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This course is no longer accepting applications for 2021 entry.

Course overview

Our Business Analytics MSc is taught in collaboration with multinational companies and will give you the essential skillset they are looking for. It has been developed by the Neo-demographic Laboratory for Analytics in Business (N-LAB), a state-of-the-art teaching, data visualisation and research facility within the Business School.

You will benefit from the engagement of N-Lab's multi-national partners, who include Ipsos, World Bank, Walgreens Boots Alliance and IBM Research. In addition to guest lectures, coursework will be based on real-world datasets and your dissertation will use transactional data from a real company.

Our course will help you to become a business orientated data scientist with a technical skillset, the ability to harness big data tools and the managerial skills to deliver practical business analytics projects. It will prepare you for senior management positions within the broad scope of the digital economy.

Why choose this course?

Delivered by N-LAB

which provides state-of-the-art research, data visualisation and teaching facilities

Top quality school

benchmarked against international standards

EQUIS and AMBA accredited

More than 19,000

Business School alumni connect you to a powerful global network of business contacts

Course content

Across the autumn and spring semesters, you will take 120 credits of taught modules. Each module typically consists of 10 two to three hour sessions.

You will complete a 60-credit dissertation over the summer, and will be allocated an appropriate dissertation supervisor who will oversee your progress.


Semester one

Core modules

Data at Scale: Management, Processing and Visualisation

This module introduces the fundamental concepts and technologies that are used by modern international businesses to store, fuse, manipulate and visualise mass datasets. 

Key concepts include:

  • core database and cloud technologies
  • data acquisition and cleansing
  • how to manipulate mass datasets (focusing on SQL, Hadoop)
  • effective solutions to common data challenges (for example, missing data)
  • handling geospatial and open data
  • visualisation technologies (Tableau, PowerMap, QGIS, CartoDB)
  • web visualisation (HTML5)

All content is based around real-world business examples.

Foundational Business Analytics

This module introduces fundamental statistical concepts and key descriptive modelling techniques in data science, while laying a foundation for the general programming skills required by any top modern business analyst (for example, Python/R).

A range of descriptive modelling concepts will be covered (such as feature engineering, clustering techniques, rule mining, topic modelling and dimensionality reduction) against a background of real world datasets (predominantly based on consumer data).

You will learn not only how to successfully implement foundational descriptive techniques, but also how to evaluate and communicate results in order to make them effective in actual business environments.

Optional modules

One from:

Entrepreneurship in Context

This module covers:

  • definitions of entrepreneurship/entrepreneurial activity
  • the theoretical perspectives underpinning the study of entrepreneurship
  • understanding what shapes the practice of entrepreneurship both in different settings (for example, social entrepreneurship, technology, family business, international entrepreneurship, environmental business, social media) and due to contextual influences (for example, influence of gender, policy)
Management Science for Decision Support

The emphasis in this module is on formulating (modelling) and solving models with spreadsheets. The topics covered include:

  • modelling principles
  • optimisation and linear programming
  • network models
  • introduction to integer programming
  • key concepts of probability and uncertainty
  • decision theory
  • queuing systems
  • simulation
Supply Chain Planning and Management

The module takes a dual approach covering both the business processes and the quantitative models and techniques necessary for supply chain planning and management. It is divided into three major parts.

  1. Supply chain concepts and definitions:
    • Fundamental planning and control concepts for supply chain and operations planning: classification of operational and supply systems
    • Inventory - forms, functions, decisions, models
    • Capacity – definitions and planning
  2. Forecasting for supply chain and production management:
    • Planning, scheduling and control approaches: aggregate planning, hierarchical planning and control
    • MRP-based planning and control
    • JIT principles, kanban systems
    • Theory of constraints (TOC)
    • Enterprise Resource Planning (ERP) systems
  3. Supply chain collaboration:
    • Planning and control across the supply chain
    • The bullwhip effect
    • Supply chain collaboration approaches – continuous replenishment
    • Vendor-Managed Inventory (VMI)
    • Collaborative Planning Forecasting and Replenishment (CPFR)

Semester two

Core modules

Analytics Specialisations and Applications

An in-depth look at specialised analytical techniques which present significant opportunities within business environments to extract actionable insights. Applications covered include Recommender Systems (for example, collaborative filtering in business), Text Analytics (linguistic processing, social media analysis), Spatial/Temporal analytics (for example, financial time series), Network analytics (for example, social graph analysis) and High dimensional analytics.

Leading Big Data Business Projects

This module explicitly focuses on technologies, planning and managerial issues associated with leading big data projects in business. Key concepts revolve around:

  • using data analytics in context (integration of qualitative and quantitative approaches, introduction to survey methods and design)
  • the full data lifecycle (including data management and security)
  • introduction to organisational scale IT infrastructure
  • ethics
  • project management 
  • presentation skills
Machine Learning and Predictive Analytics

This module builds on Foundational Business Analytics covering more advanced predictive models and their motivation within business use cases. Students will establish knowledge of state-of-the-art prediction techniques including SVMs, temporal Nearest Neighbour models, Bayesian methods, Ensembles and Deep Learning.

Practical exercises will be set against a range of real world datasets and time series data. Focusing on the applicability of models to real world problems the module will consider the appropriateness and utility of each method with respect to common ''tricky'' data properties in real world data that lead to under-performing models.

Examples include unbalanced classes, heterogeneous input feature types and detrimentally large number of input features. Within the module methods to unpack the various predictive models to understand why they predict what they do and the utility of this information in various business contexts will be covered.

This module is taught primarily using Python against a background of industrial workflow data modelling environments (for example, SPSS Modeller, Orange) where applicable.

Optional modules

One from:

Advanced Operations Analysis

Module content is organised around four themes:

  1. More ‘advanced’ forecasting techniques (including more advanced time series and causal models)
  2. Inventory modelling (quantity discount models; joint replenishment; reorder point – lot size systems; periodic review models; news vendor model; (S-1, S) model; multi-warehouse situations)
  3. Shop floor control: WIP and Little’s law; introduction to operations scheduling and sequencing
  4. Introduction to distribution logistics modelling, reverse logistics and closed-loop supply chains
Consumer Behaviour and Analytics

The module interrogates the concept of 'the consumer' and 'consumption'. It examines behaviour across the consumption cycle (through production, acquisition, use and disposal) addressing individual and contextual factors that shape behaviour at micro and macro levels.

It reviews the roots of research into consumer behaviour and consumption, covers particular theories and bodies of literature (for example, decision making, learning, habits, socio-cultural processes). It provides opportunities to apply theory to consumer behaviour and consumption in a variety of context and to assess the implications for commercial and non-profit organisations, public policy and consumers themselves.


Data Driven Dissertation Project in Business Analytics

Representing the culmination of the programme, you will design, execute and report a research project based on the analysis of real-world or simulated data. This includes an 8,000-word dissertation, exhibits and data visualisations, and will need to satisfy scholarly objectives consistent with the execution of quality applied research in a business or social context.

The above is a sample of the typical modules we offer but is not intended to be construed and/or relied upon as a definitive list of the modules that will be available in any given year. Modules (including methods of assessment) may change or be updated, or modules may be cancelled, over the duration of the course due to a number of reasons such as curriculum developments or staffing changes. Please refer to the module catalogue for information on available modules. This content was last updated on Wednesday 14 April 2021.

Learning and assessment

How you will learn

We are preparing your tutorials, laboratory classes, workshops and seminars so that you can study and discuss your subjects with your tutors and fellow students in stimulating and enjoyable ways. While we will keep some elements of online course delivery, particularly while Covid-19 restrictions remain in place or where this enhances course delivery, teaching is being planned to take place in-person wherever possible. This will be subject to government guidance remaining unchanged.

We will use the best of digital technologies to support both your in-person and online teaching. We will provide live, interactive online sessions, alongside pre-recorded teaching materials so that you can work through them at your own pace. While the mix of in-person and digital teaching will vary by course, we aim to increase the proportion of in-person teaching in the spring term.

  • Lectures
  • Seminars
  • Tutorials
  • Workshops

How you will be assessed

All assessments in the 2021/22 academic year will be delivered online unless there is a professional accreditation requirement or a specific need for on-campus delivery and in-person invigilation.

  • Dissertation
  • Examinations
  • Essay

Modules are assessed by a combination of exams and coursework at the end of the relevant semester.

Entry requirements

All candidates are considered on an individual basis and we accept a broad range of qualifications. The entrance requirements below apply to 2021 entry.

Undergraduate degree2:1 (or international equivalent) in any discipline; applicants should not have previously studied a significant amount of business analytics, but must have a 2:1 (or international equivalent) in quantitative modules at degree level with a significant amount of mathematical/statistical content


You are required to submit a personal statement and a list of modules being studied in the final year (for applicants who have not yet completed their undergraduate degree).

Spaces on this course are limited and, as such, applications will open in the following windows:

  • Window one: 8 July to 28 September 2020
  • Window two: 26 October to 30 November 2020
  • Window three: 8 February to 8 March 2021

The course will close to all applicants after each window. Applications will then be considered against each other and offers made to the top performing proportion of applicants in each round of selection. Please allow four weeks from the closing date of each window to receive a final decision.

Our step-by-step guide covers everything you need to know about applying.

How to apply


Qualification MSc
Home / UK £13,000
International £30,000

If you are a student from the EU, EEA or Switzerland starting your course in the 2021/22 academic year, you will pay international tuition fees.

This does not apply to Irish students, who will be charged tuition fees at the same rate as UK students. UK nationals living in the EU, EEA and Switzerland will also continue to be eligible for ‘home’ fee status at UK universities until 31 December 2027.

For further guidance, check our Brexit information for future students.

Additional costs

As a student on this course, you should factor some additional costs into your budget, alongside your tuition fees and living expenses.

You should be able to access most of the books you'll need through our libraries, though you may wish to purchase your own copies or more specific titles.


The Business School has a number of MSc scholarships available for 2021 entry.

MSc scholarships

There are many ways to fund your postgraduate course, from scholarships to government loans.

We also offer a range of international masters scholarships for high-achieving international scholars who can put their Nottingham degree to great use in their careers.

Check our guide to find out more about funding your postgraduate degree.

Postgraduate funding


We offer individual careers support for all postgraduate students.

Expert staff can help you research career options and job vacancies, build your CV or résumé, develop your interview skills and meet employers.

More than 1,500 employers advertise graduate jobs and internships through our online vacancy service. We host regular careers fairs, including specialist fairs for different sectors.

Graduate destinations

Our in-house Postgraduate Careers Team provides expert advice and guidance so that you can make an informed decision about the right career path for you.

Prior to commencing your course you can take part in pre-entry careers discussions and once on programme our MSc Accelerated Career Leader Programme is complemented by one to one career coaching.

Our support continues throughout your time at Nottingham and after you graduate. Business School postgraduates also have access to events and resources provided by the Careers and Employability Service.

Career destinations for our postgraduates include:

  • accountants
  • finance and investment analysts
  • higher education teaching professionals
  • investment bankers
  • IT business analysts
  • management consultants
  • marketing professionals
  • public relations professionals
  • university researchers

Some MSc graduates have gone on to doctoral studies, others have become entrepreneurs. Our Ingenuity Lab has supported a number of our MSc graduates in starting their own company.

Career progression

88.8% of postgraduates from Nottingham University Business School secured graduate level employment or further study within 15 months of graduation. The average annual salary for these graduates was £41,001.*

* HESA Graduate Outcomes 2020, using methodology set by The Guardian. The average annual salary is based on graduates working full-time within the UK.

Two masters graduates proudly holding their certificates

Related courses

The University has been awarded Gold for outstanding teaching and learning (2017/18). Our teaching is of the highest quality found in the UK.

The Teaching Excellence Framework (TEF) is a national grading system, introduced by the government in England. It assesses the quality of teaching at universities and how well they ensure excellent outcomes for their students in terms of graduate-level employment or further study.

This content was last updated on Wednesday 14 April 2021. Every effort has been made to ensure that this information is accurate, but changes are likely to occur given the interval between the date of publishing and course start date. It is therefore very important to check this website for any updates before you apply.