Module Directory

This module is designed to equip students with the foundational knowledge and practical skills essential for leveraging statistical methods in today’s ever-evolving business environment. As organisations increasingly rely on data-driven decision-making, the ability to analyse and interpret data is a crucial skillset for professionals in the field.

Throughout this module, students will delve into the principles of statistical analysis, exploring various techniques for data exploration, data visualisation, hypothesis testing, and regression analysis. The focus will be on the application of statistical methods to extract meaningful insights from complex datasets commonly encountered in business scenarios. Practical exercises and real-world case studies is integrated into the curriculum, providing students with hands-on experience in utilising statistical tools and software.

The aims of this module are:

• To enable students to develop a proficiency in cleaning, organising, and visualizing data to extract meaningful insights.


• To provide a grasp of the nature of uncertainty and the quantitative techniques used to manage it.


• To provide students with the ability to conduct hypothesis testing using secondary data sources.


• To develop competence in making forecasts through a variety of quantitative techniques.


• To provide a primary focus on practical problem-solving using a variety of statistical and computational methods.

By the end of this module, students will be expected to be able to:

1. Have obtained a critical understanding of principal-driven and data-driven approaches in statistical computing and modelling which can be used to analyse data for answering real-life questions.


2. Have developed key analytical skills of analysing data using modern software tools and techniques from an application point of view.


3. Have gained overall perspective on the importance of data analysis and statistics in both strategic and tactical decision making faced by decision makers in the modern business world.


4. Critically differentiate between the questions which can be tackled using qualitative methods and those which require statistical analytical techniques.

Furthermore, the module will explore forecasting techniques, emphasising their significance in aiding organisations to anticipate trends, make informed strategic decisions, and optimise resource allocation. Students will gain proficiency in time series analysis, enabling them to develop accurate forecasts and contribute to proactive business strategies. The aim is to empower students to become adept analysts capable of extracting actionable insights from data, fostering a data-driven decision-making culture within organisations.

The importance of data driven decision-making cannot be overstated in today's big data era. Decision makers including private equity investors, venture capitalists, analysts, entrepreneurs, management consultants, and business managers are increasingly facing a very complex business environment and have to make decisions which potentially could have a huge impact on not only the business unit in question, but also on employees, stakeholders and society at large.

While the problems have become more complex, decision makers have also at their fingertips, access to vast amounts of data as well as tools which can help them analyse the data. The ability to understand the data and use them to support their decisions is increasingly proving to be a necessary skill in a decision maker's portfolio.

The lectures will be developed around the key theoretical concepts of modern statistical computing and modelling, and how they are generally utilised in analysis and answer real business-oriented questions. The lecture material provides an overall view of how to use multiple methods in statistical computing and methods, based on the nature of the question posed as well as nature and source of available data.

The seminars will focus on practical aspects of using the material taught in lectures for solving real life problems. They will use freely available datasets to learn and practise the use of statistical methods taught in the lectures in a practical context. They will give the students hands on practice of the freely available statistics software SPSS, which has a whole range of functionalities from basic to advanced along with excellent graphing qualities.