Business Analytics - MSc

The aim of this module is to enable students to use spreadsheets (Microsoft Excel) to structure, analyse and solve a variety of business problems. It will also provide the students with a basic knowledge of Visual Basic for Applications (VBA) as a means to automate Excel functionalities and create user-friendly applications.

The module will cover two main topics:

• Spreadsheet Modelling. This part will involve learning about modelling techniques to represent the real world in a structured and logical way; how to use basic and advanced spreadsheet facilities to organize, visualise, query and summarise data; how to use spreadsheets to analyse and solve managerial problems in a variety of organisations (e.g. scheduling, forecasting, inventory, optimisation, financial analysis, and project management problems).
• Visual Basic for Applications. In this part of the module, students will learn how to take their Excel abilities to the next level by wrapping their spreadsheet models into friendly applications for the end users. Through guided demonstrations, students will develop a variety of applications, including financial, logistics and business management applications.

The module will be subject to continual refinement to ensure the content is kept relevant to industry's usage of spreadsheets.

The aim of this module is to enable students to apply basic statistical inference methods for tackling real-world business questions and equip them with basic knowledge of the R statistical programming package.

The module covers two indicative areas:

1. Business Statistics: Students will learn about descriptive analysis of quantitative data, focusing mainly on how to effectively summarise data, and inferential analysis of quantitative data, which includes identifying key properties of a given dataset, deriving point and interval estimates, hypothesis testing, correlation analysis, and simple linear regression.
2. Python programming package: This will cover the Python programming language and introduce students to basic and more advanced concepts within Python, as well as how to use Python for performing statistical data analyses.

The aim of this module is to introduce students to optimisation modelling and solution techniques, typical applications areas within strategic/operation business planning, and the use of commercial optimisation software.

The module covers the following indicative topics:

• Linear Programming: Students will be introduced to the building blocks of optimisation (i.e. decision variables, objectives, constraints), how to mathematically formulate linear programming (LP) models, LP solution techniques, sensitivity analysis (e.g. range of optimality reduced costs, dual prices), and typical applications like production planning, scheduling, and portfolio selection.
• Network Models: This topic includes a range of concepts and modelling techniques for formulating classic network models, including transportation and assignment, shortest path, maximum flow, and minimum spanning tree problems, and common solution approaches.
• Integer Programming: This will cover integer linear programming (ILP) models, including binary integer models, classic exact and heuristic solution methods (e.g. branch and bound, greedy heuristics), and typical application areas of ILP, including capital budgeting, fixed charge production, and facility location.

The aim of the module is to give students hands-on experience in using industry-standard simulation modelling software in order to structure and solve complex and large-scale managerial decision problems.

The module will cover the following indicative topics.

• Queuing theory: Students will be introduced to the basic underpinnings of queuing theory, including key assumptions, benefits, and limitations.
• Discrete-event simulation: Core theory of discrete-event simulation will be covered, including a review of simulation mechanics, how to incorporate randomness into a simulation, and the systematic analysis of simulation model results. This will be supplemented with practical training in how to build and run simulation models using commercial software. Example applications will be drawn from a variety of sectors, such as manufacturing/production, transportation, healthcare, and other service industries (e.g. banking, retail, customer service).

In this module, students will learn about the fundamentals of machine learning and forecasting techniques and gain hands-on experience with analysing and solving a variety of problems encountered in business and management.

Three indicative areas of the module could include:

1. Machine learning: The introduction of modern machine learning techniques used in business data analysis, including both supervised learning (e.g. regression, classification, and artificial neural networks) and unsupervised learning (e.g. association rule discovery and cluster analysis).
2. Forecasting: Students will learn about various forecasting methods, including exponential smoothing methods and the Box-Jenkins method (i.e. the ARIMA model and variants).
3. Data analysis report writing. Students will systematically carry out a data analysis project and write a data analysis report.

The data analysis packages such as R, SPSS, and Weka may be used in this module.

This module aims to introduce students to the power of big data analytics and data visualisation techniques in contributing to business value creation. The module will also enable students to solve a variety of complex data centred business problems using computer software tools like Apache Spark and Python.

The module covers two main themes as follows.

1. Theoretical understanding of big data analytics: This part involves learning about the theoretical foundations of big data analytics, text mining, and social media data mining. It also introduces the effective use of data visualisation and database management concepts and their links with big data analytics. Example applications of big data analytics and visualisation techniques discussed within the module will focus on addressing contemporary challenges faced by industry.
2. Building practical skills and managerial insights: In this part of the module, students will learn how to interact with both SQL and NoSQL databases and how to solve business problems using advanced functions within the Apache Spark and Python software platforms. Students will be guided through demonstrations involving a variety of exercises that will prepare them to be data-driven managers and executives capable of utilising big data analytics for business value creation.

Employers are in search for individuals who possess logical thinking, analytical capability, leadership, communication and the ability to work under pressure. This module develops the necessary research knowledge and skills for students to be able to successfully complete a piece of research in industry or consultancy contexts or academia.

Indicative topics are:

• Choosing the topic of interest and literature review
• Research process and Ethics in in business research
• Choosing your research design
• Preparing the research proposal
• Communicating the Research.

The module will develop students' understanding of corporate finance theory and its applications to the main problems faced by financial managers and corporate decision makers. The main topics that will be covered include corporate capital budgeting, investment decisions under uncertainty, cost of capital, sources of finance, capital structure, dividend policy and mergers and acquisitions (M&A) decisions.

This module will cover the following topics:

• Investment appraisal techniques and decisions• Stock market efficiency – capital market behaviour• Portfolio theory• The Capital Asset Pricing Model• Sources of finance• Capital Structure

The aim of this module is to enable students to critically evaluate the roles warehouses, transportation centres and the different modes of transport have on the logistic and supply chain systems. It will also provide the student with an understanding of warehouse management activities, such as picking strategies and warehouse layout, packaging, etc and of distribution decisions, such as transport modes and single-, multi- or omni-channel planning. Students will be able to appreciate the use of appropriate methods that are used in practice and their impact in generating the company competitive advantage.

According to the traditional marketing concept, the Consumer is at the heart of all marketing activities. Thus, how consumers and organisations buy, own, consume and dispose of products, brands, marketplace communities, and experiences is the heart of marketing. Consumption is researched by a diverse array of disciplines including economics, anthropology, psychology, sociology and cultural studies, (human) geography, history and linguistics and political science. This interdisciplinarity has brought great depth and complexity to marketing's understanding of consumption.

Although the focus of this module is consumer behaviour, organisational behaviour will also be explored. Students will develop an understanding of how theories relating to consumer and organisation decision-making, and buyer behaviour inform marketing practice. There will be an emphasis on (i) a micro-level analysis, which relates to more immediate or individual aspects of Consumer and organisational buying behaviour; and (ii) the macro-level, relating to how the broader environment and cultural issues influence consumption.

This module provides an overview of key topics within the domain of marketing research and will provide students the platform to understand the importance and use of information for making marketing decisions. While this module will cover concepts in marketing research, its focus will be on i) providing students with an understanding of how marketing research fits into the contemporary marketing framework, and ii) understanding and interpreting the use of marketing research information and tools for the purposes of understanding information use.

This module will combine lectures and seminars to present, transfer, discuss and summarise intercultural marketing concepts and frameworks. Indicative topics are:

• Analysis of cultural conditions in global markets

• Cross-cultural Marketing research

• Identification of International segments and niche markets

• Executing marketing mix strategies across different cultures

• Consumer Behaviour across cultures

• Making ethical decisions across cultures

In today's competitive and global economy, companies are turning to project management to consistently deliver business results. Increased number of international and complex projects brings with it a growing demand for project management specialists, according to Project Management Institute (PMI). This module introduces the principles and practice of project management in a global context. The module aims to:

Equip you with project management tools, techniques and management issues, focusing on key challenges that arise from managing complex projects, such as with regards to project time, quality and cost, resource constraints, stakeholder analysis, cultural diversity and teamwork.

Analyse contemporary project management issues and employ a range of established and innovative methodologies for adequate project plan, execution and control;

Critically analyse risks in projects, develop sustainable contingency plans and demonstrate ability to set reasonable and achievable deadlines and milestones across different project tasks with due consideration to economic, social, and environmental aspects surrounding a project.

As global supply chains stretch across the globe with buyers from numerous countries on one side and multinational suppliers on the other, corporations are under pressure to unlock the working capital trapped in their supply chains. In this module, we will explore a set of traditional and technology-driven solutions that optimise cash flow by allowing businesses to lengthen their payment terms to their suppliers while providing the option for their suppliers to get paid early, resulting in a win-win outcomes and risk minimisation across the supply chain. The module will cover the following indicative topics:

- Working capital and cash-to-cash cycle approaches as indicators of supply chain efficiency and methods for improving cash flow

- Transitional stages of supply chain finance, its barriers, challenges and opportunities, the new role of financial service providers, supply chain finance options, platforms, costs, benefits and risks

- Blockchain technology and its application in supply chain financing and traceability, through smart contracts, peer-to-peer value exchange, transparent private and public distributed validation.

The operations management function has always been of vital importance in a wide variety of organisations and industries whether manufacturing- or service-oriented, public or private, small or large. With the rise of Industry 4.0, managers are expected to continuously optimise operations to enable even faster, more flexible, and more efficient processes to create and deliver higher-quality goods and services at reduced costs.

The module examines the main methods of marketing communications. Strengths and weakness of marketing communications channels will be analysed as well as their suitability and effectiveness. The module systematically evaluates the principles, methods and strategies of marketing communications. The role of message content, format and source will be discussed as well as psychological processes involved in consumers' processing of and response to advertising. The module will discuss how different marketing communications platforms can be combined to reach specific objectives.

Indicative topics are:

• The communications process• Advertising• Strategy and media planning• Image, brand management and packaging• Direct marketing• Digital and interactive media• Sales promotion, merchandising and point of sale• Public relations and corporate identity• Exhibitions, trade shows, product placement and sponsorship• Personal selling and sales management• The implications of digital marketing communications are included in the above topics.

This module will cover the design, planning, execution, control, and monitoring of supply chain activities, such as supplier relationship, production, inventory, transportation and demand management. Strategic questions regarding efficiency versus responsiveness will be addressed with the objective of creating net value, building competitive strategies, leveraging worldwide logistics and synchronizing supply with demand. The module will equip students with appropriate methods that are adopted in practice.

This module allows students to put into practice the knowledge and skills gained in the other modules on the MSc Business Analytics. Working under the guidance of an academic supervisor and possibly with a company, students solve practical problems that require an application-oriented thinking. The problems are varied and interesting, such as analysing marketing campaigns, ranking credit risks, optimising capital investments, forecasting sales trends, simulating patient flow through hospitals and extracting patterns from large datasets.