Who is it for?
Our proposed programme is designed to provide a foundation to those who will determine the scope and direction of data analytics research within their organisation, and communicate the research outcomes to the ultimate decision-makers. Our graduates are trained to participate in the strategic management process, improve the organisation’s financial performance and help design the effective measures of performance of an organisation for which evidence-based data become a strategic asset in the decision-making process. Therefore, the primary goal is to provide an insight into business data analytics and prepare the students to develop the set of skills and attitudes that will evolve into effective leadership skills.
This programme is subject to approval later in the year. Course content may change.
The main purpose of the programme is to develop a comprehensive set of skills and to encourage the positive attributes that are essential to becoming a successful business analyst.
The degree is committed not only to imparting specialist skills, but also to developing the so-called "soft skills” which are important in influencing people and organisations. As well as obtaining effective and persuasive communication skills, you will also learn about ethics-related issues, which are another key ingredient to responsible leadership.
- Extract valuable information from the data in order to create a competitive advantage
- Make use of analytical skills to evaluate and solve complex problems within the organisation’s strategic perspective
- Present and explain data via effective and persuasive communication
- Show commercial focus and the ability of strategic thinking
- Demonstrate depth and breadth of using analytical skills to interrogate data sets
- Illustrate professional integrity and show sensitivity towards ethical considerations.
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.
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 lecturerYou will then be able to discuss the leader’s responses and approach within a case study context.
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.
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.
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.
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.
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:
- An applied research project (20 credits)
- Four electives (10 credits each).
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.
To satisfy the requirements of the degree course, students must complete:
- Eight core modules (15 credits each)
- Four elective modules (10 credits each)
- One applied research project (20 credits).
Assessment of modules on the MSc in Business Analytics, in most cases, is by means of coursework and unseen examination. Coursework takes a variety of formats and may consist of individual or group presentations/reports, set exercises or unseen tests.
There is a compulsory two week induction programme just before Term 1 starts, which is a dedicated introduction to the course and to business analytics. You are required to complete a number of induction workshops at the beginning of the course as follows:
- Team building
- Career induction and careers fair
- Professional development skills.
The teaching staff on the MSc in Business Analytics have many years of practical experience working in industry and are also active researchers in their fields
This knowledge and experience inform the highly interactive lectures that make up the master's degree.
Other module leaders include
How to apply
Documents required for decision-making
- Transcript/interim transcript
- Personal statement (500-600 words)
Documents also required (may follow at later date)
- IELTS/GMAT reports
- Two references, one of which MUST be an academic reference
- Work experience is not a requirement of this course, applicants with in excess of three years of experience should consider the MBA programme
- For a successful application to receive an unconditional status all documents must be verified, so an original or certified copy of the degree transcript must be sent by post to Specialist Masters Programme Office, 106 Bunhill Row, London, EC1Y 8TZ, UK
We cannot comment on individual eligibility before you apply and we can only process your application once it is fully complete, with all requested information received.
If you would like to visit us to discuss your application please do arrange an individual appointment.
A UK upper second class degree or above, or the equivalent from an overseas institution, in a relevant subject is required.
Students with a degree that includes quantitative topics are sought and such degrees are: actuarial science, business, computer science, economics, engineering, finance, geography, mathematics, psychology, sociology, statistics or any other quantitative social science.
GMAT is recommended for students wishing to apply for this course.
English language requirements
If you have been studying in the UK for the last three years it is unlikely that you will have to take the test.
If you have studied a 2+2 degree with just two years in the UK you will be required to provide IELTS results and possibly to resit the tests to meet our requirements.
The required IELTS level is an average of 7.0 with a minimum of 6.5 in the writing section and no less than 6.0 in any other section.
Please note that due to changes in the UKVI's list of SELTs we are no longer able to accept TOEFL as evidence of English language for students who require a CAS as of April 2014.
Fees in each subsequent year of study (where applicable) will be subject to an annual increase limited by the All Items Retail Prices Index. We will confirm any change to the annual tuition fee to you in writing prior to you commencing each subsequent year of study (where applicable).
Deposit: £2,000 (paid within 1 month of receiving offer and non-refundable unless conditions of offer are not met)
First instalment: Half fees less deposit (payable during on-line registration which should be completed at least 5 days before the in-person ID–checks)
Second instalment: Half fees (paid in January following start of course)