On our Data Analytics MSc you'll be introduced to subjects including data mining, statistical modelling, business intelligence and data visualisation. The course has been developed with direct input from industry experts who’ll present you with real-life business cases as part of your work-related learning. By the end of the MSc degree, you’ll be ready to apply for rewarding roles in the data science and big data industries, as well as the many sectors and organisations that increasingly require data analysts.
Our Data Analytics MSc degree has been accredited with partial CITP status by BCS, The Chartered Institute for IT. This accreditation is a mark of assurance that the degree meets the standards set by BCS. As a graduate of this course, accreditation will also entitle you to professional membership of BCS, which is an important part of the criteria for achieving Chartered IT Professional (CITP) status through the Institute.
The course has also achieved partial CENG status by BCS on behalf of the Engineering Council. Accreditation is a mark of assurance that the degree meets the standards set by the Engineering Council in the UK Standard for Professional Engineering Competence (UK-SPEC). An accredited degree will provide you with some or all of the underpinning knowledge, understanding and skills for eventual registration as an Incorporated (IEng) or Chartered Engineer (CEng).
Some employers recruit preferentially from accredited degrees, and an accredited degree is likely to be recognised by other countries that are signatories to international accords.
The course can also prepare you for the industry-recognised Oracle Professional Certification.
This postgraduate course will equip you 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, you’ll be well prepared for a career in data analytics upon graduation.
The modules on this course have been developed with the help of industry professionals, some of whom will be present to teach you in specific classes. These experts will help you explore advanced techniques in data science. You'll study specialist subjects including financial mathematics, statistical modelling and forecasting, as well as having the chance to develop your own unique piece of work in the MSc Project module.
You’ll also be trained to use specialist software tools and environments currently used by professionals in industry. For example, you’ll have access to R and Python programming, IBM SPSS, SAS®, Tableau, Oracle and Hadoop. Familiarity with these tools will greatly enhance your employability upon graduation.
Our skilled professionals will expose you to real-life business case scenarios. For example, past students have worked on data warehouse projects with Lloyds Bank. This ensures you’ll develop practical skills and become familiar with the kind of challenges you could face in data analyst roles.
The course is assessed in a number of ways including written reports, practical and research assignments, demonstrations, presentations, group work and examinations.
You will be required to have:
To study a degree at London Met, you must be able to demonstrate proficiency in the English language. If you require a Tier 4 student visa you may need to provide the results of a Secure English Language Test (SELT) such as Academic IELTS. For more information about English qualifications please see our English language requirements.
If you need (or wish) to improve your English before starting your degree, the University offers a Pre-sessional Academic English course to help you build your confidence and reach the level of English you require.
The modules listed below are for the academic year 2020/21 and represent the course modules at this time. Modules and module details (including, but not limited to, location and time) are subject to change over time.
Year 1 modules include:
This module explores fundamental concepts for analysing and visualising data. The module covers descriptive statistics for exploratory data analysis, correlation analysis and linear regression model. Graph and text data analysing techniques for web and big data and reporting the results and presenting the data with visualisation techniques are also discussed. A substantial practical element is integrated into the module to enable students to apply data analysis and visualisation techniques for real world data analytical problems.
This module provides an appreciation of data mining concepts, techniques, and process for business Intelligence. It covers data mining techniques for both supervised learning (decision tree, logistic regression and neural network models) and unsupervised learning (cluster and association analyses).
It is designed to help equip the students with practical skills in applying data mining techniques in a modern business environment.
The module provides an introduction to relational data modelling and multidimensional data modelling techniques for data analytics. It enables students to acquire skills in advanced SQL and OLAP operations (OLAP cube, rollup, drill-down, slice and dice and pivot). The module is designed to help students with practical skills in preparing data for analysis which usually takes 50%-70% of data analytical project time. Big Data analytics platforms will also be introduced.
The module provides students with the experience of planning and bringing to fruition a major piece of individual work. Also, the module aims to encourage and reward individual inventiveness and application of effort through working on research or company/local government projects. The project is an exercise that may take a variety of forms depending on the nature of the project and the subject area. Particular students will be encouraged to carry out their projects for local companies or government departments.
Prerequisites: all course specific core modules
Assessment: 100% coursework (project viva is compulsory for all students)
The module aims to encourage and reward individual inventiveness and application of effort. It also aims to allow students:
- To have an opportunity to assimilate the knowledge they gained in their course and extend this knowledge to new area of application.
- To apply newly acquired knowledge and techniques to a specific problem using established research techniques and methods.
- To determine the framework of the project according to a set of specifications relevant to the subject of study.
- To manage an extended piece of work by confining the problem within the constraints of time and available resources.
- To research effectively the background material on the topic using a variety of sources and to develop ability to conduct critical analysis and draw conclusions.
- To develop the ability to produce detailed specifications and design frameworks relevant to the problem of investigation in the subject related to the industry.
- To demonstrate the originality in the application of new knowledge and skills.
- To effectively communicate the work to others by means of verbal and appropriate documentation techniques.
- To raise awareness in potential business development opportunities in an area pertinent to the topic.
This module develops students’ foundation of programming principles through the introduction of application programming for data analytics. The module covers common programming data structures, flow controls, data input and output, and error handling. In particular, the module places emphasis on data manipulation and presentation for data analysis. A substantial practical element is integrated into the module to enable students to use a programming language (e.g. Python) to prepare data for analysis and develop data analytical applications.
This module will introduce students to modern statistical modelling techniques and how those techniques can be used for prediction and forecasting. Throughout the statistical environment and software R will be used in conjunction with relevant statistical libraries.
The module will, introduce modern regression techniques (including smoothing), discuss different model selection techniques (including the classical statistical hypothesis) and how those techniques can be used for prediction purpose.
This module provides an introduction to some of the key mathematical methods used in financial calculations and how they are applied to the valuation of projects in the presence of uncertainty. There will be a particular focus on Discounted Cash Flow and Real Options methods but also on recent developments in the field of project valuation.
Methods such as Monte-Carlo simulation for financial options valuation and the Capital Asset Pricing Model (CAPM) with the aim of optimising a portfolio will also be explored using real financial data.
The module enables students to undertake an appropriate short period of professional activity, related to their course at level 7, with a business or community organisation and to gain credit for their achievements. The activity can be a volunteering activity, employment activity, an activity within the Faculty of Computing Virtual Business Environment (VBE), placement or business start-up activity. For the purpose of this module – the FOC VBE will be also be recognised as ‘an employer’.
It is expected student should work for 200 hours which should be recorded clearly (in a learning log for instance) in the portfolio. The 200 hours can be completed in a FT mode, or spread over a semester in a PT mode.
Students should register with the module leader to be briefed on the module, undergo induction and work related learning planning and to have the work related learning agreement approved, before they take up the opportunity. It is essential that students are made aware that both the “work related learning agreement” and relevant “health and safety checklist” where applicable need to be approved before starting the placement.
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.
Please note, in addition to the tuition fee there may be additional costs for things like equipment, materials, printing, textbooks, trips or professional body fees.
Additionally, there may be other activities that are not formally part of your course and not required to complete your course, but which you may find helpful (for example, optional field trips). The costs of these are additional to your tuition fee and the fees set out above and will be notified when the activity is being arranged.
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You are advised to apply as early as possible as applications will only be considered if there are places available on the course.To find out when teaching for this degree will begin, as well as welcome week and any induction activities, view our academic term dates.
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Professor in Computer Science, Head of Intelligent Systems Research Centre