On our Data Analytics MSc you'll dive into vital subjects including data mining, statistical modelling, business intelligence and data visualisation. The course has been developed with direct input from industry experts who know the value of learning through real-life business cases. By the end of the master's 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. What's more, you'll get 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. As a partner of SAS® and Qlik Academic Programs, you'll have access to these revered tools, along with R and Python programming, IBM SPSS, Tableau, Oracle and Hadoop. Familiarity with these will greatly enhance your employability upon graduation.
Studying this Data Analytics MSc will see you gain exposure to key industry resources and real-life business case scenarios:
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:
Any university-level qualifications or relevant experience you gain prior to starting university could count towards your course at London Met. Find out more about applying for Accreditation of Prior Learning (APL).
To study a degree at London Met, you must be able to demonstrate proficiency in the English language. If you require a 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 2021/22 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.
The aims of this module are to:
• enable students to gain understanding of fundamental concepts in data analysis and visualisation,
• develop students expertise in data analysis with descriptive statistics, correlation analysis, and linear regression model,
• enable students to acquire knowledge of graph and text data analysing, and
• develop students with practical skills in applying data analysis and visualisation techniques for real world data analytical problems.
This module provides an appreciation of data mining and machine learning fundamental concepts, algorithms, and process. It covers machine learning algorithms and data mining techniques for data analysis, pattern mining, clustering, classification and regression. It equips the students with practical skills in applying data mining and machine learning techniques in real-world analytics problems.
The aims of this module are to:
• provide students with an understanding of data mining and machine learning fundamental concepts, algorithms, and process.
• understand the purpose and breadth of areas of application of data mining and machine learning
• understand and compare the techniques and tools available for various type of data analytics problems
• develop students with practical skills in applying data mining techniques to solve real-world analytics problems.
The module aims to strengthen students’ skills in data technologies ranging from database and data warehousing to Big Data. First, it will provide students with good understanding of database concepts and database management systems in reference to modern enterprise-level database development. Once gaining good skills in database development, students will be able to study and gain an in-depth understanding of data warehousing which include concepts and analytical foundations as well as data warehousing development. Through intensive hands-on sessions, the students will be able to get familiar with related technological trends and development in the field. the module will leverage a portfolio of SQL server tools that include SQL Server DBMS, SQL Server Integration Services (SSIS), SQL Server Reporting Services (SSRS) and SQL Server Analysis Service (SSAS) to provide hands-on experience in implementing a reporting solution through a combination of assignments and lab exercises.
The module introduces also the foundation of Big data management based on Apache Hadoop platform and provides students with a broad introduction to Big Data technologies. This will involve hands-on sessions, designed for data analysts, business intelligence specialists, developers, administrators or anyone who has a desire to learn how to process and manage massive and complex data to infer knowledge from data. Topics include Hadoop, HDFS, MapReduce, Spark, Sqoop, Hive, Pig and MLlib.
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)
Prior knowledge: Understanding of research management, planning and LSEP issues
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.
The aims of this module are to:
• enable students to gain understanding of programming principles,
• develop students’ knowledge and skills in programming design and coding,
• develop students expertise in data manipulation and presentation for data analysis,
• develop students with practical skills in data analytical applications development, and
• enhance students skills for integrative reasoning, problem-solving and critical thinking.
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.
Prior learning: Statistical knowledge desirable.
The module aims to:
1. Equip graduate students with modern statistical techniques
2. Provide students with some selected advanced statistics topics including forecasting
3. Prepare students to be able to read and understand professional articles
4. Prepare students to carry on their own research and use modern statistical techniques as one of the tools for their research.
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 aims to:
1. Provide students with a set of up-to-date mathematical tools for project valuation with a particular focus on financial applications.
2. Provide a foundation in modern developments in optimisation theory and methods and introduce essential topics of unconstrained and constrained optimisation.
3. Explore the applications of Capital Asset Pricing models to problems involving decision making in modern portfolio management using real world 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, or an activity within the University or its entrepreneurship facility, Accelerator.
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.
The module aims to provide students with the opportunity to:
• Gain a useful experience of the working environment and the career opportunities available on graduation.
• Undertake a work related project appropriate to their academic level.
• Enhance and extend their learning experience by applying and building on their academic skills and abilities by tackling real life problems in the workplace.
• Enhance professional and personal development.
"London Met has definitely shaped my next steps by providing me with the resources and understanding to become a confident and skilled analyst."
Maya Pun, Data Analytics graduate 2021. You can read about Maya's full experience to find out more.
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.
Use the apply button to begin your application.
If you require a Student visa and wish to study a postgraduate course on a part-time basis, please read our how to apply information for international students to ensure you have all the details you need about the application process.
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.
Please select when you would like to start:
Data Analytics MSc students Stephanie Healy Fabrega and Indrajitrakuraj Ravi have been employed on a research project.
Two students have been hired to work alongside London Met lecturer Sandra Fernando on a major data analysis project, giving them real-life work experience alongside their degrees.
Cyber Security student Dipo Dunsin has been hired to advance the project as part of London Met's commitment to providing students with valuable work experience for their future careers.
The prestigious accreditation scheme means the qualifications gained by London Met graduates are recognised by the computing industry for their high quality and rigorous standards.
Professor in Computer Science, Head of Intelligent Systems Research Centre