Online course details

Study options

Part-time: 3 years

UK fee

£8,550

International fee

£8,550

Course level

Postgraduate

Qualification

MSc

Start dates

January, September, May

Location

Online

Online course description

Big data is all around us. It is the vast amount of data that continues to be generated in line with our growing use of IT products and services. This constant growth creates exciting new career opportunities for big data specialists. Professionals who can identify and obtain intelligence from big data are in demand like never before.

Our online Big Data Analytics programme gives you an in-depth knowledge and critical understanding of the key issues and concepts in today’s data-driven business and science landscapes. You will develop powerful skills in the extraction, analysis and management of information from big data using a variety of scientific techniques and software tools.

As an online student, you will be able to draw on the extensive industrial links of our academic staff and share knowledge of best practice with fellow students, all while navigating your course materials in our virtual learning environment.

Real-world understanding

During your studies, you will address real-world industry-based problems. This intellectually demanding process requires not only specialist knowledge of big data analytics but also the ability to apply multi-disciplinary concepts to today’s dynamic business and scientific areas.

Natural language processing and text mining

Gain insight into industry relevant approaches to natural language processing (NLP) and text mining, developing the ability to extract and analyse various data sets. Relevant software tools will be used to enable a critical appreciation of computational, ethical and governance issues in this field, using the Vs of Big Data (volume, velocity, variety, veracity and value) as a critical framework.

Build your skill set around your career aspirations

This is a flexible programme which enables you to choose from highly relevant optional modules to tailor your programme of study to meet your particular interests or professional needs.

On this programme, you’ll develop a comprehensive skill set in computing and information technology, particularly in the areas of data analytics, business intelligence and information management, visualisation and assurance, as well as people analytics in HR. This will prepare you for a career in a rapidly growing industry more widely applicable and relevant than ever.

You can also study the Big Data Analytics MSc full-time or part-time on campus.

What you will study

By completing this programme, you will:

We will advise you of your study plan - the running order and availability of the modules - when you are invited to enrol. If we have insufficient numbers of students interested in an optional module, this may not be offered. In addition, where demand is high, some modules may be subject to a cap.

What you can achieve

The online Big Data Analytics MSc comprises three progressive stages: postgraduate certificate (PG Cert), postgraduate diploma (PG Dip) and masters (MSc). You will be required to achieve 60 credits to complete each award within the programme, totalling to 180 credits to achieve the masters.

You will be awarded a Postgraduate Certificate in Big Data Analytics if you pass the 20-credit core module Studying at Masters Level and Research Methods and two other 20-credit modules (adding up to 60 credits) from either the core modules or the optional modules.

A Postgraduate Diploma in Big Data Analytics can be obtained by passing the four 20-credit core modules and two of the 20-credit optional modules (adding up 120 credits).

A Master of Science in Big Data Analytics can be obtained by achieving the PG Dip and completing the independent study module worth 60 credits (180 credits).

We would encourage you to complete the full MSc but, if you prefer, you can still gain an exit award at each stage: a PG Cert or a PG Dip.

Full MastersFull Masters

Code: 7CS094

Studying at Masters Level and Research Methods

The Module aims to develop your ability to study at Master’s level and to develop, plan, and execute a project using the processes of research. This module is a prerequisite for undertaking the Master’s dissertation (Independent Scholarship).

Module learning outcomes

On successful completion of the module, students will be able to:

  1. Research, review and critically evaluate current academic literature within a specific context in order to identify the key issues and gaps within current knowledge
  2. Demonstrate a critical understanding of research design and methodological enquiry and to propose an appropriate research plan. Reflective evaluation of the impact of ethical values on academic research will be demonstrated
  3. Systematically analyse and synthesise researched data and theories where appropriate

Module content

The aim of this module is to impart the necessary skills and behaviours required for Master’s study, as well as building the essential characteristics of professionalism in your chosen discipline. It is important that you are able to participate fully in the module so that you can gain maximum benefit from it.

This module will help you identify the knowledge, skills and competencies (academic and professional) that you bring with you to the module, from previous studies or your work experience or both of these. You will have the opportunity to identify areas where your skills might need further development in order for you to achieve your personal aims during the course.

The module also provides the opportunity for you to engage with academic staff in your chosen specialist area, as well as putting the module into the context of your overall professional development now, and in the future.

There are three key areas of study as follows:

  1. Critical thinking. A fundamental principle of Level 7 study and your development as a professional is your ability to think and act critically. You will be exposed to wide range of resources that will enable the development of advanced professional behaviours
  2. Managing a research project. As part of your Master’s study you will be required to conduct a rigorous investigation that will result in tangible research outputs. During this module you will gain first-hand experience of the management of enquiry-based activity to prepare you for research in academic or industrial contexts

Evaluating research outputs. The ability to evaluate and provide a robust justification of your ideas is key to demonstrating professionalism. You will be exposed to a variety of approaches that will allow you to practice critical evaluation of enquiry focused processes and outputs.

More information
20 Credits
core
Coursework

Code: 7CS512

Business Analytics

The acquisition, validation, organisation, management, analysis, presentation and interpretation of data is the primary purpose of information systems in organisations. Underlying much of the success of information systems has been the development of the databases upon which modern information systems are built.

This module will expose students to a wide range of database and data analysis topics which will be actively and practically explored by the students in order to prepare them for their future careers in large and small organisations.

They will have opportunities to work with representative, real-world datasets to develop their understanding of the issues involved in managing and analysing the data and to explore the practical aspects of data mining and management data analysis and presentation using relevant suitable computer languages and computer software.

Module learning outcomes

On successful completion of the module, students will be able to:

  • Identify and critically evaluate the key requirements for effective data management, analysis and presentation to meet users’ needs
  • Evaluate methodologies and develop critiques of management information reporting system that meets the users’ needs using a prototyping / Agile approach in the relevant programming environment

Module content

  • The principles of data analysis and normalisation as the foundation of the ‘store once and once only’ principle for data integrity
  • The requirements for optimisation of data management for the conflicting needs of real-time and MIS / EIS and BI purposes
  • The impact of user-centred analysis and participatory development on the development of data structures and stores and systems that meet the needs of the organisation
  • The role, application and benefits of prototyping in the rapid development of systems, using prototyping, RAD and Agile approaches
  • Techniques and practices for effective data analysis and presentation
  • Together with a range of current, leading-edge topics and technologies that affect data management and usage
More information
20 Credits
core
Coursework

Code: 7CS517

Analytics: Ethics, Trust and Governance

A key question for 21st century companies is how to develop, deploy and manage their information analytics assets in a sustainable fashion that meets best practice in terms of corporate governance, information security, IT law, corporate and information strategy, effective project delivery, ethics and low carbon footprint to name but a few of the most important factors.

Big Data analytics has developed as a result of the very rapidly growing volumes of data being gathered via social media sources and the Internet of Things (IoT) which is now growing at rates similar to Moore’s Law, doubling every 18 months to 2 years. This volume of data, coming from many sources, has the potential for both great benefits to society and organisations and also to great damage to organisations. In other contexts, Big Data analytics has proven to be untrustworthy. 

As a result, there is a need for analytics practitioners to develop an excellent understanding of the governance requirements that they will need to follow in their Data Science career, in order to ensure that their organisations and clients are protected from significant reputational, legal, compliance and operational failures.

Module learning outcomes

On successful completion of the module, students will be able to:

  • Critically evaluate issues and frameworks of ethics, trust, compliance, law and governance in the context of the development and evaluation of Big Data analytics governance strategies and projects
  • Develop and comprehensively justify a systematic appropriate ethical governance strategy for Big Data analytics projects

Module content

  • The principles and frameworks of sustainable corporate and information governance
  • Relevant IT law, with specific reference to the European Data Protection regimes
  • The implications of the Big Data Analytics Vs and their value in helping to identify critical questions about the value of big data analytics in specific business contexts
  • Corporate and professional ethics values and practices as they apply to Big Data and analytics and the individual Data Scientist and Analyst
  • Critical issues of trust, with specific relation to both the data and analytical tools used for Big Data
  • The principles and practices of data stewardship and the curation of Big Data
  • Data and application verification and validation concepts and principles

These topics will be related to current situations and contexts, in order to develop relevant practical and academic understanding and application of the relevant frameworks, theories and principles.

More information
20 Credits
core
Coursework

Code: 7CS997

Independent Scholarship

This module provides the opportunity for you to consolidate, apply and extend your understanding, skills and knowledge of Computing as developed through study on a relevant programme. In particular, you will demonstrate knowledge, understanding, skills and professional behaviour at masters level. The aim is to ensure that you are able to formulate and tackle real world, commercial problems competently, efficiently, independently, and with relevance to a particular problem and/or application, drawing upon knowledge and experience from the related first degrees or equivalent experience.

A portfolio of artefacts will provide the means of assessment, which will include evidence of a ‘product’. A product may be a piece of software, a detailed design for a system, a feasibility study, a creative piece of work, or any tangible asset or collection of assets that are relevant to the investigation. All deliverables will be negotiated with your designated supervisor. Ultimately this is your opportunity to demonstrate your ability to apply what you have learned on the programme in an independent and rigorous fashion.

Module learning outcomes

On successful completion of this module you will be able to:

  • Critically assess contributions in the literature of a range of academic concepts/paradigms and analyse their relevance to a range of academic, technical, creative or business contexts, leading to the creation and justification of a methodologically sound research programme
  • Undertake methodologically sound enquiry into a significant issue in a field relevant to the domain of Computing, demonstrating critical awareness to devise, recommend and/or implement innovative and creative solutions to the area under investigation, and to be able to present these solutions coherently, using the most appropriate media
  • Adopt an original and self-critical approach and reflect on the processes of enquiry and undertake a significant piece of independent research

Module content

This module serves as a ‘capstone’ that effectively demonstrates your ability to demonstrate your management and execution of a significant project. You will use your knowledge and experience to plan, design, execute and evaluate a research project within your chosen domain.

More information
60 Credits
core
Coursework

Code: 7CS518

Natural Language Processing

The aim of the module is to develop the reflective and critical understanding of the main approaches to Natural Language Processing (NLP) and text mining. The course will enable students to gain an insight into the extraction and analysis of data-sets of various origin. In order to achieve this, a variety of methods and techniques from statistical machine learning, ontology based approaches will be discussed. Relevant software tools will be used to enable a critical appreciation of computational, ethical and governance issues in this field, using the Vs of Big Data as a critical framework.

Module learning outcomes

On successful completion of the module, students will be able to:

  • Demonstrate an in-depth critical knowledge of the main techniques and methods in NLP and text mining
  • Be able to apply and critically evaluate and apply the technical and governance theories to real-world business cases via the use of appropriate algorithms and their implementation

Module content

  • Statistical algorithms and techniques for NLP and text mining
  • Ontology-based approaches for data and text mining
  • Hybrid methods and applications to specific text mining topics, such as stream computing, corpora analysis, social web mining, etc
  • Use of specialised software and programming techniques, such as MATLAB, SAS, Python based tools

These topics will be related to current situations and contexts in order to develop relevant practical and academic understanding and application of the relevant frameworks, theories and principles.

More information
20 Credits
optional
Coursework

Code: 7CS519

Information Visualisation

The volume and velocity of the data are such that a traditional statistical analytical system is not capable of coping with either the volume or the rate of delivery of the data in ways that provide opportunities for managerial action. The growth of cyber-crime and cyber-fraud is overwhelming traditional approaches. One approach which shows promise is the use of high-performance visualisation tools offered by SAS® Visual Analytics and SAS® Visual Statistics. In other situations, the use of visualisation tools, both proprietary, such as SAS® JMP and Base SAS®, and open-source and web-based tools provide suitable visualisations that can be used for smaller data sets. This module provides students with the opportunities to work with a wide range of visualisation tools and data sets to develop their visualisation skills.

Module learning outcomes

On successful completion of the module, students will be able to:

  • Critically evaluate the capabilities of different visualisation tools, both proprietary, such as SAS, and open-source, to support the discovery and display of critical and valuable answers hidden in small, medium and large data
  • Execute analysis and visualisation using realistic data sources from disparate disciplines and using the most appropriate visualisation tools in order to identify the valuable questions and to develop well justified, actionable answers
  • To critically evaluate and apply ethical principles and standards to the whole of the visualisation process
More information
20 Credits
optional
Coursework

Code: 7MA505

Statistical Techniques

This module develops a rigorous understanding of statistical concepts relating to probability, data analysis, and statistical modelling, which underpin a variety of engineering topics. In particular, a range of statistical techniques will be discussed. These emphasise the applications to real-life problems involving the analysis of data and interpretation of results, also supported by relevant software tools. Furthermore, techniques associated with hypothesis testing and the analysis of data will also be introduced to enable a full appreciation of the applications of statistical techniques. This will enable you to obtain the relevant skills to systematically test claims on a data set and to determine the validity (or not) of the corresponding hypothesis.

As a consequence, the module increases students’ awareness of applied statistics in industrial and commercial environments. Support for computer-based activities will provide tailored learning from a potentially broad base of prior experience.

Module learning outcomes

On successful completion of the module, students will be able to:

  • Obtain a comprehensive understanding of statistical concepts and a critical awareness of their applications to real-life engineering problems, together with the ethical consequences
  • Develop comprehensive hypothesis testing and statistical analysis to investigate relevant complex problems

Module content

  • descriptive statistics
  • discrete probability distributions
  • continuous probability distributions
  • hypothesis tests
  • inference about population variances
  • tests of goodness of fit and independence
More information
20 Credits
optional
Coursework

How you will learn

Pace of study

We recommend about 20 hours of study per week to complete one 20-credit module over a 10-week trimester. If you aim to study two modules in one trimester, we recommend 40 hours of study per week.

Assessment method

This course is assessed through 100% coursework with a range of methods, such as essays, research reports, presentations, group work and practical reports.

Student wearing headphones at a laptop participating in an online webinar

Study Big Data Online at Derby

Wednesday, 14 August 2019 18.00 - 19.00

Join this webinar to find out more about studying the online MSc in Big Data Analytics, including the key topics you'll cover. You will hear from the academic team and have a chance to ask questions. 

Book your webinarBook your webinar

Entry requirements

You will require a 2.2 or higher bachelors degree in a Science, Technology, Engineering, Mathematics (STEM) or a closely related discipline with significant mathematical content at an appropriate level, or an equivalent international degree.

If you don’t have a degree or an accredited degree, we can consider your application if supported by significant industrial experience. Please include evidence of all experience you have in your application. While the University guidelines on recognition of prior learning (RPL) will be followed, RPL will only be recommended where applicants can present a portfolio of professional work that is deemed to be of an appropriate level.

English language qualifications

If English is not your first language you will need an English language qualification*. For this course you will need at least one of the following:

*If you have a minimum of IELTS level 4.5 you can study our Certificate of Credit in English for Academic Purposes which we will accept as evidence that you are able to perform at a suitable level of IELTS 6.0. We will also accept the Certificate of Credit as evidence that you can perform at GCSE level.

Documents to support your application

You'll need to provide:

*Documents not in English or Welsh must be accompanied by a certified translation by a professional translator/translation company. Each translation must contain:

A list of approved translators can be found on the UK Government website.

Fees and funding

 Per 20 creditsModulesCost
UK/EU/International £950 7 (six 20-credit modules and one 60-credit module) £8,550

† Prices correct for 2019/20 new students. Subject to potential annual increase in September 2020.

Flexible payment plans available

Choose from three options:

 

Masters funding options

Depending on where you are from in the UK or EU, and on your pace of study, you may be eligible for a postgraduate student loan. Accessible through Student Finance, this is a non-income based loan to help with living costs and tuition fees whilst studying your masters programme.

How to apply

Students should apply directly to the University.

Apply now

Careers

The job market around Big Data is rapidly increasing and is trending to be in constant demand as our use of computer data is ever expanding. After completing the Big Data Analytics MSc, you will be able to pursue a career in many areas of computing and information technology, particularly in the areas of data analytics, business intelligence and information management, visualisation and assurance, as well as people analytics in HR.

You could also consider further study and research towards a PhD qualification.

Dr Ovidiu Bagdasar
Programme leader

Dr Ovidiu Bagdasar is the Erasmus Coordinator for Mathematics and Computing. His research in Discrete Mathematics, Optimisation, and Maths Anxiety has been disseminated in numerous international journals and conferences. Ovidiu also works with colleagues and technology companies to improve standards in mathematics education within the University, and beyond.

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Contact us

Contact the University of Derby Online Learning:

Contact usContact usFrequently asked questionsFrequently asked questions

† Additional information about your studies

Start dates are subject to programme reaching minimum numbers

Prices correct for 2019/20 new students. Subject to potential annual increase in September 2020.

Download programme specification