Overview
Who is it for?
The MSc in Business Analytics programme will help you generate and capture greater competitiveness in data-driven business.
Our Business Analytics 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.
Typical backgrounds of students are Accounting, Biology, Business Administration/Studies, Computer Science, Economics, Engineering, Environmental Studies, Finance, Hospitality Management, Human Resources, Information Systems/Technology, Management, Marketing, Mathematics and Psychology.
Objectives
The main purpose of the master's in Business Analytics 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 master's 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.
Structure
On the master's in Business Analytics, you will learn to:
- 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.
Induction Weeks
All of our MSc courses start with two compulsory induction weeks which include relevant refresher courses, an introduction to the careers services and the annual careers fair.
Pre-study modules
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.
Python and R tutorials run in small groups during the induction week and the first two weeks of Term 1. These tutorials assume that the students are very familiar with the online material from the Introduction to Python and Introduction to R Programming pre-study modules.
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.
Term 1
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.
Term 2
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.
Term 3
In term three you will study:
- Applied research project (20 credits)
- Four electives (10 credits each).
Suitable electives include:
- Applied Natural Language Processing (NLP)
- Applied Machine Learning
- Building Project Management and Procurement
- Data Management Systems
- Ethics, Society and the Finance Sector
- Fashion Brand Management
- Innovation in Organisations
- International Sponsorship and Public Relations
- Matlab
- 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.
In the past, most students have chosen projects designed by our industry partners that aim to develop the consulting skills of each student. For this academic year, students chose from twenty projects offered by various analytics consultants, live events, and insurance companies. Some of the projects are directly supervised by the industry partner representatives.
Assessment methods
Assessment
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.
Professional development
There is a compulsory one 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.
During this period, a variety of activities are offered to students, to support them in their learning and professional development. Cass Careers offers workshops with a focus on the key skills that employers are looking for, as well as preparing students for the application process. The annual MSc Careers Fair at this time provides the opportunity to meet more than 60 companies who are recruiting across many sectors including consulting, insurance, finance, energy, and other fields.
During the year you will also get the opportunity to attend employer events such as recruitment sessions designed to make you more aware of the job opportunities and career pathways open to you. There will also be industry information sessions to help you build and maintain your commercial awareness, a key skill which employers are looking for in their candidates. Examples of the employers who are set to meet the Business Analytics students this year are Accenture, British Airways, Ekimetrics and EY.
Cass Careers also provides a range of workshops and online resources and one-to-one appointments to help you gain key employability skills and information to help you with your career planning and throughout the job search process.
Ask a student
Chat to one of our current master's students now about applying for a MSc at Cass.
Term dates
Term dates 2020/21
In-Person ID Checks (all students must attend): Commences 14 September 2020
Compulsory Induction: 14 - 25 September 2020
Term I
28 September 2020 - 11 December 2020
Term I exams
11 January 2021 - 22 January 2021
Term II
25 January 2021 - 09 April 2021
Term II exams
26 April 2021 - 07 May 2021
Term III
10 May 2021 - 09 July 2021
Term III exams
12 July 2021 - 23 July 2021
Resit period
Students who are required to resit an examination or invigilated test will do so in the period:
09 - 21 August 2021
Submission deadline for Business Research Project or Applied Research Project
30 August 2021
Official Course End Date
30 September 2021
Timetables
Course timetables are normally available from July and can be accessed from our timetabling pages. These pages also provide timetables for the current academic year, though this information should be viewed as indicative and details may vary from year to year.
Please note that all academic timetables are subject to change.
Teaching staff
Course Director - Dr Vali Asimit
Vali's research interests include analysis of multidimensional extremes, robust decision making and robust data visualisation.
Module Leaders of the compulsory Business Analytics core modules
Dr Daniel Beunza
Daniel is Associate Professor in Management and he is the Module Leader in Leadership and Organisational Behaviour. Daniel is an award-winning instructor with years of experience in teaching MBA students at Columbia Business School, MSc students at the London School of Economics and bank executives in the City of London. Daniel’s research examines the role of technology in organisations, and his study of the use of algorithms in the New York Stock Exchange was recently profiled in the Wall Street Journal.
Alan Chalk
Alan is a Data Scientist at Sabre Insurance Company Limited where he applies Machine Learning techniques to insurance related tasks. He started his career as an Actuary and worked in in non-life insurance where his focus was on predictive analytics and pricing. Whilst serving as Global Aerospace Actuary at American International Group (AIG) UK, Alan worked with AIGs dedicated Machine Learning Team. Following this, he expanded his training in statistics and data science with an MSc in Statistics at Sheffield University and an MSc in Machine Learning at University College London. Alan teaches the Term 3 Applied Machine Learning Module. He is also part of the MSc in Business Analytics Industry Partners Programme that offers industry-based projects designed by Alan that aim to enhance the experiential learning when the Term 3 Applied Research Project is developed. Such projects are directly supervised by the industry partner contact person(s).
Dr Oben Ceryan
Oben is a Lecturer in Operations and Supply Chain Management and he is the Module Leader of the Revenue Management and Pricing module. He obtained his PhD degree in engineering from the University of Michigan. His research interests are in dynamic pricing and revenue management, an emerging area that aims to enhance firms’ profitability by aligning demand with constrained supply through the integration of operations and marketing decisions and that is applicable to a wide range of industries from manufacturing to hospitality, and from online marketplaces to retailing.
Dr Rosalba Radice
Rosalba is a Reader in Statistics and she is the Module Leader of the Quantitative Methods and the Analytics Methods for Business modules. She obtained her PhD degree in Statistics from the University of Bath. Her research interests are in distributional regression, simultaneous joint equation models, copula regression modelling, generalized additive modelling and applications in wide range of applied areas. She has extensive experience with teaching applied statistics courses including regression models and computational data mining methods. Rosalba co-developed the GJRM package (former SemiParBIVProbit and SemiParSampleSel packages) in R since 2011 that led to over 60,000 downloads; the package is mainly addressed to a wide variety of practitioners that aims to model additive distributional joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association, endogeneity, non-random sample selection or partial observability.
Dr Pedro Rodrigues
Pedro is the module leader of the Strategic Business Analytics module he is one of the industry partners that designs and supervises the summer Applied Research Projects. He holds a PhD degree in Computer Science from Imperial College London. Pedro teaches applications of Machine Learning to key stakeholders from different backgrounds in Finance and regularly presents at reputable conferences and venues. Pedro is the CEO of Savvy Data Insights, where he leads an experienced and highly skilled team that help organisations solve their business problems using innovative solutions based on cutting edge technology and Artificial Intelligence/Machine Learning Algorithms. He is also part of the MSc in Business Analytics Industry Partners Programme that offers industry-based projects designed by Pedro that aim to enhance the experiential learning when the Term 3 Applied Research Project is developed. Such projects are directly supervised by the industry partner contact person(s).
Dr Simone Santoni
Simone Santoni is a Lecturer in Strategy at the Cass Business School, where he leads, among others, the Network Analytics and Data Visualisation teaching modules. He obtained a PhD in Organizations and Markets from the University of Bologna and refined his studies at Columbia University and New York University. Simone's research concentrates on the network foundations of organizations and markets―especially markets for culture and labour. Throughout the years, he has consulted for prominent organizations operating in the recording music industry, theatre, and contemporary art.
Dr Elizabeth Stephens
Dr. Elizabeth Stephens is the Founder & Managing Director of Geopolitical Risk Advisory, a consultancy that uses data analytics to advise clients on how geopolitical risks will impact upon their specific trading relationships and investments. For nine years prior to this, she was the Head of Credit & Political Risk Advisory at JLT Specialty where she provided corporate clients with strategic advice on the identification, management and mitigation of country risk. Elizabeth has a Ph.D. in International Relations from the London School of Economics and is a guest Lecturer at Cass Business School and Henley Business Schools, where she delivers Masters and Executive Education courses on Political Risk Management and Business Analytics. Elizabeth is also involved in teaching the Strategic Business Analytics module. She is also part of the MSc in Business Analytics Industry Partners Programme that offers industry-based projects designed by Elizabeth that aim to enhance the experiential learning when the Term 3 Applied Research Project is developed. Such projects are directly supervised by the industry partner contact person(s).
Dr Rui Zhu
Rui is a Lecturer in Statistics and she is the Module Leader of the Machine Learning module. She obtained her PhD degree in statistics from University College of London and her research is in statistical learning, pattern recognition, high-dimensional data analysis and interdisciplinary applications for real-world problems. Rui’s research interests include classification and dimension reduction for high-dimensional data, distance metric learning and real-world applications, such as spectral data analysis, image quality assessment and hyperspectral image analysis.
Application
How to apply
Documents required for decision-making
- Transcript/interim transcript
- Current module list (if still studying)
- CV
- 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 the Master's 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.
Individual Appointments
If you would like to visit us to discuss your application please do arrange an individual appointment.
Terms and conditions
Students applying to study at Cass Business School are subject to City, University of London's terms and conditions.
Entry requirements
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
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.
IELTS
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.
Fees
Fees in each subsequent year of study (where applicable) will be subject to an annual increase of 2%. We will confirm any change to the annual tuition fee to you in writing prior to you commencing each subsequent year of study for continuing students (where applicable).
Deposit: £2,000 (usually paid within 1 month of receiving offer and non-refundable unless conditions of offer are not met)
First installment: Half fees less deposit (payable during on-line registration which should be completed at least 5 days before the in-person ID–checks)
Second installment: Half fees (paid in January following start of course)
Course information and statistics
