For more information about Business Analytics at City, University of London, please visit the webpage using the button above.
Who is it for?
Our proposed programme is designed to provide a foundation for those who will determine the scope and direction of data analytics research within an organisation, and communicate the research outcomes to the ultimate decision-makers. Our graduates are trained to participate in the strategic management process, improve the financial performance and advise about designing effective measures of performance of an organisation for which evidence-based data becomes 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 encourage positive attributes that are essential to becoming a successful business analyst.
The degree is committed not only to creating specialist skills, but also to developing the so-called ``soft skills' aimed to influence people and organisations. Besides achieving effective and persuasive communication, the module leaders draw attention over ethics-related issues, which is another key ingredient to responsible leadership.
The MSc in Business Analytics starts in the summer before the beginning of Term 1 with three compulsory pre-courses which ensure that every student has the minimum specific background required by all other 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 discussed 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 discussed 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 should be expected. In addition, the students are encourage to make considerable use of their 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 a Q&A, led by the lecturer, but enabling you to ask your own questions. Further, once the interviewee has left, you will discuss their 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, which enables to understand the significance of data by placing in a visual context. You should expect to learn different approaches to data visualisation (e.g., pattern recognition or 'data storytelling') and to mobilise these approaches in order to reach various types of audiences.
Fundamental quantitative concepts and methods are introduced in this module by familiarising you with the necessary theoretical background followed by extensive computer-based real life business applications that are meant to develop basic practical skills for approaching any basic data analytics task. This module teaches you essential probability and statistical concepts useful to understand more complex analytical tools developed in all other modules and provides the foundation of using the R programming language to translate the 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 and 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 on overview of various machine learning concepts, techniques and algorithms used in practice to describe and analyse complex data, and design predictive analytics methods. You should expect to grasp the main idea and intuition behind modern machine learning tools from a practical experience. 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, demand forecasts under market uncertainty etc.
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. The analytic methods do not play the leading role and instead, 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
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:
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 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:
How to apply
Documents required for decision-making
Documents also required (may follow at later date)
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.Entry requirementsGMATEnglish language requirementsEntry requirements
A UK upper second class degree or above, or the equivalent from an overseas institution.
Some level of previous study in finance or quantitative methods is preferred.GMAT
GMAT is recommended for students wishing to apply for this course.English language requirements
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.
Notes about fees for this course
UK/EU/International£23,000 Tuition fees are subject to annual change
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 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)
Contact City, University of London to find course entry requirements.
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