Business Analytics MSc

Core 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.

Core 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:

• Applied research project

  The aim of this module is to enable you to demonstrate how to integrate your learning in core and elective modules and then apply this to the formulation and completion of an applied research project. You will be required to demonstrate the skills and knowledge that you have acquired throughout your MSc study.

  You will undertake a short piece of applied research on a question of academic and/or practical relevance. Guidelines will be provided in order to help you identify the research question. Based on your chosen topic, you must write a report of around 3,000–5,000 words that summarises and critically evaluates your method and your findings.

  In the past, all students were offered projects designed by our industry partners that aim to develop the consulting skills of each student.  For the last academic year, students chose from twenty projects offered by various analytics consulting companies, companies from finance and insurance sectors, well known retailers, etc., including: Bank of England, Ekimetrics, Government Actuary's Department, Rolls-Royce and Vodafone UK.

You will also choose four 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