Timetable and Modules
Note: Modules offered each academic year are subject to change. Listed below are the modules and timetable for 2023/2024.
Michaelmas Term |
Hilary Term |
Trinity Term |
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Michaelmas Term (September to December)
- Foundations of Business Analytics
- Data Management and Visualisation
- Business Data Mining
- Business Forecasting
- Strategy for Analytics
- Ethical and Privacy Issues in Big Data
Hilary Term (January to April)
- Business Decision Optimisation
- Marketing Research and Analytics
- Financial Modelling and Analysis
- Operations Analysis
- Social Media Analysis
- Big Data and AI in Business
Hilary Term (May to August)
- Research Project
This project allows students to showcase the knowledge they have gained and enhance their career potential by specialising in a particular area
Module Descriptions
Foundations of Business Analytics (5 ECTS)
This module will help students develop analytical skills and enhance their statistical knowledge, allowing them to understand and analyse data and draw insights that support and determine the way decisions are made in the context of operations processes and across the supply chain. This module will draw on the principles of management science and cover topics such as, describing and summarizing data, statistical inferences, ANOVA, distributions and sampling
On successful completion of the modules, students should be able to
- Explain the concept of statistical inference
- Describe how to conduct correct hypothesis testing, ANOVA and regression
- Interpret the result of hypothesis testing, ANOVA and regression
- Apply the descriptive analytics to solve real business problem
Data Management & Visualisation (5 ECTS)
Within a professional context, this module prepares students to understand the role and use of database and database management systems. Students should gain an extensive understanding of techniques and processes that are essential to developing and managing databases. The module would also introduce students to communication of data as a storyboard along with geo‐location data analysis. In this module, the students will learn database management, data retrieval and data visualisation technique using Tableau.
On successful completion of the modules, students should be able to:
- Explain the concept of database management system
- Compare different mechanisms to retrieve and store data
- Assess and compare key data visualisation techniques
- Implement key data visualisation techniques using Tableau
- Demonstrate visualization practice to represent large data in a small space and make information coherent
Business Data Mining (5 ECTS)
This module is an introductory one on data mining. It introduces the basic concepts, principles, methods, implementation techniques, and applications of data mining, with a focus on two major data mining functions: (1) pattern discovery and (2) cluster analysis. This module would introduce students to basic sets of supervised and unsupervised data mining techniques to enable better business decision making. This module would introduce students to various data mining techniques including random forest, decision trees, clustering and classification.
On successful completion of the module, students should be able to:
- Compare unsupervised and supervised data mining techniques
- Formulate business situation in terms of data mining problems
- Assess the right data mining technique for analysis
- Interpret the data mining results for business managers
- Use and understand basic programming constructs
Financial Modelling & Analysis (5 ECTS)
The module introduces students to the meaning of financial numbers and metrics and the decision‐making processes that involve financial information. This module is aimed at participants with little or no financial training or background. This module will enable the students to understand the financial statements of a business, analyse those statements and make well‐informed, rational decisions based on their analysis. The goal of the module is to bring students with no business or finance background to financial literacy in a business context.
On successful completion of the module, the student should be able to:
- Identify the key financial objectives that influence organisations
- Apply investment appraisal techniques and make appropriate recommendations.
- Develop useful models to analyze and solve financial problems.
- Use Excel to implement spreadsheet solutions for financial models.
- Evaluate decisions made using Excel solutions Exercise
- Assess and select appropriate policies based on financial and strategic analysis
Operations Analytics (5 ECTS)
This module will introduce students to fundamental concepts of supply chain management. Students will learn how to apply descriptive, predictive and prescriptive analytics to support decision making in operations and supply chain management. The module on operations analytics, will focus on how the data can be used to profitably match supply with demand in various business settings. The module will use excel as a software tool to introduce various supply chain application techniques for making business decisions.
On successful completion of the module, the student should be able to:
- Interpret the supply chain business problems in analytical terms
- Identify suitable analytics tools for a given operations and supply chain problem
- Apply descriptive, predictive and prescriptive analytics to solve operations and supply chain problems
- Translate analytics result into operations and supply chain decisions
Social Media Analysis (5 ECTS)
As social networks like Facebook, Twitter, and YouTube continue to play an important role in mediated communication today, be it at organizational or individual levels, the volume of data generated by their users increase phenomenally. Accordingly, searches and processing of social Web data beyond the limiting level of surface words are becoming increasingly important to business and governmental bodies, as well as to lay Web users. Detection of sentiment, emotion, deception, gender, sarcasm, age, perspective, topic, community, and personality are all valuable social meaning components that promise to be important elements of next generation search engines. The emerging area of extracting social meaning from social network data using automated methods is known as Social Media Intelligence (SMI).
On successful completion of the module, students should be able to:
- Describe a wide range of social media usage, management, and mining concepts
- Explain the structure of networks and graphs
- Apply data mining techniques to social media platforms
- Develop intelligence based on emotion and sentiments in online content
- Apply social media marketing, management, and mining methods to address information needs, questions, and issues.
Business Forecasting (5 ECTS)
In this module, the students will learn analytics techniques and models that are used to predict what might happen based on the available data. This module takes modern approach to analysing large business data sets. In this module students will build upon the fundamental regression techniques that they learnt in foundations of business analytics module to both continuous time series data as well as categorical data that is of high importance in business contexts
On successful completion of this module, students should be able to:
- Demonstrate how to choose and apply different forecasting methods
- Select and assess forecasts from different sources
- Interpret the result of forecasting and simulation models
- Apply the predictive analytics to solve real business problems
Business Decision Optimization (5 ECTS)
In this module, the students will learn analytics techniques and models that are used to propose a solution based on the available data. This module introduces students to the arena of “what‐if” analysis. Comparing different scenarios and evaluating the best scenario for business is one of the primary functions of modern business managers. Business decision optimization module will use simulation and optimization techniques to enhance the understanding of business decision‐making processes. The techniques that will be covered include: linear programming, integer programming, heuristics and multicriteria decision making.
On successful completion of the module, students should be able to:
- Explain the concept of optimisation and multicriteria decision making
- Describe how to conduct correct linear programming, integer programming and heuristics
- Interpret the result of linear programming, integer programming and heuristics models
- Apply the prescriptive analytics to solve real business problems
Big Data and AI in Business (5 ECTS)
This module introduces students to some of the emerging topics in field of business analytics and its implications for businesses. The module would consist of mix of case studies on latest technologies and introduction to the underlying technologies. It is not intended to be a deep diving technical module in big data or AI. The module would prepare the students to be managers who could understand the implications for these technologies in business context. This module provides a non‐technical, highly interactive, and engaging introduction to help kick‐start professionals in their understanding of these topics.
On successful completion of the module, students should be able to:
- Explain the strategic importance of Big Data in businesses
- Identify Big data opportunities within the firm
- Evaluate potential for AI use in businesses context and its implications
- Formulate a big data strategy for firm growth
Ethical and Privacy Issues in Big Data (5 ECTS)
Business and the business corporation are central elements in the modern social construct. In this context the question of privacy and ethics in business data management has become a major feature of the discourse on the legitimacy of business practice. Rising global legislations like GDPR add to the regulatory requirements that managers need to navigate while using data for business practice.
On successful completion of the module, students should be able to:
- Compare and critique conceptual elements of major ethical discourses.
- Design ethical and compliant data management structures
- Analyse the range of stakeholders in relation to the functioning of business and identify their various interests and concerns.
- Use ethics as a frame for examining the interrelationship of these challenges faced by businesses.
Strategy for Analytics (5 ECTS)
There has been a growing awareness about the networked nature of businesses amongst managers globally. Combined with the rise of tools and techniques about business analytics, modern managers need to be equipped with tools and techniques to utilize the enormous datasets available at their disposal and analyse them in a networked economy context. The module enables students to understand the business context of analytics, diagnose business problems and generate business insights. The module also helps students to understand the impact of network effect in social media and its business implications.
On successful completion of the module, students should be able to:
- Determine how business analytics can generate competitive advantage for firms
- Explain the differences between structured and unstructured data and the role played by such data in different applications of business analytics
- Explain how firms can overcome the difficulties of adopting business analytics
- Explain how to derive competitive advantage from network effect
- Describe the mechanisms to derive maximum benefit of network effect on social media
Marketing Research and Analytics (5 ECTS)
In this module, students will learn how to acquire, manage, and analyse marketing data, as well as how to transform the insights into actions. Companies need to monitor and adapt their marketing strategies to deal with changing consumer needs, global realities and dynamic technological challenges. This module will also provide some applied tools of descriptive, predictive and predictive analysis to improve marketing decision making. These tools can be used to analyse data from various sources, such as survey, CRM, the web, mobile, social media data, etc
On successful completion of the module, student should be able to:
- Apply descriptive, predictive and prescriptive analytics to solve marketing problems
- Acquire, manage, and analyse marketing data
- Define, understand and critically evaluate key marketing concepts and philosophies
- Construct distinct segments within a market and identify and select optimum target market(s) for companies
The MSc in Business Analytics Research Project
The research project will allow you to showcase the knowledge you have gained and enhance your career potential by specialising in a particular area.