Theory: Practical Aspects (ICPA) (not running 2018/9).													S												E			A
88	10	COM00029H		Introduction to Neural Networks (INNS).													S								A				E
89	20	COM00009H		Multi-Agent Interaction and Games (MAIG).			S																		E							A
90	10	COM00028H		Fundamentals of Machine Learning (FUML).			S								E	A
91	10	COM00032H		Machine Learning & Probabilistic Graphical Models (MLPG).													S									A			E
92	10	COM00023H		Data-Oriented Specifications & their Analysis (DOSA).			S								E	A
93	10	COM00020H		Concurrent System Analysis & Verification (CSAV).													S												E			A
94	Stage 4
95	Credits	Module				Autumn Term										Spring Term										Summer Term
96		Code		Title		1	2	3	4	5	6	7	8	9	10	1	2	3	4	5	6	7	8	9	10	1	2	3	4	5	6	7	8	9	10
97	40	COM00138M		Group Project (Integrated Masters) (GPIM).													S												A				EA
98	10	COM00066M		Adaptive & Learning Agents (ALAS).																		S			E	A
99	10	COM00069M		Critical Systems (CRSY).			S			A		E				A
100	10	COM00071M		Evolutionary Computation (EVCO).			S			E								A