Drawing on faculty from across 果酱视频官网, the course is led by Cranfield鈥檚 Economics and Banking Group, which has been consistently ranked in the World top 10 in the Financial Times Global MBA Ranking for its teaching of economics in relation to our full-time MBA programme.
By studying this master鈥檚 in business data analytics, you will be immersed in a varied, stimulating, and experiential learning environment. Taught modules consist of formal lectures, in-class discussions and computer-based practical sessions, placing an emphasis on the practical application of business analytics.
Overview
- Start date30 September 2024
- Duration1 year
- DeliveryTaught modules 60%, thesis 40%
- QualificationMSc, PgDip, PgCert
- 果酱视频官网 typeFull-time
- CampusCranfield campus
Who is it for?
The Business Data Analytics MSc has been designed for early to mid-career students who want to specialise in business data analytics and learn in an applied setting.
Why this course?
- Cranfield School of Management consistently performs well in international business rankings. We are ranked 8th in the UK and 37th in Europe in the Financial Times European Business School 2023 Rankings.
- You will have the opportunity to undertake an individual thesis in conjunction with an external organisation, presenting findings to senior managers from the organisation involved.
- You will develop your knowledge and skills in business data analytics, develop your self-awareness and undergo personal development, critical to career progression.
- You will benefit from our close connections with international businesses, by using learning approaches based on real-world problems you will develop skills that are practical and distinctive.
- Gain analytical skills which will enable you to identify routes to sustainable competitive advantage for organisations and communities across the world.
Informed by Industry
An external advisory panel informs the design and development of the course, and comprises senior management practitioners, reinforcing its relevance to the modern business world. Many of our faculty have held senior positions in industry and continue to engage with industry through consultancy and teaching. They are also supported by a team of international visiting industry speakers from influential financial organisations and professors who bring the latest thinking and best practice into the classroom.
Course details
This course comprises 6 core modules. Each delivered module comprises 40 hours of class contact time with a further 160 hours of study time to consolidate learning and carry out assignments, giving 200 notional learning hours per module. The thesis component of the module is a total of 80 credits.
Course delivery
Taught modules 60%, thesis 40%
Thesis
You will have an opportunity to undertake an individual thesis in conjunction with an external organisation, presenting findings to senior managers from the organisation involved.
The aim of the thesis module is to develop your ability to undertake a major business data analytics related research project and to give you hands-on experience of a data analytics management issue or situation through researching, reporting, and presenting on a project.
Course modules
Compulsory modules
All the modules in the following list need to be taken as part of this course.
Artificial Intelligence and Machine Learning
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Aim |
To introduce core Artificial Intelligence (AI) concepts, architectures, methods and tools. This will highlight the potential of AI for aiding innovation, enabling you to develop a practical knowledge of AI-enabled solutions development process for product and service innovation. Further this module will introduce you to machine learning for big data applications. |
Syllabus |
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Intended learning outcomes |
Upon successful completion of this module, you should be able to:
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Business Analytics and Management
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Aim |
Business analysts are frequently asked to gather, review and analyse business and industry data to produce robust, meaningful recommendations to senior managers. This requires the combination of a range of knowledge, skills and behaviours. For instance, the selection of quality data, building reliable databases and models, applying statistical models, deriving and communicating meaning from the findings. This module aims to provide you with the ability to collect, process, analyse and present relevant data that will support evidence-based decision making. In addition, the module will also provide a platform which will help you engage with internal or external 鈥渃lients鈥, undertake a project and, consequently, be able to make coherent and compelling recommendations to senior managers. The module will further develop programming skills using the R software environment. |
Syllabus |
The principles of business analytics: The nature of quantitative analysis: |
Intended learning outcomes |
On successful completion of this module you should be able to:
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Business Analytics and Optimisation
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Aim |
Prescriptive analytics has the power to help businesses use data to determine the optimal course of action. Prescriptive analysis works with data collected or generated from a wide range of descriptive and predictive sources and creates algorithms to facilitate decision making. It accounts for existing conditions, constraints and the results of each possible decision, while also evaluating potential consequences in different scenarios. Prescriptive analytics is a valuable tool that informs decisions and strategies and can be used alongside subjective judgement to find the best possible solutions among various options. The module aims to provide you with a comprehensive understanding of prescriptive analytics techniques and their application within a business context. It aims to equip you with both knowledge and transferable skills necessary for making data-driven decisions and generating optimal solutions to complex business problems. This process will be facilitated through the use of spreadsheet-based software packages and Python software. You will have an opportunity to develop your own prescriptive models and apply them to various business problems in areas such as marketing, finance, operations and supply chain management, and HR. |
Syllabus |
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Intended learning outcomes |
On successful completion of this module you should be able to:
This module is distinctive because it will provide you with the opportunity to gain experience of quantitative tools and techniques to solve realistic business problems using Excel and Python software packages. |
Descriptive Analytics
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Aim |
The usefulness of the outputs of data analytics is dependent on the quality of data used in the analysis. In a world awash with data, it is critical that analysts understand how to recognise and harness appropriate data. Further, data visualisation is a key method of communicating important outcomes to stakeholders. This module is designed to provide you with the knowledge, skills and behaviours for acquiring data and creating datasets that are fit-for-purpose. Using data, you will learn to apply a range of data visualisation techniques, such as scenario building, data mining and descriptive statistics, which will enable you to communicate research findings effectively to key stakeholders. The module will also introduce the R software environment and give you experience of using R to produce descriptive outputs. |
Syllabus |
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Intended learning outcomes |
On successful completion of this module you should be able to:
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Predictive Analytics
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Aim |
This module is designed to provide you with the required skills for structuring predictive research projects including conceptualising research questions and managing data. It explores the use of different methods for making predictions about future outcomes, using historical data. It also explores the validity of these empirical models and the nature of the uncertainty inherent in them. |
Syllabus |
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Intended learning outcomes |
On successful completion of this module you should be able to:
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Programming for Business Analytics
Module Leader |
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Aim |
The Python programming language has become a key language for business analysts and software developers in both desktop and internet/cloud network-based environments. This module aims to provide you with the necessary skills and knowledge to develop software solutions to problems in these fields using Python. The principle and advanced elements of Python, associated libraries/toolboxes, programming methodologies and good design principles are covered. Hands-on programming exercises culminating in the construction of a fully functional three-tier application form an essential part of the course. |
Syllabus |
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Intended learning outcomes |
On successful completion of this module you should be able to:
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Keeping our courses up-to-date and current requires constant innovation and change. The modules we offer reflect the needs of business and industry and the research interests of our staff. As a result, they may change or be withdrawn due to research developments, legislation changes or for a variety of other reasons. Changes may also be designed to improve the student learning experience or to respond to feedback from students, external examiners, accreditation bodies and industrial advisory panels.
To give you a taster, we have detailed the compulsory and elective (where applicable) modules which are currently affiliated with this course. All modules are indicative only and may be subject to change for your year of entry.
Teaching team
The programme is taught by faculty experts who have extensive industry experience and who regularly work with major organisations around the world. The Course Director for this course is Professor Andrew Angus.
Your career
The Careers and Employability Service offers a comprehensive service to help you develop a set of career management skills that will remain with you throughout your career.
During your course you will receive support and guidance to help you plan an effective strategy for your personal and professional development, whether you are looking to secure your first accounting and finance role or wanting to take your career to the next level.
The market for data analysts is widely recognised as one of the fastest growing job markets. On completion of this course, graduates can expect to apply their skills in a range of private sector organisations in areas such as finance, consulting, retail, manufacturing, and pharmaceuticals. Graduates can also expect to find opportunities to apply their skills in the public sector, non-governmental organisations, and education.
How to apply
Our students do not always fit traditional academic or career paths. We consider this to be a positive aspect of diversity, not a hurdle. We are looking for a body of professional learners who have a wide range of experiences to share. If you are unsure of your suitability for our Business Data Analytics MSc programme we are happy to review your details and give you feedback before you make a formal application.
To apply you will need to register to use our online system. Once you have set up an account you will be able to create, save and amend your application form before submitting it.
Application deadlines
There is a high demand for places on our courses and we recommend you submit your application as early as possible.
Entry for September 2024
- Applications from international and European students requiring a visa to study in the UK must submit their application by Friday 12 July 2024.
- There is no application deadline for UK applicants, but places are limited, so we recommend you submit your application as early as possible.
Once your online application has been submitted together with your supporting documentation, it will be processed by our admissions team. You will then be advised by email if you are successful, unsuccessful, or whether the course director would like to interview you before a decision is made. Applicants based outside of the UK may be interviewed either by telephone or video conference.
Read our Application Guide for a step-by-step explanation of the application process from pre-application through to joining us at Cranfield.