Faculty of Engineering and Computing. Programme Specification for MSc Data Science and Computational Intelligence ECT104

Transcription

1 V 10 Faculty of Engineering and Computing Programme Specification for MSc Data Science and Computational Intelligence ECT104 Coventry University 1

2 MSc Data Science and Computational Intelligence Contents Introduction Part 1: Programme Specification 1-10 Basic Programme information 2 11 Educational Aims of the Programme 5 12 Intended Learning Outcomes 5 13 Programme Structure and Requirements 7 14 Support for Students and their Learning 8 15 Criteria for Admission Methods for Evaluation and Enhancing the Quality and Standards of Teaching and learning Regulation of Assessment Indicators of Quality and Standards Additional Information List of mandatory and core option modules Curriculum Map Capabilities (Skills) Map 15 Part 2: Supporting information Relationship to the national Qualifications Framework, Subject Benchmarks and Professional/Statutory Body requirements. Teaching and Learning Strategy 17 Assessment Strategy 18 Programme/Course management 19 Entry Requirements and Selection Procedures 19 Compliance with the University s Academic Regulations and Current Legislation 19 V 10 Page 17 2

3 Introduction V 10 Data science refers to understanding, processing, extracting value from and representing very large amounts of data. It requires multidisciplinary skills including traditional computer science, mathematics, statistics combined with business aptitude, curiosity and entrepreneurship. Computational Intelligence (CI) is a branch of computer science studying problems for which there are no effective computational algorithms. Computational Intelligence approaches mimic human information processing and reasoning mechanisms as well as other biologically inspired processes such as evolution and collective intelligence found in many natural systems. The main areas of Computational Intelligence are Neural Networks, Evolutionary Computation and Fuzzy Systems. The focus of the proposed MSc Data Science and Computational Intelligence Programme is on applications of data science methods and tools combined with Computational intelligence techniques to the analysis, interpretation and visualisation of complex data. The Department of Computing has considerable expertise in the above areas as evident by the research projects carried by the Applied Computing Research Centre and the numerous research publications. The proposed MSc programme is expected to attract around 20 students per year which hold 2.1 or 1 st BSc Computer Science or related degree. The programme will provide students with the knowledge and understanding of the underlining theory of data science and CI methodologies and practical experience in applying these to real life problems in business, finance, security and bioinformatics. The unique selling point of the programme will be the delivery through activity and problem based led learning in the context of current real research or industry consultancy projects conducted by the academics teaching on the course. The best students will be invited to apply for PhD study or research projects taking place in the department. The programme will equip students with strong analytical and problem solving skills, ability to apply computational intelligence methods, algorithms and techniques to real data applications. There have been many publications predicting that the demand for specialists capable of analysing data to help businesses to make decisions will reach more than a million by 2018 (e.g. Report by McKinsey Global Institute, 2011). It is also emphasised that data science, computational intelligence and machine learning will drive a new wave of innovation. Successful data and computational intelligence applications require specialist knowledge and skills not readily available on the market. The course will provide the opportunity to individuals with the right background to acquire such skills and meet the demand for experts in these areas. 3

4 Part 1: Programme Specification for MSc Data Science and Computational Intelligence V 10 1 Available Award(s) and Modes of Study Title of Award * Mode of attendance* UCAS Code FHEQ Level* Master of Science (MSc) Data Science and Computational Intelligence/ Fallback awards: Postgraduate Diploma Data Science and Computational Intelligence (Pg Dip)/ Postgraduate Certificate Data Science and Computational Intelligence (Pg Cert) Computational Intelligence F/T 1 year, P/T day release 2 years 2 Awarding Institution/Body * Coventry University 3 Collaboration Not Applicable 4 Teaching Institution and Location of delivery* 5 Internal Approval/Review Dates Coventry University 6 Programme Accredited by* Not Applicable 7 Accreditation Date and Duration 8 QAA Subject Benchmark Statement(s) and/or other external factors * Date of approval*/latest review*: Feb/2013 Date for next review: 2019/20 Not Applicable Relevant Subject Benchmark statement(s): N/A 7 QAA subject benchmark statement for Master's degrees in computing, 2011, QAA386_Computing.pdf QAA for Higher Education statement on Masters programmes Much of the study undertaken at Masters level will have been at, or informed by, the forefront of an academic or professional discipline. Students will have shown originality in the application of knowledge, and they will understand how the boundaries of knowledge are advanced through research. They will be able to deal with complex issues both systematically and creatively, and they will show originality in tackling and solving problems. They will have qualities needed for employment in circumstances requiring sound judgement, personal responsibility and initiative, in complex and unpredictable professional environments. Extract from: The framework for higher education qualifications in England, Wales and Northern Ireland, 2008, FHEQ08.pdf Benchmarking standards for taught master degrees in Computing, CPHC and BCS, This programme satisfies the requirements of the Quality Assurance Agency for Higher Education as stated above and also complies with the University s Code of Practice for Academic and Professional Skills Development. 4

5 V 10 9 Date of Programme Specification * 10 Programme Manager/Course Tutor * March 2015 Dr Chrisina Jayne 11 Educational Aims of the Programme * The aims of the programme are to: Provide industry relevant programme that meets the needs of individuals wishing to pursue a career in research and development focused in data science and computational intelligence; To enable students to enhance their analytical, problem solving, critical communication and presentation skills in the context of their taught modules and develop the ability to analyse, evaluate and model complex problems involving large amounts of data To advance the skills and knowledge acquired through previous study and experience into cutting edge research and technologies and to enhance students' transferable and professional skills and, thereby, their employment prospects; To provide in depth knowledge and understanding of the computational intelligence area including specialist knowledge in neural networks, fuzzy logic, evolutionary computing, data and web mining, information retrieval and data science technologies and tools; To enable students to analyse and critique the central problems of current research in data science and computational intelligence; To enable students to operate as effective independent researchers and/or consultants in their chosen specialised aspect of the course; To provide relevant and topical subject content for personal professional development that promotes good ethical practice in the workplace, particularly relating to the application of data analysis and computational intelligent techniques to real world applications and projects including in business, finance, industry control, engineering, natural language processing, information retrieval and bioinformatics; To enable students to adapt to future changes in technology in relation to data science and computational intelligence areas. 12 Intended Learning Outcomes* 12.1 Knowledge and Understanding* On successful completion of the programme a student should be able to demonstrate knowledge and understanding of KU1 The fundamental principles and techniques of data science and computational intelligence KU2 Analysing complex, high-volume, high-dimensional, structured/unstructured data from varying sources KU3 The combination of theory and practical application of data science and computational intelligence methods and techniques KU4 Professional, legal, social, cultural and ethical issues related to data science, computational intelligence and an awareness of societal and environmental impact In addition for the MSc students should be able to KU5 The current research problems and cutting edge developments of data science and to computational intelligence areas KU6 A specialised advanced area related to computational intelligence developed through a substantial individual project The principal teaching, learning and assessment methods normally used to enable outcomes to be achieved and demonstrated are identified below. Teaching and Learning Assessment KU1 Lectures, computer laboratory sessions, Web-based learning, Examinations, Individual written 5

6 KU2 KU3 KU4 KU5 KU6 problems and activity led classes, seminars discussions and tutorials, guided independent study, project work guided independent study, personal supervision and support V 10 coursework, Practical assignments, inclass tests, portfolios of activities Dissertation 12.2 Cognitive (thinking) Skills* On successful completion of the programme a student should be able to CS1 Engage in a peer review process that involves the critical review of papers, proposals and IT solutions related to data science and computational intelligence areas and make sound recommendations CS2 Critically evaluate a range of possible options or solutions to address a sizeable data application and present a soundly reasoned justification for the final recommendation In addition for the MSc students should be able to CS3 Demonstrate competence, creativity and innovation in solving unfamiliar problems CS4 Communicate effectively outcomes from major projects to technical and non-technical audiences The principal teaching, learning and assessment methods normally used to enable outcomes to be achieved and demonstrated are identified below. CS1 CS2 CS3 CS3 CS4 Teaching and Learning Group discussions either seminar or Webbased, problem led classes and tutorials. Guided independent study, personal supervision and support Assessment Presentations, poster display, seminar papers, written projects, examinations, problem solving exercises Written reports, Dissertation, Presentations 12.3 Practical Skills* On successful completion of the programme a student should be able to PS1 Select and apply relevant knowledge and skills in big data applications using relevant tools and technologies PS2 Identify and make effective and systematic use of a range of suitable techniques for developing solutions to complex data and analytical problems In addition for the MSc students should be able to PS3 Plan and conduct an independent project or dissertation either to solve a real practical problem applying best practice or to pursue research in a specific specialised aspect PS4 Create relevant software solutions for data science and computational intelligence methods applicable to real world problems demonstrating business aptitude, curiosity and entrepreneurship The principal teaching, learning and assessment methods normally used to enable outcomes to be achieved and demonstrated are identified below. Teaching and Learning Assessment Lectures, laboratory sessions, Web-based learning, problem led Practical and written assessments, PS1 classes and tutorials, workshops, research design tutorials, dissertation presentations and production of a supervision and guided and self-directed study research plan and report Lectures, seminars, problem led classes, and tutorials. Written papers, exercises and PS2 presentations PS3, Supervised Project or dissertation, Library searches, web-based Formal project report, presentation 6

7 PS4 learning, class discussions or viva, demonstration of software product V Transferable Skills * On successful completion of the programme you should be able to TS1 TS2 TS3 TS4 TS5 TS6 TS7 retrieve and manipulate information communicate complex quantitative analysis, idea and results clearly and effectively solve problems and create software exercise initiative and personal responsibility make decisions in complex and unpredictable situations demonstrate the independent learning ability required for continuing professional development operate effectively in a variety of team roles and take leadership roles where appropriate Transferable/key skills are generally incorporated within modules and related to relevant assessments as appropriate. Self-directed learning forms an element of all modules and the necessity to work within tight deadlines is an essential requirement across the curriculum. The ability to communicate orally and in writing will be developed across the range of modules. The wide range of assessment techniques will ensure that students are given every opportunity to demonstrate your skills in these areas. 7

8 V Programme Structure and Requirements, Levels, Modules, Credits and Awards Mandatory Modules Credits M23COM Artificial Neural Networks 15 M24COM Machine Learning 15 M28COM M26COM M27COM M25COM M26CDE M37COM Evolutionary and Fuzzy Systems Intelligent Information Retrieval Business Intelligence and Big Data Processing Cloud Computing and Distributed Technologies Advanced Database Systems Research Methods in Computing M08CDE Project dissertation 60 Awards: MSc Data Science and Computational Intelligence: The full curriculum (180 credits). Pg Dip Data Science and Computational Intelligence: 120 credits comprising of the taught modules described in the programme of study. Pg Cert Data Science and Computational Intelligence: 60 credits comprising at least three mandatory taught modules described in the programme of study. 8

9 14 Support for Students and their Learning A comprehensive support and guidance system exists for all postgraduate students in the Faculty of Engineering and Computing. Support is via a network of University and Faculty provision. V 10 The Programme Manager or Course Tutor is available to advise students on academic and pastoral issues may refer students to many other Faculty or central support services of the University where necessary. The Faculty Registry is open weekdays from 8:30 until 18:00 and provides the main focus for administrative support matters. The Student Experience Enhancement Team within the Faculty aims to take a pro-active student- centred approach to identify scope for innovation and improvements to any part of the educational process. The Team is also available to help resolve problems affecting students progress, including financial problems, and to advise students and staff on student-centred matters. Additional postgraduate student support is provided by the Graduate and CPD Centre, based in the Jaguar Building and open until 20:00 weekdays. Support is provided for both full-time and part-time students during the daytime and in the evening. The Graduate Centre facilities include the Barclay Lounge and Snack bar. Many events are organised for postgraduate students through the Graduate Centre, including careers workshops, prestigious guest lectures and social events. Overseas students may take advantage of services and guidance offered by the International Office and EU students are supported by a team of academic advisers comprising the Faculty s External Relations Team. Other University agencies including Careers Guidance Services, Centre for Academic Writing and Disabilities Office are able to provide specialist advice as required by the Programme Manager and students. The Faculty of Engineering and Computing has a dedicated employability unit and provides regular talks from industry speakers as well as career advice to students. The Department of Computing organises yearly Computing show which gives the opportunity to students to present their work to industry professionals. There are also regular research seminars in the Faculty which are open to all postgraduate students. Students who are studying modules managed by other faculties within the University have access to appropriate teaching and learning resources available within those faculties. In such cases programme managers liaise with their counterparts in the faculties concerned so that they can provide advice to their students about operational requirements and procedures for the other faculty. Each student receives a Student Handbook containing information about the Faculty, as well as specific information about the programme and modules. There is a scheduled induction period at the start of each semester at which new students are briefed on regulatory matters, processes and procedures relevant to their studies. The aim is to familiarise new students with the structure and operation of their programme and the wider facilities of the University. Essential briefings are provided into aspects such as Health and Safety. As soon as possible after enrolling, students are assigned an academic Personal Tutor, who invites a small group of tutees to an introductory meeting to discuss any problems arising from the arrival and induction process. Where possible the personal tutors are assigned from the team of teaching staff delivering postgraduate modules. Academic staff assigned as personal tutors receive regular guidance covering pastoral and academic support requirements. Module Leaders and associated Module Teams support student learning at module level. The assessment mode varies according to subject content and the nature of the intended learning outcomes. Formative assessment styles and feedback mechanisms are designed to develop skills and support student achievement. All module assessment is carried out in compliance with the University Assessment Strategy which has been developed to 9

10 ensure assessment is fair and equitable for students. V 10 Prior to the commencement of the master s project, individual supervisors, ideally with appropriate expertise or research experience, are assigned to help the students develop their project specifications. The Project Supervisor then normally assumes the role of Personal Tutor. For students who are undertaking projects in conjunction with external companies, the project supervisor normally facilitates meetings and liaises closely with the company and the student during the project period. For all types of project, the student and Supervisor agree regular contact times during the project in order to monitor progress and provide guidance. Additional support is provided to cover project supervision during the summer, to allow for academic staff absences for annual leave. This takes the form of a hotline for support and a postgraduate duty rota of academic staff from each department. The University places a high priority on providing equal opportunities for all students and has received national recognition for its work in assisting disabled students. Individual staff have been briefed and trained on their responsibilities in these areas and provide the basic assistance required in the academic work of a study module. A Faculty Learning Support Co-ordinator and a central University-wide Disabilities Office provide more specialised support. Reasonable adjustments can be made to the teaching, learning, assessment and support of the course(s) to maximise accessibility to students with disabilities. The University has an excellent record on widening access and welcomes students from all backgrounds on to its courses. 10

11 15 Criteria for Admission Applicants for this programme will normally be expected to possess a minimum of upper second class honours degree in Computer science, Mathematics or other relevant area. Students whose first language is not English should have achieved a minimum English language standard of IELTS 6.5. Alternatively, students may be admitted with IELTS 6.0 subject to successfully completing a compulsory five week pre-sessional English course, operated by Coventry University, before joining their master s programme. Admission of disabled students The University and the Faculty have always adopted a very positive approach to applications from students with disabilities. If possible students with disabilities are identified on application to the Faculty. All letters inviting students to interview or open days include a sentence asking disabled students to contact the admissions team to discuss any special needs they may have in advance of their visit. On receipt of an application from a disabled student, the Admissions Officer liaises with the student and the University Disabilities Office to arrange meetings to discuss requirements and to ensure an assessment of needs is undertaken. Where required the Programme Manager is involved at the earliest opportunity to ensure that all special operational requirements can be identified and satisfied in advance of arrival. Disabled students with special educational needs are encouraged to select their core options fat the earliest opportunity to allow academic staff sufficient time to prepare any bespoke teaching methods and materials. Disabilities Officer contacts all students who indicate on their enrolment form that they have a disability to offer support and guidance. The Faculty Learning Support Co-ordinator and the Programme Managers monitor and liaise about issues affecting enrolled students with disabilities. The Faculty Learning Support Co-ordinator liaises with the University Disabilities Office and provides guidance to staff and students on matters relating to study support. V 10 Accreditation for prior learning (APL) is in accordance with University regulations as set out in paragraph of the regulations for taught postgraduate courses. The accreditation for Prior Experiential learning (APEL) will only be awarded for achievements equivalent to masters level. Module exemptions can be given for prerequisites that have been achieved through previous study or experience. Normally alternative modules will be chosen from those available in the core options list. However appropriate alternative modules may be substituted, subject to the approval of the Programme Manager, who must ensure that the aims and learning outcomes of the named award are achieved by any variations in programmes of study. 11

12 16 Method for Evaluating and Enhancing the Quality and Standards of Teaching and Learning The Programme is managed by the Computing Board of Study of the Faculty/School of Engineering and Computing. The Programme Assessment Board (PAB) for Computing is responsible for considering the progress of all students and making awards in accordance with both the University and course-specific regulations. The assurance of the quality of modules is the responsibility of the Boards of Study which contribute modules to the programme. External Examiners report annually on the programme and their views are considered as part of the annual quality monitoring process (CQEM. Details of the CQEM process can be found on the Registry s web site. Students are represented on the Student Forum, Board of Study and Faculty/School Board, all of which normally meet two or three times per year. Student views are also sought through module evaluation questionnaires and student forums. All programmes are subject to a major review involving subject experts external to the University, normally on a five or six year cycle. At these reviews the views of employers and current and former students are sought where appropriate. V Regulation of Assessment University policy requires the internal moderation of all assessments. External Examiners are appointed for all named University awards. The role of the External Examiner at module level is to ensure that academic standards are in line with national norms for the subject. External Examiners undertake the moderation of examination papers and assessment tasks, and view representative samples of work for the modules for which they have responsibility. At programme level, External Examiners help to ensure fairness in the consideration of student progression and awards. They have the right to comment on all aspects of the assessment system and participate as full members of the assessment boards. The Pass mark for all modules is 40%. This overall module mark may comprise more than one component (e.g. coursework and exam). The individual module descriptors give the precise pass criteria and the weighting of the component marks that contribute to the overall module mark. Awards for Taught Master programmes may be made with Distinction or Merit (i.e. achievement of an average mark of at least 70% or 60% respectively). 12

13 V Indicators of Quality and Standards The following are key indicators of quality and standards: The programme has been designed in accordance with the QAA benchmark statements for Master's degrees in computing, 2011, and QAA for Higher Education statement on Masters programmes The programme has been designed in accordance with the benchmarking standards for taught master degrees in Computing, CPHC and BCS, This programme satisfies the requirements of the Quality Assurance Agency for Higher Education as stated above and also complies with the University s Code of Practice for Academic and Professional Skills Development. The Department has a strong portfolio of industry-related research evidence by the projects conducted by the Applied Computing Research Centre The Department has strong links with employers, enabling visits, guest lectures and joint projects with local companies; RAE 2008 results for the research in Computer Science and Informatics at Coventry University are: 5%World-leading, 20% Internationally Excellent, 50% International and 25% National levels. 86% of the students at Coventry University are satisfied with their experience (NSS 2012) 94% employability or further study rate (DLHE 209/10) The report of QAA s Institutional Audit undertaken in November 2008 confirmed that Confidence can be placed in the soundness of the institutions current and likely future management of the academic standards of its awards Confidence can be placed in the soundness of the institutions current and likely future management of the quality of the learning opportunities. 19 Additional Information Key sources of information about the course and student support can be found in Student Handbook Module Guides Module Information Directory CU Online Module Webs Postgraduate Programme Webs EC Faculty Postgraduate Web CU Portal Study Support information is accessible from student services home page Generic Faculty information is available on the EC Faculty Web Please note: This specification provides a concise summary of the main features of the programme and the learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if s/he takes full advantage of the learning opportunities that are provided. More detailed information on the learning outcomes, content, and teaching, learning and assessment methods of each module can be found in the Module Information Directory (MID), student module guide(s) and the course handbook. The accuracy of the information contained in this document is reviewed by the University and may be verified by the Quality Assurance Agency for Higher Education. 13

14 V Mandatory and Option Modules Module code Module title Credit value Pre/Co requisite M23COM Artificial Neural Networks 15 None M M24COM Machine Learning 15 None M M28COM Evolutionary and Fuzzy Systems 15 None M M26COM Intelligent Information Retrieval 15 None M M27COM Business Intelligence and Big Data Processing 15 None M M25COM Cloud Computing and Distributed Technologies 15 None M M08CDE Project dissertation 60 None M M26CDE Advanced Database Systems 15 None M M37COM Research Methods in Computing 15 None M Key M = Mandatory (i.e. must be studied and passed for the named award) O = Option MSc Data Science and Computation al Intelligence 14

15 21 Curriculum Map This diagram is used to map the intended learning outcomes against the mandatory and option modules. It is used to check that the students will have the opportunity to achieve the intended learning outcomes shown in section 12, regardless of which options are chosen. Intended Learning Outcomes Knowledge and Understanding Cognitive (Thinking) Skills Practical Skills Transferable Skills Module codes KU1 KU2 KU3 KU4 KU5 KU6 CS1 CS2 CS3 CS4 PS1 PS2 PS3 PS4 TS1 TS2 TS3 TS4 TS5 TS6 TS7 M23COM ANN M24COM ML M28COM FL_EC M26COM IR M27COM BI M25COM CCDS M08CDE Proj M26CDE M37COM MSc\Programme specification\ect104 Data Science and Computational Intelligence MSc.docx 15

16 22 Capabilities (Skills) Map In the grid below, the box where the module listed offers a significant opportunity to demonstrate the capability is annotated It includes taught (T), practised (P) and assessed (A) information regarding the stated intended outcomes and how they will be experienced/demonstrated by the student. Module codes Learning to Learn Working with others Problem Solving and Innovation Numeracy IT and Online Learning Communication Career Management Information Management M23COM ANN P TPA PA TPA TPA PA P M24COM ML P TPA TPA PA TPA TPA PA M28COM FL_EC P TPA PA TPA TPA PA M26COM IR P TPA TPA PA TPA TPA PA M27COM BI P TPA TPA TPA TPA P PA M25COM CCDS P TPA TPA TPA PA M08CDE Proj P PA PA PA PA PA PA TPA M26CDE P TP TPA TPA TPA TPA TP TPA M37COM P TP PA TPA TPA TPA TP TP Key: T=Taught, P=Practiced, A=Assessed The Code of Practice for Academic and Professional Skills Development requires that each of the capabilities be demonstrated at least once during the programme. Capability Outlines (from the Code of Practice for Academic and Skills Development) Personal Development Planning Learning to Learn Students should be ready to accept responsibility for their own independent learning. They should also be able to reflect on their learning and appraise their capabilities and achievements. Students should also be able to identify their individual needs for effective learning. Working with Others Students should be able to work effectively as part of a group, and respect the dignity, rights and needs of others. Problem Solving and Innovation Students should be able to use problem-solving skills in a variety of practical situations. They should be able to demonstrate creativity, flexibility, perception, decisiveness, confidence and an awareness of values. Numeracy Students should be able to interpret, analyse and present numerical data. IT and Online Learning Students should be able to use computer-based systems for learning, communicating, collaborating with peers and tutors, and working with data. Communication Students should be able to communicate effectively in appropriate forms in a wide variety of situations. Career Management Students should appreciate the values, culture, structure and process of work organisations relevant to their area of study. Students should also appropriately match their experience and academic achievements to employer expectations. MSc\Programme specification\ect104 Data Science and Computational Intelligence MSc.docx 16

17 Information Management Students should be able to carry out research relevant to their field of study by retrieving and using information drawn from a variety of resources. Personal Development Planning Students should be able to demonstrate self-awareness, set personal goals and record achievement. Capabilities developed through the Add+vantage Scheme In all full-time UK based undergraduate courses (with the exception of those that lead to a licence to practice), students will undertake at least one 10 credit Add+vantage module in each of the three years of their course. Theses Add+vantage modules will develop the following generic capabilities: Problem Solving Skills Action Planning and Organising Written and Oral Communication Questioning and Listening Employability competencies and career management skills will be introduced in each Add+vantage module. The following personal qualities related to employability will be addressed in each of the Add+vantage modules: Achievement orientation Initiative (Creativity) Self Confidence Decisiveness Reflectiveness Adaptability/Flexibility Influencing Career Management Skills MSc\Programme specification\ect104 Data Science and Computational Intelligence MSc.docx 17

18 Part 2: Supporting Information for MSc Data Science and Computational Intelligence This section explains the how and the why of what is described in the Programme Specification e.g. the rationale and/or strategy for a particular aspect of the programme. 1 Relationship to the National Qualifications Framework, Subject Benchmarks and Professional/Statutory Body requirements The programme has been designed in accordance with the QAA benchmark statements for Master's degrees in computing, 2011, and QAA for Higher Education statement on Masters programmes The programme has been designed in accordance with the benchmarking standards for taught master degrees in Computing, CPHC and BCS, These sources informed the design process when the aims and learning outcomes of the programme were devised. The completed design has also been reviewed against these essential requirements to ensure that the programme contains the main features expected. At present the course has not been presented for accreditation but there is a future option to present it to the British Computer Society for exemption from the educational requirements for Charter Scientist CSci. Even if the course was not accredited it is still possible for applicants to apply to the BCS on an individual basis. 2 Teaching and Learning Strategy The main strategies behind the teaching and learning methods employed include: To determine the teaching and learning methods on a module by module basis according to what was found from experience to be most effective. A number of modules on the programme are already running successfully on existing MSc programmes. To seek a diverse experience for students where they will meet a number of varied styles during the course of the entire programme. To include practical and project work that gives students the opportunity to develop technical and transferrable skills at the level expected from professionals working in associated industries and businesses. The Faculty educational philosophy of Activity-led learning will be embraced to support this. To work within the University imposed constraints for the operation of postgraduate taught programmes. The assessment regime ensures that students receiving the award will have broadly met all the expected learning outcomes. Specifically, the learning outcomes of the programme are mapped to specific modules (section 21 of programmes specification). In turn the learning outcomes of each module are mapped to assessment tasks. Hence, to receive the MSc degree a student must have achieved the expected learning outcomes to an acceptable level. The delivery of the programme has been informed by the general direction of the Faculty of Engineering and Computing to adopt an activity-led learning approach. With the move to activity led learning it is now delivered for most modules using 3 contact hours per week; 1 hour in the lecture room and 2 hours in a laboratory where students work on practical tasks and projects. If the module population requires it the laboratory session will run multiple times supervised by a Teaching Assistant. The module assessment normally has separate elements which require students to undertake individual or group practical assignments and evidence that they have completed them successfully. Many staff teaching on the programme are active in both technical and educational research/development projects. They have authored recent publications covering topics in Artificial Neural Network Applications, Fuzzy Logic, Information Retrieval, Distributed Systems and Cloud L:\REG\Quality Enhancement Unit\Quality\Approved courses\ec\active courses\pgt\com\data Science and Computational Intelligence 18

19 Computing. The staff take pride in being innovative and are always looking for ways to improve their teaching and assessment. The departmental assessment strategy has been designed to provide a reasonable work load for students. There is inevitably an assessment peak at the end of each postgraduate semester, but staff provide as much advanced notice of assessment requirements as possible often provided on week one of a module to ensure students have the best opportunity to plan their time effectively. To encourage student participation all modules make use of the University s virtual learning environment (CU online). Registers are also routinely taken and persistent non-attendees followed up by the departmental administrative team. To encourage personal development students are invited to attend relevant professional society events on campus and can participate in the Global Leader s programme offered by the Graduate and CPD Centre. 3 Assessment Strategy The programme s assessment strategy follows the Department of Computing assessment strategy which is in turn informed by the University s assessment strategy. This is implemented by requiring module leaders to comply with the strategy when they develop modules and present them for approval to the Board of Studies. Highlights from the strategy are repeated below for reference. This strategy is intended to provide guidance in setting module assessments. It is not intended to be prescriptive, or to prevent innovation, which is encouraged. Assessment methods within modules are approved by their associated Board of Studies. The assessments associated with a module must be as described in its descriptor which should give students an indication of what to expect from a module and how the module mark is calculated. Further detail may be provided in the module guide including the timing of assessments. Volume of assessment should be based on estimating the amount of time a student takes to complete it. The normally expected assessment load for a module is from 20% - 30% of the study hours for the module (30 to 45 hours for a 15-credit module). A rough guide to estimating hours is given in the following table. It is recommended to keep the assessment load as low as possible consistent with providing a sound assessment. Assessment Type Assessment Hours Unseen examination 10 hours per hour exam duration Class Test 10 hours per hour test duration Presentation 5 hours 1000 word essay 10 hours It is expected modules with examinations will only use exam durations of 2 or 3 hours. Modules with coursework and examination elements are expected to employ one of the following preferred formats: Coursework 50% and Exam 50% or Exam 100% All coursework should be issued to students with clear information on: learning outcomes covered, assessment criteria, due date (which should be reasonable), planned return date, submission arrangements, how the marks relate to the module mark, and details of the assignment task. Marked coursework should be returned in accordance with the University policy. Marks must be simultaneously posted on CU online. Feedback should be given to justify the mark awarded and assist students with their learning. There should be a timetabled opportunity for students to question the assessor about their results and seek clarification if necessary. In the event of a dispute over marks awarded the work may be referred to the moderator for a second opinion. Detailed marking schemes or worked solutions as appropriate should be prepared for all summative assessments. The preferred mechanism for distribution of coursework assessments, their collection, detailed feedback and return is via CU online. Software for detecting plagiarism should be used wherever possible along with electronic marking applications such as Grademark. Assessments should be designed to fully assess all learning outcomes associated with a module. The number of assessments to reliably achieve this should be kept as low as L:\REG\Quality Enhancement Unit\Quality\Approved courses\ec\active courses\pgt\com\data Science and Computational Intelligence 19

20 possible. All assessments should be moderated in accordance with Faculty Policy both before being issued to students and once marked, before return. Moderators are encouraged to question the challenge presented by an assessment and the fairness of its marking. Reassessment will normally be of the same form as the original assessment. It will cover the same learning outcomes and offer a similar challenge. In addition to summative assessment it is required that students are given formative tasks to support their learning. Formative assessments need not follow the same rigour as summative assessments. Examples could include: tutorial questions for which solutions sheets are issued, on-line assessments, etc. To aid student learning it is required there be at least one early assessment activity which completes by the mid-point of the module delivery. It is desirable that there be a variety of assessment experiences within a course and module. It is important that the total course assessment load is reasonable for students and module leaders will need to co-ordinate to ensure there are no significant overloads. It is recognised that group assessments presents particular challenges and require particularly careful design. There are additional Faculty Policies giving advice on group work. 4 Programme/Course Management Admissions to the programme will be managed in detail by the Graduate Centre and International Office in compliance with the guideline entry requirements set by the Department of Computing. Any problematic or borderline applications will be forwarded to the department admissions tutor or programme manager for detailed consideration. Operational responsibility for the management of the course rests with its designated programme manager. Where assistance is needed the programme manager can call on help from the associated departmental and faculty management structures. Students are formally represented in course management through the system of representatives and student forums operated through the student union. These operate in accordance with University guidelines. For effectiveness the meetings for this programme will be run alongside other department MSc programmes. 5 Entry Requirements and Selection Procedures The entry requirements are as described in Section 15 of the programme specification. Selection is not based on interview or any other exceptional procedures. 6 Compliance with the University s Academic Regulations and current legislation The programme has been designed to be fully compliant with the current University Academic Regulations and the numerous associated guidelines, policies and codes of practice. It is not possible to provide a formulaic approach to how the programme might be varied to suit students with disabilities as it requires individual reasonable adjustment. Section 15 in the programme specification outlines the general procedure that will be followed when an application is received from a disabled student. Template version May 2011 L:\REG\Quality Enhancement Unit\Quality\Approved courses\ec\active courses\pgt\com\data Science and Computational Intelligence 20