Program Overview

Business Data Analytics MSc

The Business Data Analytics MSc is designed for early to mid-career students who want to specialize in business data analytics and learn in an applied setting. This master's program is led by Cranfield's 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 the full-time MBA program.

Overview

• Start date: 28 September 2026
• Duration: 1 year
• Delivery: Taught modules 60%, thesis 40%
• Qualification: MSc, PgDip, PgCert
• Study type: Full-time
• Campus: Cranfield campus

Who is it for?

The Business Data Analytics MSc has been designed for early to mid-career students who want to specialize in business data analytics and learn in an applied setting.

Class Profile 2024/25

• Gender: Male 77%, Female 23%
• Age range: 21 - 46 years
• Average age: 29
• Number of nationalities: 8
• Nationality: UK 23%, International 77%
• Total number of students: 13
• Average class size: 13

Why this course?

• Cranfield School of Management consistently performs well in international business rankings, ranked 5th in the UK and 29th in Europe in the Financial Times European Business School Rankings 2025.
• Opportunity to undertake an individual thesis in conjunction with an external organization, presenting findings to senior managers.
• Develop knowledge and skills in business data analytics, self-awareness, and personal development critical to career progression.
• Benefit from close connections with international businesses, using learning approaches based on real-world problems to develop practical and distinctive skills.
• Gain analytical skills to identify routes to sustainable competitive advantage for organizations and communities worldwide.

Course Details

The course comprises 6 core modules, each delivered with 40 hours of class contact time and a further 160 hours of study time, giving 200 notional learning hours per module. The thesis component is a total of 80 credits.

Compulsory Modules

• Artificial Intelligence and Machine Learning
  • Aim: Introduce core Artificial Intelligence (AI) concepts, architectures, methods, and tools.
  • Syllabus: AI Concepts, Paradigms, Intelligent Agents, Intelligent Search, Knowledge Representation, Logic Programming, Inference and Reasoning, Planning, Learning, Reinforcement Learning, Machine Learning, Deep Learning, AI Challenges, AI-driven Innovation in Products and Services.
  • Intended learning outcomes: Appraise main AI concepts, evaluate use cases, evaluate key AI architectures, apply machine learning approaches, implement and deploy machine-learning techniques.
• Business Analytics and Management
  • Module Leader: Professor Andrew Angus
  • Aim: Provide the ability to collect, process, analyze, and present relevant data to support evidence-based decision making.
  • Syllabus: Principles of business analytics, literature reviews, research strategies, research designs, planning a management project, data collection and cleaning, data analysis in R software environment, research ethics, statistical analysis of data, probability theory, sampling, structured interviews and questionnaires, hypothesis testing, correlation and regression analysis.
  • Intended learning outcomes: Critically evaluate theoretical principles, appraise usefulness of data analytics methods, create and communicate robust recommendations, practice high ethical standards.
• Business Analytics and Optimisation
  • Module Leader: Professor Ying Xie
  • Aim: Provide a comprehensive understanding of prescriptive analytics techniques and their application within a business context.
  • Syllabus: Introduction to Prescriptive Analytics, Optimisation techniques, Linear, Goal and Non-Linear Programming Models, Decision Trees, Multi-criteria Decision Making, Simulations, Prescriptive analytics in Marketing, Finance, Operations and Supply Chain Management, HR.
  • Intended learning outcomes: Explain various techniques used in prescriptive analytics, deploy simulation to gain insights, critically evaluate limitations and strengths of prescriptive analytical techniques, appraise options and select appropriate techniques.
• Descriptive Analytics
  • Module Leader: Dr. Lakshmy Subramanian
  • Aim: Provide knowledge, skills, and behaviors for acquiring data and creating datasets that are fit-for-purpose.
  • Syllabus: Types of data and data sources, research ethics, data cleaning, principles of scenario analysis, descriptive statistics, data mining, introduction to R software environment.
  • Intended learning outcomes: Design a robust program of systematic data collection, employ robust processes of data collection, cleaning, transformation, and validation, evaluate appropriateness of different data analytics methods, effectively communicate findings.
• Predictive Analytics
  • Module Leader: Dr. Vineet Agarwal
  • Aim: Provide required skills for structuring predictive research projects, including conceptualizing research questions and managing data.
  • Syllabus: Formulating research questions, managing predictive data, ethical considerations, cross-sectional and time-series regressions, time-series analysis, Generalised Method of Moments, application of predictive analytics in R software environment, cluster analysis, neural networks, text analysis.
  • Intended learning outcomes: Conceptualize and formulate research questions, appraise suitability of predictive methods, critically evaluate model assumptions, judge performance of predictive models.
• Programming for Business Analytics
  • Module Leader: Dr. Irene Moulitsas
  • Aim: Provide necessary skills and knowledge to develop software solutions using Python.
  • Syllabus: Python program structure, data types, basic and advanced language constructs, functional and object-oriented methodologies, built-in and third-party libraries, software design principles and practices.
  • Intended learning outcomes: Evaluate object-oriented and functional programming methodologies, solve problems using Python, formulate a solution based on good software design principles, employ class and functional-based libraries, deploy a working knowledge of a programming language.

Teaching Team

The program is taught by faculty experts with extensive industry experience, including:

• Professor Andrew Angus, Course Director
• Professor Ying Xie
• Dr. Lakshmy Subramanian
• Dr. Vineet Agarwal
• Dr. Irene Moulitsas

Your Career

The Careers and Employability Service offers comprehensive support to help develop career management skills. Graduates can expect to apply their skills in private sector organizations, public sector, non-governmental organizations, and education, in areas such as finance, consulting, retail, manufacturing, and pharmaceuticals.

How to Apply

To apply, register to use the online system and submit an application form along with supporting documentation. Application deadlines are:

• For international and European students requiring a visa: Monday, 29 June 2026
• For UK and Irish students: Monday, 21 September 2026

Fees and Funding

• Home: ｣15,500 (MSc Full-time), with a deposit of ｣1,000
• Overseas: ｣29,650 (MSc Full-time), with a deposit of ｣3,000 Funding opportunities include Merit Scholarships, International Scholarships, Prodigy Finance Student Loans, and the Cranfield University GREAT Scholarship.

Entry Requirements

Applicants must usually hold a UK lower second-class undergraduate degree with honors or an equivalent international qualification, with a quantitative methods module or a thesis with significant elements of quantitative methods. English language proficiency is also required, with approved tests including IELTS, TOEFL, Cambridge Assessment English, and others, each with specific score requirements.