PMDATANA - MSc Data Analytics

Teaching and learning strategies and methods are organised around direct contact time with the teaching team and tutor-directed activities. The direct contact time takes place through lectures, seminars, workshops and tutorials. Information is conveyed through various mediums and methods such as case studies, group work, problem-based learning, and project work. Students are expected to complement these 'formal' sessions with:

• the reading and critical appreciation of material suggested in teaching sessions;
• individual and group work on research and development projects;
• regular practice of software tools to gain proficiency;
• individual directed research on current developments in the subject area.

The teaching and learning of cognitive skills is mainly based on lectures, seminars, workshops and tutorials, which will be organised around various activities such as case studies and problem-based learning scenarios.

Many activities are structured to encourage students to carry out independent work prior to meetings with lecturers and to cooperate and coordinate with their peers. Increasingly throughout the course students will also be required to engage in research using a variety of materials to analyse, assimilate, theorise, criticise and evaluate a variety of data analytics-related issues.

Since data analytics projects are generally team and project-based, these skills are emphasised throughout the course. Critical thinking is a vital constituent of any postgraduate course and lectures, seminars and tutorials provide an opportunity for students to develop this skill. Also important is the ability to arrive at alternative, practical solutions to a given problem; workshops provide an opportunity for students to develop some of these skills required by industry. Students are expected to behave throughout the course as though they are part of a data analytics project team and, as such, are expected to acquire and practice employability and professional skills in that context.

Practical skills learning is an inherent part of each and every module in the course – students are given the opportunity to practice subject-specific practical skills in workshops based in suitably equipped computer laboratories. The practical data analytics skills using a range of developmental hardware and software platforms are developed through tutor-guided workshop exercises, sometimes using peer assistance and evaluation. Students are encouraged to exert individual effort to acquire these practical skills with guidance from tutors. Practical skills are developed progressively throughout the course and culminate (and are assessed) in the final project.

The course is a specialist advanced course whose aim is to develop graduates who are equipped with the theoretical, technical and practical competencies required in this current area of economic growth. The course curriculum content has been developed with direct input from pertinent industry experts (in particular from within the Bank of England) and utilizes specialist software tools and techniques. Students’ experience of the course will be enriched with exposure to real life business case scenarios brought to them by skilled professionals currently employed as data analysts in the Bank of England.

The course aims to provide students who already possess a good honours degree in any discipline that has an element of data analysis, or have substantial relevant industrial experience, an opportunity to study and practice at postgraduate level in the emerging area of data analytics.

The main aims of the course are:

• To provide students with knowledge and skills for designing, building, analysing and evaluating applications/systems/services for the analysis of big data.
• To further develop the intellectual skills of reasoning, problem solving, decision-making, self-expression and independent study, thereby enabling students to deal with complex issues both methodically and creatively.
• To offer students an opportunity to develop advanced expertise in a specialist field relevant to their skills and professional career aspirations.
• To undertake a substantial individual project which utilises current and up-to-date software platforms and tools.
• To further students commitment to professional ethics and enthusiasm for the data science profession, and to prepare them for advanced studies and for employment as data analytics professionals.

LO1: Knowledge and Understanding
On completing the course students will be able to:

• demonstrate a deep understanding of the relevancy and applicability of data analytics – both from a technical and an end-user perspective;
• demonstrate a critical appreciation and good working knowledge of the process of designing, developing, analysing, evaluating and presenting data analytics solutions;
• demonstrate a high level of comprehension in choosing and applying analysis and visualisation methods and tools;
• apply research skills and methods to current areas within the field.
• demonstrate an ability to plan, execute and report on system development and on project evaluation.
• develop competence in areas of problem-solving, troubleshooting, working within teams, communication, decision making, self-management and self-presentation as applicable to the world of work.
• evaluate the ethical, social, legal and professional issues involved in developing and deploying data analytics solutions.

LO2: Cognitive skills
By the end of the course the student is expected to develop higher order skills that are reflected in
the student’s ability to:
• carry out independent scholarly and practical research and investigation. In particular, an ability
to use such knowledge to provide analysis and evaluation of specific issues and problems related
to the development and management of data analysis;
• develop and apply intellectual and critical skills to the theories and ideas related to the
synthesis, development and evaluation of data analytics solutions;
• detect and resolve issues related to the deployment, maintenance and evolution of data analytics;
• carry out evaluation and comparison of a range of technological offerings related to
data analysis and visualisation in modern organisations;
• carry out a critical review of the literature and be aware of alternative approaches to big
data analysis.

LO3: Transferable skills including those of employability and professional practice
The most useful practical skills, techniques and capabilities are to:
• act as an intermediary between technical specialists and user groups;
• communicate ideas and information effectively by oral, written and visual means;
• work effectively both in a team and independently on a given task or project;
• take a trouble-shooting, problem-solving approach to existing projects;
• think critically by questioning given information, testing hypotheses, formulating policy
suggestions;
• apply effective time-management and self-management skills.

LO4: Subject-specific practical skills
On completion of the course students will be able to:
• plan and management data analytical project with cross-industry standard process;
• understand user requirements for data analytics based on business and data understanding;
• collect and prepare data for analysis with programming and database tools;
• analyse data in depth with statistics, data analytical and visualising tools for pattern discovery;
• develop appropriate predictive data models for business forecasting and knowledge discovery;
• implement business forecasting model with stochastic modelling techniques;
• develop the use of appropriate testing and evaluation techniques;
• develop data analytical application deployed from data analytical models;
• report modelling results and present the data with visualisation techniques;
• be competent communicators of complex ideas and analysis by oral, written and visual means.

The programme design has been informed by the “Subject Benchmark Statement Master’s Degrees in Computing” 2011.
http://www.qaa.ac.uk/en/Publications/Documents/SBS-Masters-degree-computing.pdf

Assessment is undertaken by a variety of formative and summative assessment methods, including:

• in-class tests;
• unseen examinations;
• individual and/or group work on research projects;
• individual and/or group work on case studies;
• the reporting of design and development work;
• demonstrations and presentations;
• the compilation of workbooks;
• the authoring of a Personal Development Plan.

Formative assessment such as report writing, presentations, group work and in-class seminar work will assess and provide feedback for improvement of cognitive skills acquired throughout the course. Summative assessment such as unseen examinations will provide concrete evidence of the level that these cognitive skills have been learnt.

As students progress through the levels of study they will be confronted with more complex cognitive skill assessment such as research reports and components of the final project.
Contextualised, realistic coursework scenarios which generally require team work (including development of team leadership skills), role play, interviews, presentations and client requirements elicitation would form the bulk of assessment of these transferable skills. In data analytics projects the process of development is as important as the final outcome and many of these professional and employability skills are highlighted in the way the student works on a project, as opposed to the final result of the project. The process of project development is facilitated by the tutors and assessed formatively and summatively.

Demonstrations and presentations of developed systems or analytic results provide a useful mechanism for assessing the practical skills acquired by students. Where these demonstrations (as in the final project) are regular, formative assessment and feedback opportunities are beneficial.

An optional opportunity is embedded into the course structure for students to take a work-related learning module.

Students are required to take 6 taught modules (5 cores and 1 designate), and the MSc Project as specified in the course structure (see details in Section 27). None of the taught modules requires a prerequisite, and may be taken in any order, subject to timetable constraints. The MSc Project would normally be undertaken following the successful completion of the six taught modules.

PG Diploma is awarded following the successful completion of any combination of modules to the value of 120 points.

PG Certificate is awarded following the successful completion of any combination of modules to the value of 60 points.

Students are encouraged to write blogs/log books to record, and to reflect on, what they have learnt each week, and to maintain a personal development portfolio.

Formative feedback is provided during the semester so that students are able to show draft work to lecturers in seminars and workshops in order to refine and enhance their work before final submission.

Initial careers information will be given by the course leader who will direct students to relevant information sites/sources.

Further career information will be provided by external industrial associates during the course who will direct students to relevant information sites/sources.

The Faculty's World of Work (WOW) Agency offers opportunities to enhance employability skills, gain real experience and 'earn while you learn' through participation in real client-driven projects - working with business and industry.

Student membership of the British Computer Society will be encouraged. Students will benefit from attending the subject-specific events held in London throughout the year. Here opportunity will exist to participate in cutting-edge lectures and seminars and to network across the UK professional community. Student membership of the Society of Data Miners will be encouraged where similar opportunities exist.

Professional contacts with specialist expertise will be drawn upon in order to broaden the student experience.

Upon completion of the course, you’ll be well equipped to work in some of the fastest growing sectors of the data science and big data industries. A wide range of career opportunities will be open to you in the commercial, public and financial sectors, especially in areas requiring big data analysis such as consumer, healthcare, scientific, financial, security intelligence, business and social sciences.

Job roles you could apply for include data scientist, data analyst, digital analyst, big data consultant, statistical analyst and data modeller. You’ll be eligible to work in a multitude of areas where skills such as R or Python programming, machine learning and statistical modelling, SAS® and SPSS experience, data visualisation and data-driven decision-making are required.

The course also provides you with an excellent basis for further study if you want to pursue a higher-level research degree or embark on an industry-based research career.

You will be required to have:

• a 2:2 UK degree (or equivalent) in any discipline that involves an element of data analysis (applicants with relevant professional experience will also be considered)

All applicants must be able to demonstrate proficiency in the English language. Applicants who require a Tier 4 student visa may need to provide a Secure English Language Test (SELT) such as Academic IELTS. For more information about English qualifications please see our English language requirements.