Business Analytics

The MSc in Business Analytics starts online in the summer before the beginning of term 1 with three pre-courses which ensure that every student has the minimum specific background required by all other modules.

These subjects are key elements of your course and you are strongly encouraged to complete the modules before you arrive at Cass in order to avoid being at a disadvantage.

• Introduction to Python

  This module is designed to provide a fundamental understanding of Python programming and no previous programming experience is expected. The teaching model is learning by doing and basic concepts are built up in an incremental manner.

  The online material is formulated via multiple Python code examples that enable the students to work independently when dealing with small Python programming tasks.

• Introduction to R Programming

  This module is designed to provide a fundamental understanding of R programming and no previous programming experience is expected. The teaching model is learning by doing and basic concepts are built up in an incremental manner.

  The online material is formulated via multiple R code examples that enable the students to work independently when dealing with small R programming tasks.

  This module is designed to prepare you for understanding and performing the computer based exercises and tasks that you encounter in all core MSc in Business Analytics modules and will therefore be completed prior to beginning your course.

• Professional Ethics and Good Academic Practice

  This module aims to cultivate your awareness of some key ethical issues prevalent in data analysis and statistics, in particular those issues emerging in the applications of modern data science.

  You will also develop your awareness of what constitutes good academic practice and learn how to properly reference your work and avoid issues such as plagiarism and poor scholarship in your work.

Core modules:

• Leadership and Organisational Behaviour

  Lectures, guided discussion, case studies, videos and group exercises will be provided. In addition, you are encouraged to make considerable use of your personal experience, both for testing theoretical ideas and for use as living case studies.

  When a business leader is invited, he/she will talk frankly and openly to the class about their methods and experience within a particular sector. This part of the class is presented as Q&A, facilitated by the lecturer.

  You will then be able to discuss the leader’s responses and approach within a case study context.

• 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.

• Quantitative Methods

  Fundamental quantitative concepts and methods are introduced in this module. This is done by getting you familiar with the necessary theoretical background followed by extensive computer-based real life business applications. This all helps to develop your basic practical skills for approaching any basic data analytics task.

  This module teaches you essential probability and statistical concepts which are so useful in order to be able to understand the more complex analytical tools developed in the other modules.

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

Core modules:

• Analytics Methods for Business

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

  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.

• 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.

• 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.

• 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.

In term three you will study:

• Applied research project (20 credits)
• Four electives (10 credits each).

Suitable electives include:

• An Introduction to Machine Learning (with a more statistical focus)
• Building Project Management and Procurement
• Ethics, Society and the Finance Sector
• Fashion Brand Management
• Innovation in Organisations
• International Sponsorship and Public Relations
• Matlab
• Modelling and Data Analysis
• New Market Creation
• Procurement (international elective held in Mannheim)
• Retail Supply Chain Management
• Storytelling for Business

Please note that electives are subject to change and availability.

Research Project

• 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.