Data Science. DDataSc PGCert Data Science Intermediate awards available PGDip Data Science UCAS code. Final award

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1 Data Science Final award DDataSc PGCert Data Science Intermediate awards available PGDip Data Science UCAS code N/A Details of professional body accreditation N/A Relevant QAA Benchmark statements N/A Date specification last up-dated July 2014 Programme content The Professional Doctorate in Data Science is aimed at professionals who wish to enhance and/or validate data-centric, evidence-based approaches within their chosen career through a combination of taught modules and doctoral research. A cross-disciplinary approach is central to the delivery of this programme and is therefore suitable for professionals in a broad range of professional disciplines and areas of employment. According to Hal Varian, Google Chief Economist, The ability to take data to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it that s going to be a hugely important skill in the next decades. It is these skills and the knowledge at depth that underpins them that are the focus of this programme. The taught elements include: Data ecology Research Methods for Technologists Applied Research Tools and Techniques Spatial Data Analysis Analysing Qualitative Data Advanced Decision Making Work-based Project Review The doctoral research element will focus on pure or applied aspects of Data Science within the student s main discipline or area of employment. The programme is unique, internationally, in offering a Professional Doctorate qualification in Data Science. The taught modules on this programme are available to be taken as credit bearing short courses by suitably qualified individuals. Professional Doctorate in Data Science

2 London is at the heart of a rich global data landscape in which information extracted from data forms an important resource in knowledge and wealth production. On the one hand UEL is tied into this landscape through its geographical proximity to data-centric industries in the City, CanaryWharf and growing numbers of technology and creative industry start-ups in East London. On the other, UEL has a rich seam of research engagement with data-centric areas of central and local government, health, crime, education, finance, private and third sector organisations, and the London 2012 Olympics. Particular expertise is in novel approaches to analysis, issues of data quality, and information security. Entry requirements Applicants for Professional Doctorate are normally expected to hold an Honours Degree with no less than an upper second class honours (2:1) in any relevant field in science, socialscience and engineering disciplines. Alternatively, applicants can hold a degreequalification of an equivalent standard from a recognised university outside the U.K. Applicants holding an Honours Degree plus a recognised professional qualification canalso apply. Professional Doctorate applicants are expected to be in or have had relevant professional employment. At least two members of academic staff will review each application before a decision is made. Applicants with either prior-certified learning or prior-experiential learning that closely matches the specified learning outcomes of the taught part of the programme may be able to claim exemption via agreed university procedures. No exemption can be claimed against the research part of the programme or in situations where a professional body excludes it. Where English is not the applicant s first language, a minimum IELTS Academic English, or such qualifications as our University deems comparable, score of 7.0 overall, with a minimum of 6.5 in all components, is required at entry. Such assessment of English language competence must normally have been undertaken no more than two years prior to application, though relevant and more recent study in a United Kingdom Higher Education Institution may be accepted as sufficient proof of ability. At UEL we are committed to working together to build a learning community founded on equality of opportunity - a learning community which celebrates the rich diversity of our student and staff populations. Discriminatory behaviour has no place in our community and will not be tolerated. Within a spirit of respecting difference, our equality and diversity policies promise fair treatment and equality of opportunity for all. In pursuing this aim, we want people applying for a place at UEL to feel valued and know that the process and experience will be transparent and fair and no one will be refused access on the grounds of any protected characteristic stated in the Equality Act Programme structure Full / Part Time Professional Doctorate programme in Data Science Taught Modules (180 credits)

3 Credits Core/ Option Module Code Module Title 30 Core (D level) CN8002 Research Method for Technologists 30 Core CN7120 Data Ecology 30 Core (D level) CN8001 Applied Research Tools and Techniques Option CN7121 Spatial Data Analysis 30 (1 from 3) CN7122 Advanced Decision Making GSM016 Qualitative Data Analysis 30 Core DSM004 Work-based Project Review 30 Core CN7124 Planning for Doctoral Research 360 Core (D level) - Research Thesis Professional Doctorate students taking the taught modules in full-time mode take two 30 credit module in each semester. Professional Doctorate students taking the taught modules in part-time mode take one 30 credit module in each semester. Full- and part-time students can proceed to the doctorate research on successful completion of at least four taught modules including module SDD002.

4 Learning environment A variety of approaches is taken to learning either as full-time, part-time regular attendance or part-time block / blended learning modes. All of these provide engagement with experienced researchers in the field with a rich collection of case studies, techniques, software tools and data with which to illustrate and provide a vehicle for learning new concepts and skills. The School has its dedicated laboratory space for undertaking practical exercises that reinforce the material learnt through lectures and seminars. Specific facilities are available for students on the doctoral research phase, shared with the PhD students, so as to provide a peer group. The Library has extensive access to on-line research databases including IEEE, and the programme being based in London affords students the opportunity to engage with other Institutes and Professional Institutions, such as the Royal Statistical Society, which have regular meeting and seminars on topics relevant to this programme. Assessment All assessment of taught modules is by coursework. Relevance to work/profession The Professional Doctorate in Data Science is aimed at professionals who wish to enhance and/or validate data-centric, evidence-based approaches within their chosen career. The programme is not focused on any one profession, but at any profession where data and their informational derivatives are central to knowledge production, business models, decisionmaking, project and risk evaluation, and the development of policy. Students will be learning and researching with a multi-disciplinary cohort of peers who are professionally engaged with data. All the taught modules draw heavily on staff research and consultancy case studies with opportunities for students to inject their own professional experience. Two modules are work-based, that is, there is an expectation that they will be carried out in the student s professional setting. The doctoral research component is also expected to have relevance to the student s professional setting and career aspirations. Research/project work On successful completion of module SDD002 and three other modules from SDD001, DSM001, DSM002, DSM003, DSM004, DSM005 or GSM016, students can progress to the research component of the programme. The Research Thesis would normally not exceed a maximum of 60,000 words. It is rated as 360 credits towards the overall programme and represents two-thirds of the total credits to be achieved for the award of Professional Doctorate in Data Science. Registration of the research component can only take place following a recommendation from the relevant School Research Degrees Sub-Committee to the university Research Degrees Subcommittee of the suitability of the candidate to undertake research, of the programme of research, of the supervision arrangements and of the research environment. These approvals require appropriate academic judgement to be brought to bear on the viability of each research proposal.

5 Candidates for a Professional Doctorate must successfully complete all assessed elements of their programme before award of the degree can be made. The progression of all Professional Doctorate students throughout their registration period will be formally reviewed annually by a panel consisting of a minimum of two members of staff from the relevant School(s) with experience of research degree supervision and who are independent of the student s supervisory team. Students must be present at the review and may request that their supervisory team is also present. The continuation of enrolled status as a student is dependent on the successful completion of an annual review. The examination of the research component of the Professional Doctorate has two stages: firstly the submission and preliminary assessment of the research; and secondly its defence by oral examination. Added value The advantage of studying this programme is that it will uniquely qualify students in a field that is increasingly recognised as being central to most professional areas and for which job opportunities have been rising exponentially. Your future career Job opportunities in data science have risen exponentially in the last few years. Holders of a Professional Doctorate in Data Science will hold the highest possible qualifications in this area and prepare them for senior positions. Holders of this qualification will be eligible to apply for Fellow, Royal Statistical Society. How we support you At the commencement of the programme students are given an explicit overview of the total programme teaching and learning activities, assignments, organisation, structure and progression. An annually updated Programme Handbook helps guide the student through the Programme. The Programmer Leader along with the Research Degrees Leader and module leaders deal with the students day-to-day academic issues. In addition supervisor and personal tutors provide academic and personal support throughout the Programme. The University Counselling, Disability, Dyslexia and Student Support Services provide more specialist help. The GraduateSchool is responsible for providing a focus to the support of our postgraduate research students and for our institution s research and scholarly strategy. Professional Doctorate students will have at least two and not normally more than three supervisors, who together demonstrate an appropriate range of academic and professional experience. One supervisor shall be the Director of Studies with responsibility to supervise the candidate on a regular and frequent basis. Programme aims and learning outcomes

6 What is this programme designed to achieve? This programme is designed to give you the opportunity to: The overarching aims of the programme are: Develop knowledge and research skills in Data Science to empower you as a higher professional. Foster reflective and analytic approaches in work-based practice and research. Produce high-quality, international standard research output What will you learn? A candidate who is awarded a Professional Doctorate will be expected to have achieved the following learning outcomes: Created and interpreted new knowledge, through original research, or other advanced scholarship, of a quality to satisfy peer review, which extends the forefront of the discipline and merits publication; Systematically acquired an understanding of a substantial body of knowledge which is at the forefront of an academic discipline or area of professional practice; The general ability to conceptualise, design and implement a project for the generation of new knowledge, application or understanding at the forefront of the discipline and to adjust the project design in the light of unforeseen problems; A detailed understanding of applicable techniques for research and advanced academic enquiry; Ability to make informed judgements on complex issues in specialist fields, often in the absence of complete data, and be able to communicate their ideas and conclusions clearly and effectively to specialist and non-specialist audiences; Ability to continue to undertake pure and/or applied research and development at an advanced level, contributing substantially to the development of new techniques, ideas or approaches; The qualities and transferable skills necessary for employment requiring the exercise of personal responsibility and largely autonomous initiative in complex and unpredictable situations, in professional or equivalent environments. Knowledge Analyse and critically evaluate projects and research outputs in Data Science Engage in knowledge production through doctoral level research Have a critical understanding of and be able to engage with the data value chain in professional settings Thinking skills

7 Critical thinking and evidential reasoning Reflect on your professional and research practice Ability to make cross-disciplinary connections with other professionals and scientists Subject-Based Practical skills Using diverse data resources and sophisticated software tools in extracting information and value from data Plan, execute and evaluate Data Science projects Produce international level scholarly research Skills for life and work (general skills) Develop sophisticated data-centric skills Integrate research, and articulate research results into professional practice Respond positively and constructively to critical feedback Communicate complex ideas with other professionals and the public The programme structure Introduction All programmes are credit-rated to help you to understand the amount and level of study that is needed. One credit is equal to 10 hours of directed study time (this includes everything you do e.g. lecture, seminar and private study). Credits are assigned to one of 5 levels: 0 equivalent in standard to GCE 'A' level and is intended to prepare students for year one of an undergraduate degree programme 1 equivalent in standard to the first year of a full-time undergraduate degree programme 2 equivalent in standard to the second year of a full-time undergraduate degree programme 3 equivalent in standard to the third year of a full-time undergraduate degree programme M D equivalent in standard to a Masters degree equivalent in standard to a Doctorate degree Credit rating Professional Doctorate programmes come under an institutional credit framework details of which can be found in the Part 9 regulations and D-level module descriptor at:

8 Typical duration It is possible to move from full-time to part-time study and vice-versa to accommodate any external factors such as financial constraints or domestic commitments. Students do make use of this flexibility where it is available but this may impact on the overall duration of their study period. The normal minimum and maximum periods of registration for a Professional Doctorate are as follows: Minimum Maximum Full-time 33 months 48 months Part-time 45 months 60 months How the teaching year is divided Full-time Professional Doctorate programme in Data Science Year Semester Credits Core/ Option Module Code Module Title (D level) CN8002 Research Method for Technologists A CN7120 Data Ecology 1 (D level) CN8001 Applied Research Tools and Techniques B 30 Option CN7121 Spatial Data Analysis

9 (1 from 3) CN7122 GSM016 Advanced Decision Making Qualitative Data Analysis CN7123 Work-based Project Review C (Summer) CN7124 Planning for Doctoral Research 1 onwards Doctoral Research component resulting in submission of a thesis Part-time and Block Mode Professional Doctorate programme in Data Science Year Semester Credits Core/ Option Module Code Module Title A CN7120 Data Ecology 1 B CN8001 Applied Research Tools and Techniques (D level) C (Summer) CN7123 Work-based Project Review

10 Year Semester Credits Core/ Option Module Code Module Title A (D level) CN8002 Research Method for Technologists the doctoral process 2 B 30 Option (1 from 3) (M level) CN7121 CN7122 GSM016 Spatial Data Analysis Advanced Decision Making Qualitative Data Analysis 2 onwards C (Summer) (M level) CN7124 Planning for Doctoral Research Doctoral Research component resulting in submission of a thesis Block mode delivery of taught modules will normally be based on a one week intensive attendance at UEL Docklands according to an advertised calendar, usually at the beginning of each semester. During the remainder of the semester, students can work on their reading, practical components (from a workbook) and coursework with on-line help, supervision and group tutorials. Regardless of mode of delivery, it is expected that DSM004 Work-based Project Review and DSM 005 Planning for Doctoral Research will normally take place in the student s professional setting with on-line guidance and supervision. An introduction and orientation to these modules together with a progress review will take place at the start of each module at UEL Dockland Campus to an advertised calendar. All assignments and coursework will be submitted on-line through UEL-Plus and does not require student s to deliver hardcopies in person to the UEL Docklands Campus. What you will study when See Programme Structure and How the Teaching Year is Divided above

11 Requirements for gaining an award In order to gain a Postgraduate Certificate, you will need to obtain 60 credits at Level M or above. In order to gain a Postgraduate Diploma, you will need to obtain 120 credits at Level M or above. In order to gain a Professional Doctorate, you will need to obtain 540 credits comprising: 120 credits at level M from taught modules, 60 credits at level D from taught modules, and 360 level D credits from doctoral research. Further information For further information and response to queries, please contact Dr Yang Li y.li@uel.ac.uk, Professor Allan Brimicombe a.j.brimicombe@uel.ac.uk or the administrator, Linda Day l.day@uel.ac.uk Teaching, learning and assessment Teaching and learning List here the key teaching and learning methods used. In order to demonstrate that you have covered the learning outcomes it may be useful to sub-divide this as follows Knowledge is developed through Reading the research literature Critical presentation and discussion of key concepts and techniques in lectures Undertaking lab-based practical exercises Undertaking research Thinking skills are developed through Reading and evaluating the research literature Engaging in classroom discussions and in preparing coursework Undertaking research Practical skills are developed through Undertaking lab-based practical exercises Undertaking research Work-based learning Skills for life and work (general skills) are developed through Managing the learning process on the programme Planning for doctoral research Communicating complex ideas and techniques

12 Assessment List here the assessment methods that you use. Once again, in order to demonstrate that all learning outcomes are assessed, it might be helpful if you use one of the approaches suggested above. Examples of forms of assessment include coursework, presentations, and case studies. All the learning outcomes of the programme are assessed through: Laboratory session portfolios Coursework Research thesis How we assure the quality of this programme Before this programme started Before this programme started, the following was checked: there would be enough qualified staff to teach the programme; adequate resources would be in place; the overall aims and objectives were appropriate; the content of the programme met national benchmark requirements; the programme met any professional/statutory body requirements; the proposal met other internal quality criteria covering a range of issues such as admissions policy, teaching, learning and assessment strategy and student support mechanisms. This is done through a process of programme approval which involves consulting academic experts including some subject specialists from other institutions. How we monitor the quality of this programme The quality of this programme is monitored each year through evaluating: external examiner reports (considering quality and standards); statistical information (considering issues such as the pass rate); student feedback. Drawing on this and other information, programme teams undertake the annual Review and Enhancement Process which is co-ordinated at School level and includes student participation. The process is monitored by the Quality and Standards Committee. Once every six years an in-depth review of the whole field is undertaken by a panel that includes at least two external subject specialists. The panel considers documents, looks at student work, speaks to current and former students and speaks to staff before drawing its conclusions. The result is a report highlighting good practice and identifying areas where action is needed.

13 The role of the programme committee This programme has a programme committee comprising all relevant teaching staff, student representatives and others who make a contribution towards the effective operation of the programme (e.g. library/technician staff). The committee has responsibilities for the quality of the programme. It provides input into the operation of the Review and Enhancement Process and proposes changes to improve quality. The programme committee plays a critical role in the quality assurance procedures. The role of external examiners The standard of this programme is monitored by at least one external examiner. External examiners have two primary responsibilities: To ensure the standard of the programme; To ensure that justice is done to individual students. External examiners fulfil these responsibilities in a variety of ways including: Approving exam papers/assignments; Attending assessment boards; Reviewing samples of student work and moderating marks; Ensuring that regulations are followed; Providing feedback through an annual report that enables us to make improvements for the future. The external examiner reports for this programme are located on the UEL virtual learning environment (UELPlus or Moodle) on the School noticeboard under the section entitled External Examiner Reports & Responses. You can also view a list of the external examiners for the UELSchool by clicking on the link below: Listening to the views of students The following methods for gaining student feedback are used on this programme: Use of module feedback forms Student representation on programme committees (meeting 3 times year) Statistical information on student performance on modules and progression Annual review by independent panel during the doctoral research phase Students are notified of the action taken through: Circulating the minutes of the programme committee A newsletter published twice a year Providing details on the programme web pages Listening to the views of others

14 The following methods are used for gaining the views of other interested parties: Feedback from former students Annual student satisfaction questionnaire (PRES) Industrial liaison committee Liaison with professional bodies Where you can find further information Further information about this programme is available from: The UEL web site The student handbook Module study guides UEL Manual of General Regulations UEL Quality Manual Regulations for theacademic Framework The Graduate School The Schools website The Centre for Geo-Information Studies website The Programme Leader website