Course specification and structure
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PMDATANA - MSc Data Analytics

Course Specification


Validation status Validated
Highest award Master of Science Level Masters
Possible interim awards Postgraduate Diploma, Postgraduate Certificate
Total credits for course 180
Awarding institution London Metropolitan University
Teaching institutions London Metropolitan University, Aventis School of Management Pte Ltd, Singapore
School School of Computing and Digital Media
Subject Area Computer Science and Applied Computing
Attendance options
Option Minimum duration Maximum duration
Full-time 12 MONTHS 72 MONTHS
Part-time Day 18 MONTHS 72 MONTHS
Course leader  

About the course and its strategy towards teaching and learning and towards blended learning/e-learning

The course fosters pedagogical practice by using a wide range of tools and development platforms that engage students in the latest products and solutions. Students are encouraged to critically evaluate different data model solutions and statistical techniques in a variety of contexts. These activities provide students’ understanding and learning the required skills and knowledge for sector demand.

The teaching strategy is coupled with a theoretical approach with practical based learning providing a varied simulating experience. Assessments at different levels are aimed to introduce and develop key skills integral to academic success at higher levels, including writing, presentation, research, analytical, mathematical and technical skills. Students are given one-on-one training for assessment submission in early levels by providing opportunities to develop good academic practice. Weekly tutorials, workshop exercises, and formative assessments are designed to enhance their learning with applied differentiation techniques.

Module assessment typically consists of a combination of assessment types, including coursework, in-class tests, and unseen exams. Coursework can include solution modelling such as Data models, a Data Program code in addition to a written report/essay. The volume, timing, and nature of assessment enable students to demonstrate the extent to which they have achieved the intended learning outcomes. Formative feedback is provided during workshops and tutorial sessions. The support is personalised, stretches students' skills and understanding of subject matters, and promotes student engagement. The summative feedback often recorded electronically or on paper, is provided for coursework and other summative pieces of work, giving students opportunities to improve. This process enables students to develop independent or group/peer learning strategies identifying by their strengths and weaknesses.

Students are provided with opportunities to develop an understanding of, and the necessary skills to demonstrate, good academic practice. Particularly, students will be encouraged to complete weekly tutorial and workshop exercises as well as periodic formative diagnostic tests to enhance their learning. During tutorial and workshop sessions students will receive ongoing support and feedback on their work to promote engagement and provide the basis for tackling the summative assessments.

A range of assessment methods is employed throughout the course. Module assessment typically consists of a combination of assessment instruments including courseworks, in-class tests and formal examinations.

Coursework can include an artifact such as an output of dataset analysis, application of algorithms, data trends or program code in addition to a written report/essay. The volume, timing and nature of assessment enable students to demonstrate the extent to which they have achieved the intended learning outcomes.
Formative and summative feedback will be provided using a variety of methods and approaches, such as learning technologies, one to one and group presentation of the submitted work, at various points throughout the teaching period and will adhere to University policy regarding the timing of feedback.

Each module development and assessment design take account of the students' background and groups. In this process, we ensure that all aspects of learning, teaching and assessment are fair and accessible for all students, particularly minority and those with disabilities. In the module and assessment design process, students are involved in reviewing the content, marking criteria and delivery methods. We create awareness of professional body requirements from the start, so they prepare themselves according to what is required by the BCS and the industries. Students express their views through the module and course discussion board, emails, face-to-face discussions and student representatives. Students are encouraged to raise issues such as fairness of marking and allocations, the educational support process and accessibility of assessments.

The inclusive curriculum gives a variety of examples where the course considers and implements aspects such as enabling differentiated learning styles, personalization, development of real-life application of knowledge, self-evaluation and critical reflection.

The assessment map describes range of assessment types deployed across the levels and across the modules. The primary methods of assessments which assess learning outcomes in this practical course are predominantly coursework (with demonstrations), individual presentations of artefacts, group and development team coursework, and unseen formal examinations.

Course aims

Data analytics has evolved as one of the most promising and in-demand career paths for skilled professionals. This postgraduate course will equip students from a wide variety of disciplines with the theoretical, technical and practical skills required to become a data analyst. With an expert teaching team, access to specialist software and work on real-life business cases, Students will be well prepared for a career in data analytics upon graduation.

The course curriculum content has been developed with direct input from pertinent industry experts, some of whom will be present to teach students in specific classes. These experts will help you explore advanced techniques in data analytics. Students will study specialist subjects including financial mathematics, statistical modelling and forecasting, as well as having the chance to develop their own unique piece of work in the MSc Project.

The course caters for a diverse student population from our local communities and from across the globe. 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.

Course learning outcomes

The course learning outcomes have been designed to meet the requirements for certification of the British Computer Society and professional benchmarks in Computing. After completing the course the graduates are expected to

LO1: Knowledge and Understanding
On completing the course students will be able to:
1.1. demonstrate a deep understanding of the relevancy and applicability of data analytics – both from a technical and an end-user perspective
1.2. demonstrate a critical appreciation and good working knowledge of the process of designing, developing, analysing, evaluating, and presenting data analytics solutions
1.3. demonstrate a high level of comprehension in choosing and applying analysis and visualisation methods and tools

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:
2.1. carry out independent scholarly and practical research and investigation. An ability to use such knowledge to provide analysis and evaluation of specific issues and problems related to data analytics
2.2. develop and apply intellectual and critical skills to the theories and ideas related to the synthesis, development, and evaluation of data analytics solutions
2.3. carry out evaluation and comparison of a range of technological offerings related to data analysis and visualization in modern organisations
2.4. 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:
3.1. act as an intermediary between technical specialists and user groups and communicate ideas and information effectively by oral, written, and visual means
3.2. take a troubleshooting, problem-solving approach to existing projects
3.3. think critically by questioning given information, testing hypotheses, formulating policy suggestions
3.4. 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.

LO4: Subject-specific practical skills
On completion of the course students will be able to:
4.1. plan and management data analytical project with cross-industry standard process
4.2. understand user requirements for data analytics based on business and data understanding collect and prepare data for analysis with programming and database tools
4.3. develop appropriate predictive data models with machine learning algorithms and implement business forecasting model with stochastic modelling techniques
4.4. apply research skills and methods to current data analytics areas to develop data analytical models and applications
4.5. evaluate the ethical, social, legal and professional issues involved in developing and deploying data analytics solutions.
4.6. Demonstrate confidence, resilience, ambition and creativity and will act as inclusive, collaborative and socially responsible practitioners/professionals in their discipline.

Principle QAA benchmark statements

The programme design has been informed by the QAA Subject Benchmark Statement for Computing (Master’s) [October 2019]

https://www.qaa.ac.uk/docs/qaa/subject-benchmark-statements/subject-benchmark-statement-computing-(masters).pdf

Assessment strategy

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.

Organised work experience, work based learning, sandwich year or year abroad

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

Course specific regulations

Part-time structures may vary based on student’s choice. A student may take one or two modules per semester according to the available modules at the semester.

The course conforms to both the framework and University Academic Regulations in which no condonement or compensation is permitted.

Modules required for interim awards

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 22). 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.

All awards are only obtainable without condonement or compensation of any modules.

Arrangements for promoting reflective learning and personal development

The course includes two semesters of formal scheduled teaching for full-time study (and up to four semesters for part-time study) where students will learn their knowledge and skills for data analytics applications, followed by one semesters dissertation project for full-time mode of study (and one or two semesters for part-time mode) where they will apply the knowledge and skills learned to develop data analytical application deployed from data analytical models. During their study the students are encouraged to reflect on their learning by various means:

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.

Students are offered open-end assessments which are directly informed by research and real world projects driven by creativity and imagination to make students more creative and confident.

Students are invited to join research teams such the University’s re-scaling project, to take part in Big Idea challenging competition, and to publish research papers. Each module of the course exposes students to a variety of tools and the teaching and learning approach taken ensures that each student becomes confident in applying those tools.

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.

Students are also invited to apply their knowledge and skills to the analysis of one of the UK's largest data collection involving social, economic and population data provided by government departments and agencies to tackle key challenges which many communities face in UK to further develop their socially responsibility and become more confident and technically competent individuals, ready to take their place in the world.

Other external links providing expertise and experience

BCS, The Chartered Institute for IT

Career, employability and opportunities for continuing professional development

On completion of the course graduates will be well equipped to work in some of the fastest growing sectors of the data science and big data industries.

Successful completion of the course offers wide-ranging career opportunities in the commercial industry, public and financial services, especially in areas requiring big data analysis such as consumer, health-care, scientific, financial, security intelligence, business and social sciences.
Job roles include data scientist, data analyst, digital analyst, big data consultant, statistical analyst and data engineer.

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

Career opportunities

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.

Entry requirements

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)

Official use and codes

Approved to run from 2015/16 Specification version 1 Specification status Validated
Original validation date 11 Jan 2016 Last validation date 13 Jan 2016  
Sources of funding HE FUNDING COUNCIL FOR ENGLAND
JACS codes 100751 (information modelling): 80% , 100403 (mathematics): 20%
Route code DATANA

Course Structure

Stage 1 Level 07 September start Offered

Code Module title Info Type Credits Location Period Day Time
CC7182 Programming for Data Analytics Core 20 NORTH SUM MON AM
          NORTH SUM WED PM
          NORTH SPR THU PM
CC7183 Data Analysis and Visualization Core 20 NORTH AUT THU PM
CC7184 Data Mining and Machine Learning Core 20 NORTH SPR THU AM
          NORTH SUM FRI PM
          NORTH SUM FRI AM
CS7079 Data Warehousing and Big Data Core 20 NORTH AUT THU AM
FC7P01 MSc Project Core 60 NORTH SUM WED PM
          NORTH SPR WED PM
          NORTH AUT WED PM
MA7007 Statistical Modelling and Forecasting Core 20 NORTH SPR WED PM
          NORTH SUM WED AM
          NORTH SUM MON PM
FC7W03 Work Related Learning Option 20 NORTH SPR WED PM
          NORTH AUT WED PM
MA7008 Financial Mathematics Option 20 NORTH AUT WED PM

Stage 1 Level 07 January start Offered

Code Module title Info Type Credits Location Period Day Time
CC7182 Programming for Data Analytics Core 20 NORTH SUM WED PM
          NORTH SPR THU PM
          NORTH SUM MON AM
CC7183 Data Analysis and Visualization Core 20        
CC7184 Data Mining and Machine Learning Core 20 NORTH SPR THU AM
          NORTH SUM FRI PM
          NORTH SUM FRI AM
CS7079 Data Warehousing and Big Data Core 20        
FC7P01 MSc Project Core 60 NORTH SUM WED PM
          NORTH SPR WED PM
MA7007 Statistical Modelling and Forecasting Core 20 NORTH SPR WED PM
          NORTH SUM WED AM
          NORTH SUM MON PM
FC7W03 Work Related Learning Option 20 NORTH SPR WED PM
MA7008 Financial Mathematics Option 20