Business Analytics MSc
Key information
Duration: 12 months
Attendance mode: Full-time
Fees: from £21,000 (more information)
Location: Bunhill Row
Start of programme: September 2024
Application deadline: August 2024
Entry year: Showing course information for 2024
Build in-demand data analysis and leadership skills that help you find and communicate opportunities
Overview
Business Analytics MSc Who is it for?
Choose the MSc Business Analytics programme to support your career progression if your aim is to generate and capture greater competitiveness in data-driven business. You’ll appreciate our technology-aided learning-by-doing teaching style, academic rigour and authentic experiential learning.
You don’t need previous experience of analytics skills, or the technology enablers needed to deploy them. Instead, you can join our pre-courses which equip you with the minimum knowledge, such as Python and R Programming, before you start your one year MSc Business Analytics Programme.
You’re likely to already have at least an upper second class degree, or the equivalent, in a subject that includes quantitative topics, such as accounting, biology, business administration/studies, computer science, economics, engineering, environmental studies, finance, hospitality management, human resources, information systems/technology, management, marketing, mathematics or psychology.
Why choose this course?
- Train in contemporary analytics skills where academic rigour and industry practice are on the forefront of our teaching practice.
- Undertake industry sponsored summer projects that should be a priority to all Business Analytics students
- Understand how real-life business solutions are powered by analytics from a variety of sectors.
Course objectives
In our Master’s in Business Analytics programme you’ll develop a comprehensive set of contemporary skills and nurture the positive attributes essential to becoming a successful business analyst. You’ll learn the specialist and technical skills, data science, data analytics and technology skills , and soft skills that are important for influencing people and leading organisations.
You’ll benefit from the distinctive experiential learning element of your business analytics MSc. Every student is encouraged to engage with industry sponsored projects which ensures that dissertation projects are matched to your skills and interests, and provide you with hands-on real-life work experience.
Recent students have chosen projects offered by analytics consulting firms, finance and insurance companies and well-known retailers, including Bank of England, Ekimetrics, Fiat Chrysler Automobiles, Government Actuary's Department, Maserati, Rolls-Royce and Vodafone UK.
Teaching staff
Other module leaders include:
- Dr Rosalba Radice
- Dr Hugo De Sousa
- Dr Elizabeth Stephens
- Dr Philippe Blaettchen
- Dr Rui Zhu
Course content
On the MSc Business Analytics course, you will:
- Build skills and connections that equip you for a career in the fast growing area of data-driven business
- Explore business processes that are core for all successful organisations, including management, finance and measuring performance
- 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.
Course structure
Programme content is subject to change. We regularly review our module offering and amend to keep up to date and relevant.
Induction weeks
The MSc in Business Analytics starts with two compulsory induction weeks which include 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:
- Professional Ethics and Good Academic Practice
- Introduction to Python Programming
- Introduction to R Programming
These subjects are key elements of your course and you are strongly encouraged to complete the modules before you arrive at Bayes 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.
Term 1
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.
Term 2
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.
Term 3
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
Download course specification:
Business Analytics MSc [PDF]Assessment methods
Term dates
Term dates 2024/25
- Induction: 9th September 2024 - 20th September 2024
- Term one: 23rd September 2024 - 6th December 2024
- Term one exams: 6th January 2025 - 17th January 2025
- Term two: 20th January 2025 - 4th April 2025
- Term two exams: 21st April 2025 - 2nd May 2025
- Term three - international electives: 5th May 2025 - 16th May 2025
- Term three: 19th May 2025 - 4th July 2025
- Term three exams: 7th July 2025 - 18th July 2025
- Resits: 11th August 2025 - 22nd August 2025
- Additional resit week - tests only: 25th August 2025 - 29th August 2025.
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.
Fees & funding
UK/Home fee
September 2024 entry
£21,000
Tuition fees are subject to annual change.
International fee
September 2024 entry
£31,500
Tuition fees are subject to annual change.
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 start of the induction period).
Second installment: Half fees (paid in January following start of course).
Scholarships & bursaries
Scholarships, sponsorships, loans and other funding could support your education at Bayes Business School.
Learn about the cost of living as a Bayes student in London.
Scholarships
We have a range of scholarships for Master's degrees at Bayes Business Scool. Most scholarship applications for 2024/25 year of entry will open in January 2024.
View our scholarships and fundingOther funding opportunities
Scholarships are very competitive, you may wish to look other options for funding, including the government PG Loan.
View other funding optionsSponsorship
Students on the course who are sponsored in full or in part by their employer will need to complete a sponsorship form as part of the application process.
View our sponsorship guidanceCareers
With your business analytics master’s you’re ready to help many types of organisation enhance their practices and head for success.
Our graduates work in a variety of roles, but mainly as business analysts or data analysts. Some work for consultancies and professional services businesses, and others for financial firms, banks and technology companies.
Our careers team support you right from the start, with careers induction and a careers fair before you start your studies, workshops to prepare you for the application process and employer events, as well as advice on choosing your career path.
During the year you will 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.
Class of 2022
Recent graduates have secured positions such as:
- Digital Personalisation Specialist
- Data Analyst
- Technology Architect
- Business Integration and Architecture Analyst
- Data Scientist
- Analytics Consultant.
Download our latest MSc Employment Report
Recent employers
Alumni stories
Entry requirements
- A UK upper second class degree or above, or the equivalent from an overseas institution.
- 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 not required for application, but may be requested as a condition of offer at the discretion of the Admissions Panel.
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 an English language test.
If you have studied in the UK at degree level for less than three years (e.g. 3+1, 2+1, 2+2, etc.) you will be required to provide the results of an approved English language test and possibly resit the test to meet our academic entry requirements.
Full list of approved English language tests/qualifications and minimum requirements.
Apply
Please see our Application Guide for details of the documents you will need to supply as part of your application, and other useful information.
We cannot comment on individual eligibility before you apply. We can only make a decision on your application once it is fully complete, with all requested information received.
Frequently asked questionsTerms and conditions
Students applying to study at Bayes Business School are subject to City, University of London's terms and conditions.
Student life
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