Business Analytics MSc

Compulsory modules:

• Network Analytics

  This module provides on overview of various frameworks and algorithms used in practice to describe and analyse network data―namely information about relations among decision makers (e.g. customers), objects (e.g. products), or decision makers and objects (e.g. customer-product ties).

  You should expect to grasp the logic behind modern network science from a practical standpoint. Standard computing skills in Python are required to put in practice the theory discussed during the lectures.

• Data Visualisation

  This module provides design principles along with frameworks and techniques to synthesise and illustrate complex information via data visualisation This enables you to understand the significance of data by placing such data in a visual context.

  You should expect to learn different approaches to data visualisation (e.g., pattern recognition or 'data storytelling') and to be able to adjust these approaches in order to reach different types of audiences.

• Revenue Management and Pricing

  The Revenue Management and Pricing module explains how firms should manage their pricing and product availability policies across different selling channels in order to optimise their performance and profitability.

  The module aims to explain quantitative models needed to tackle a number of important business problems including capacity allocation, markdown management, e-commerce dynamic pricing, customised pricing and demand forecasts under market uncertainty.

• Analytics Methods for Business.

  This module provides a collection of standard analytical methods and explains how data analysis is performed in the real world.

  They represent an introduction to specific tasks that a business analyst has on a daily basis that ultimately would help in analysing, communicating and validating recommendations to change the business and policies of an organisation.

  Furthermore, the module provides the foundation for using the R programming language to translate theory into practice.

Compulsory modules:

• Applied Deep Learning

  The Applied Deep Learning module provides practical implementations of Deep Learning tools into the real world by showing multiple use cases from various sectors, e.g. Recommender Systems and their applications in E-commerce (product recommenders) and social media platforms (content recommenders), Fraud Detection, Digital Marketing etc.

  This module is not necessarily aimed to develop a strong Deep Learning foundation, and instead, a learning-by-doing is the main delivery method of the main concepts.

  The key takeaway of this module is to familiarise the students with contemporary Deep Learning applications that are essential to understand prior to taking the compulsory summer Applied Research Project, which is the knowledge integration part of your education journey during your studies.

• Machine Learning

  This module provides an overview of various machine learning concepts, techniques and algorithms which are used in practice to describe and analyse complex data, and to design predictive analytics methods.

  You should expect to grasp the main idea and intuition behind modern machine learning tools from a practical perspective. Standard computing skills in R and Python are required to put in practice the theory discussed during the lectures.

• Strategic Business Analytics

  This module teaches you how to design, validate and communicate business strategies by using quantitative techniques encountered in all other core MSc in Business Analytics modules.

  A strategic consulting approach through real-life case studies is the key ingredient of the module that enables the module leader and invited speakers to illustrate the scope of modern business analytics by providing expert solutions to various chosen real-world problems.

  You are trained to develop complex analytical problem-solving skills and hone the critical thinking of a future business analyst.

• Digital Technologies and Value Creation.

  The Digital Technologies and Value Creation module follows a use case approach and aims to explain how digital technologies could enhance the business opportunities for a firm.

  Various real-life applications are provided from problem identification to practical implementations, and the chosen sectors are Marketing Technology (MarTech), People Analytics, Social Media Analytics etc.

  This module is not necessarily aimed to develop the core analytics tools, and therefore, the main takeaways of this module is to familiarise the students with contemporary Business Analytics applications that are essential to understand prior to taking the compulsory summer Applied Research Project, which is the knowledge integration part of your education journey during your studies.

In term three you will study:

• General Research Project

  This module will help you integrate your learning across a range of skills and in particular to enable you to demonstrate the results of your learning through the development of a final project. You may choose to complete more traditional academic desk research, complete a business plan or carry out a consultancy project and any reasonable proposal is acceptable providing that it is agreed with the project supervisor and the course director

You will also choose three elective modules; popular choices include:

• Applied Machine Learning
• Applied Natural Language Processing
• Business Intelligence Deployment
• Data Management Systems
• Fintech – Financial Services in the Digital World
• Practicing Management in the Digital Age