The MSc in Data Science is aimed at providing opportunities for students who wish to establish expertise and employment in data-centric, largely quantitative areas within a broad range of professional disciplines and areas of employment. A cross-disciplinary approach is therefore central to the delivery of the programme.

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 that underpins them that are the focus of this programme.

• The taught elements include:
• Data ecology
• Quantitative Data Analysis
• Spatial Data Analysis
• Analysing Qualitative Data
• Advanced Decision Making

The dissertation element will focus on pure or applied aspects of the Data Science research agenda and will provide an opportunity for students to apply and enhance their new knowledge and skills under expert guidance.

The taught modules on this programme are available to be taken as credit bearing short courses by suitably qualified individuals.

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.

Applicants are normally expected to hold an Honours Degree in any relevant field in science, social-science and engineering disciplines.

The applicants should hold a British award classification of no less than a lower second class honours (2:2). Alternatively, applicants should hold a degree qualification of an equivalent standard from a recognised university outside the U.K.

In the case of applicants whose first language is not English, then IELTS 6.5 or TOEFL 250/600 (or equivalent) is required. International qualifications will be checked for appropriate matriculation to UK Higher Education postgraduate programmes.

Students that apply to enter stages of the programme may be admitted through normal Accreditation of Experiential Learning (AEL) or Accreditation of Certificated Learning (ACL) processes, or through an approved articulation agreement. Therefore such applicants must be able to demonstrate and evidence that they have the required learning outcomes as listed in the modules for which they are seeking exemption.

Full-time MSc students take two 30 credit taught module in each of semesters A and B, and the research dissertation on completion of the taught modules. Part-time MSc students take one 30 credit taught module in each of semesters A and B, and the research dissertation on completion of the taught modules.

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.

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. 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.

All assessment of taught modules is by coursework.

The MSc in Data Science is aimed at students who wish to enhance and/or validate data-centric, evidence-based approaches within their chosen career, or enter the field and pursue a professional career in Data Science. 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, decision-making, project and risk evaluation, and the development of policy. Students will be learning and researching with a multi-disciplinary cohort of peers. All the taught modules draw heavily on staff research and consultancy case studies with opportunities for students to inject their own professional experience. The research dissertation component is also expected to have relevance to the student’s professional setting and/or career aspirations.

The research dissertation is a key element of this programme and it counts as a 60 credit module. Research topics are often developed from work based problems and can be supported by research collaborations with companies.

The objective of the dissertation is to develop the student's ability to study independently, making their own critical appraisal of the chosen subject and drawing conclusions therefrom.

Students will be required to demonstrate that the research undertaken has been completed to an appropriate level for a Masters award. The dissertation must therefore provide students with the opportunity to demonstrate:

• Their ability to apply practical and analytical skills present in the programme as a whole
• Innovation and/or creativity
• Synthesis of information, ideas and practices to provide a quality solution together with an evaluation of that solution
• That their project meets a real need in a wider context
• The ability to self-manage a significant piece of work

The research will be required to make a contribution to the understanding of the field studied and will be supervised by a member of staff with a research interest in the field.

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.

Job opportunities in data science have risen exponentially in the last few years. Holders of an MSc in Data Science will hold advanced qualifications in this area and prepare them for a professional or research career. Holders of this qualification will be eligible to apply for Fellow, Royal Statistical Society.

The School prides itself on its student support systems.

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 module leaders and dissertation supervisors deal with the students day-to-day academic issues. In addition personal tutors provide academic and personal support throughout the Programme.

The University Counselling, Disability, Dyslexia and Student Support Services provide more specialist help.

The programme benefits from teaching staff maintaining close links with industry and participating in international research projects.

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 research output through the dissertation

Knowledge

• Analyse and critically evaluate projects and research outputs in Data Science
• Engage in knowledge production through dissertation research
• Have a critical understanding of and be able to engage with the data value chain in professional settings

Thinking skills

• 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 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 typical duration of this programme is one year full-time or two years part-time.  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 but this may impact on the overall duration of their study period.

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.

All assignments and coursework will be submitted on-line through UEL-Plus and students are not required to deliver hardcopies in person to the UEL Docklands Campus.

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
• Preparing coursework

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

Modules are allocated a mark out of 100%. The pass mark for each module is based on an aggregate mark of 50%. The aggregate mark comprises marks from components whose threshold is 40%. Assessment may incorporate one, two or three components.

The module specifications specify the mode of assessment for each module.

All the learning outcomes of the programme are assessed through:

• Laboratory session portfolios
• Coursework
• Research dissertation

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.

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.

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 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 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

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

The following methods are used for gaining the views of other interested parties:

• Feedback from  former students
• Annual student satisfaction questionnaire
• Industrial liaison committee
• Liaison with professional bodies

Further information about this programme is available from:

•     The UEL web site http://www.uel.ac.uk
•     The student handbook http://www.uel.ac.uk/geo-information/courses/datascience
•     Module study guides http://www.uel.ac.uk/geo-information/courses/datascience
•     UEL Manual of General Regulations and Policies http://www.uel.ac.uk/qa/
•     UEL Quality Manual http://www.uel.ac.uk/qa/
•     Regulations for the Academic Framework http://www.uel.ac.uk/academicframework/
•     The Schools website http://www.uel.ac.uk/ace
•     The Centre for Geo-Information Studies website http://www.uel.ac.uk/geo-information
•     The Programme Leader website http://www.uel.ac.uk/geo-information/w-yli.htm

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