MSc

Data Analytics for Business

Group work
  • Level(s) of study: Postgraduate taught
  • Study mode(s): Part-time (day)
  • Location: Clifton Campus
  • Starting: September 2018
  • Course duration: 2 year(s)
  • Entry requirements: More information

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 ‘Project Conceptualisation and Planning’).

    At the end of this module, you will produce a Professional Portfolio - compiled over the entire course to evidence your skills, experience and achievements, this portfolio will be summatively assessed. The portfolio will also be an opportunity to evidence how you have considered appropriate professional, legal and ethical issues associated with big data systems.

  • Other data science short courses

    You could benefit from studying these additional short courses involving data science. R is a free software programming language and software environment for statistical computing and graphics:

    Start using R - Introductory course - 2 days

    This two-day workshop is designed for people who want to use R for analysing data and generating custom graphics.

    Intensive R Course - 4 days

    You will explore R graphics in depth and acquire skills and knowledge for visualising data and producing professional graphics in R. You will begin to customise R's core analytical capabilities and perform advanced statistical analyses.

    Advanced R Skills - 2 days

    This workshop is perfect for you if you want to customise R's core analytical capabilities for their own purposes and for those wishing to visualise sequencing or related kinds of data.

  • Key stats

    • 90% of our research activity achieved world-leading, internationally-excellent or internationally-recognised status (latest Research Excellence Framework 2014)
    • 95% of our postgraduates are in work or further study within just six months of finishing their degrees (latest Destination of Leavers from Higher Education survey 2014-15)
    • 83% of NTU postgraduate taught students said that overall they were satisfied with the quality of their course (Postgraduate Taught Experience Survey 2016)
    • Leading university in the UK for the number of postgraduate students taking professional qualifications (latest Higher Education Statistics 2014/15)

Course specification

View the full course specification
Please note that course specifications may be subject to change

How you’re taught

This part-time course can be studied while working full-time and fitted around other commitments. The course is studied over a two-year period. You will attend three-day study blocks once every 12 weeks, which will usually run Thursday–Saturday.

You will attend university in ‘study blocks’ during which you will be introduced to relevant theories and concepts. Outside of these study blocks you will be expected to engage in independent learning, and will be guided through this by your module tutors.

  • Before each study block you will be provided with pre-work. This may take the form of readings, exercises, discussion points and research to undertake within your organisation.
  • During the study blocks, you could be undertaking individual research, practicals, participating in small group or one-to-one tutorials, working with fellow students in seminar or workshop activities or attending larger group lectures.
  • After each study block you will be expected to undertake independent learning in order to consolidate and extend your learning, applying what you have learned to your work environment, and further exploring subjects before completing the module assessment.

Assessment methods include:

  • examinations
  • academic reports or essays
  • technical reports
  • reflective work
  • practicals
  • presentations or portfolios

Careers and employability

We have tailored this Data Analytics postgraduate degree to provide you with the knowledge and experience needed to take the next step in your career.

You may already work in one of these fields or are aspiring to further your career in:

  • ecommerce
  • professional, scientific and technical services
  • finance and insurance
  • engineering and manufacturing
  • science and research
  • healthcare
  • telecommunications
  • retail
  • media, communication and entertainment
  • transportation and automotive

You will graduate with the techniques and skills relevant to your sector and industry requirements, as you combine academic study with in-company work-based projects.

Entry requirements

What are we looking for?

  • A 2.2 honours degree or above in a related subject
  • Applicants should be in employment or be able to gain access to an organisation upon which to base assignments

If your education qualifications are lower level than a first degree, relevant work experience will be considered and you may be asked to provide some evidence of your capability to study on a master's programme.

How to apply

Ready to join us? Then apply as soon as you can. Just click the Apply button at the top of the page and follow the instructions for applying. Make sure you check the entry requirements above carefully before you do.

Please read our notes on the University's commitment to delivering the educational services advertised.

Fees and funding

Course fees for September 2018 and January 2019 entry are:

Study routeUK / EU
Part-time block-release£10,000

Funding your studies

Preparing for the financial side of student life is important, but there’s no need to feel anxious and confused about it. We hope that our funding page will answer all your questions.

Getting in touch

For more advice and guidance, you can contact our Student Financial Support Service on: +44 (0)115 848 2494.

Still need help?

+44 (0)115 941 8418