For more information about Data Analytics for Business at The School of Science and Technology, Nottingham Trent University, please visit the webpage using the button above.

The award
MSc

How long you will study
2 years

Domestic course fees
find out

How you will study
part-time

Course starts
September

International course fees
find out

All study options

About Data Analytics for Business at The School of Science and Technology, Nottingham Trent University

Learn new analytical tools and skills needed to maximise the big data revolution, developed specifically for you to fulfil the needs of employers.

This is a part-time industry-led data analytics Masters with modules from Nottingham Trent University's School of Science and Technology and Nottingham Business School. You will gain a more comprehensive understanding of data science and the processes involved in the entire data life-cycle, giving you the flexibility to gain the skills and confidence to help your organisation harness the power of big data.

To help you manage working full-time and studying, we've created a study pattern to fit in with your other commitments:

  • You will attend a three-day study block for each module, which will usually run Thursday-Saturday
  • Your learning will be supported with online resources and tutor support
  • You'll study alongside professionals from a number of different sectors. Students have come from industries including e-commerce, engineering and manufacturing, healthcare and retail.

You may also be interested in the Online MBA with Data Analytics.

Co-designed by specialist academics from the School of Science and Technology, Nottingham Business School and employers

Postgraduate open events: We hold postgraduate open events throughout the year. Find out more and book a place.

Contact details: For further details please email DACourse@ntu.ac.uk.

Telephone: +44 (0)115 848 8351

What you'll study

For those business professionals or students who are relatively new to data analytics, the modules you study will broaden and deepen your technical understanding. We imagine that you will already be in employment, however, if you don't have access to large data sets, we can provide you with live industry projects.

Our progressive integration of business, computer science and applied mathematics modules make it a suitable and challenging option for a diverse mix of individuals.

Those who may be experienced in data analytics and are now looking to progress, you will gain a wider perspective on the industry whilst honing the management and leadership skills necessary to realise the benefits of big data innovation.

  • Modules - year one

    Big Data & its Infrastructure (20 cp)

    The module content is designed to develop and structure your understanding according to the stages an organisation moves through in order to develop and manage the infrastructure necessary to derive business value from large volumes of data.  The module is organised as follows:

    • The Big Picture of Big Data
    • Overview of Database Technology for Big Data
    • Technology and Infrastructure for Managing Big Data
    • Deriving Business value
    • Social, Legal and ethical issues

    Statistical Approaches to Data Analysis (20 cp)

    The aim of this module is to provide students with an introduction to the statistical principles and statistical methods required for the analysis of large datasets. The module uses a statistical computing tool such as R, Minitab or SPSS for initial exploration and visualisation of data and for predictive modelling. This module includes hands-on labs to familiarise students with the concepts taught.

    Types of data:

    • Statistical inference: population and sample
    • Descriptive statistics
    • Exploration and visualisation of data
    • Probability and normal distribution
    • Principles of hypothesis testing
    • One sample t-test, two-sample t-test, paired t-test
    • Nonparametric tests
    • Correlation and regression
    • Chi-square tests.

    Delivering Value (20 cp)

    • Developing the marketing infrastructure and the operational aspects of marketing to create value
    • Managing new and existing brands, products and services in a range of markets
    • Managing channel and stakeholder relationships
    • Understanding what it is to deliver value from the customer perspective
    • Trade-off choices and why operations and marketing need to be aligned
    • Managing and reducing variability in a delivery system
    • Principles, theories and concepts that support decision making
    • Continual improvement - culture, practice and tools

    Effective Change Management (20 cp)

    This module will consider:

    • Difficulties of Driving Change Through Organisations
    • Common Models and Theories of Change Management Practices
    • Project Management and Operational Considerations
    • The Role of Leadership and Personal Effectiveness
  • Modules - year two

    Practical Machine Learning Methods for Data Mining (20 cp)

    The module is designed to develop you as a Data Analyst who is able to competently work with large volumes of data to extract, interpret and present meaningful information. Subsequently, the content is organised as follows:

    • Reminder: CRISP-DM
    • The Basic Components of Machine Learning Models
    • Machine Learning Methods for Classification and Prediction
    • Machine Learning Methods for Clustering

    Project Conceptualisation and Planning (20 cp)

    The module will consist of the following indicative content:

    • Skills of a Systems Analyst
    • Project Identification and Selection
    • Systems Development Lifecycle and Methodologies
    • Project Planning and Management
    • Business Process Improvement, Automation and Redesign
    • Requirements Elicitation
    • Requirements Modelling

    Work-based Project (60 cp)

    You will apply your new skills and knowledge to a three to six month project that is directly relevant to your employer's needs. Your learning will be largely independent, under the guidance of your academic mentor. They will help you to identify and access suitable learning resources. It is likely that you will engage with materials relating to:

    • Research methodology, strategies, methods & techniques
    • Research and/or practitioner skills
    • Leading edge theory and practice in big data systems (here you will be able to use prior learning from earlier modules as a starting point to direct your efforts)
    • Project management and modelling solutions (you can draw here upon your experiences in the core module

Study options for this course

Notes about fees for this course

UK Fees

Tuition fees 2017

  • Tuition fees for 2017/18 will be £10,000, but bursaries of £2,500 are available for those enrolled for 2017

Tuition fees 2018

  • TBC

Please see our Fees and Funding page for details and scholarships.

International Fees

Entry requirements

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