MSc Business Analytics and Artificial Intelligence

Fees for entry in 2027 have not yet been set. For reference, the fees for the academic year beginning September 2026 were as follows:

• MSc (full-time)
  UK students (per annum): £20,000
  International, including EU, students (per annum): £35,700

The fees quoted above are fully inclusive of tuition, administration and computational costs.

Fees for entry are subject to yearly review. The University reserves the right to increase your tuition fee by up to 7% each year for courses lasting more than one year, including to reflect rising costs associated with delivering our educational and wider student experience. Postgraduate fees information .

Always contact the admissions team if you are unsure which fee applies to your qualification award and method of attendance.

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

We know that student finance can be complicated. The links below provide further information to help guide you.

Learn more about - student finance options for UK students.

Learn more about - fees and finance for international students.

Graduates of The University of Manchester and Manchester Metropolitan University can receive a 10% discount on their master's degree tuition fees as part of our Manchester Alumni Loyalty Discount scheme.

This course covers the fundamentals of machine learning as a core area of artificial intelligence, introduces key machine learning techniques across three main categories: supervised learning, unsupervised learning, and reinforcement learning, and explores the challenges and solutions of applying machine learning to solve real-world business problems.

BMAN60422 Programme Req: BMAN60422 is only available as a core unit to students on MSc Business Analytics and as an elective to students on MEng (Hons) Computer Science

BMAN60422 is available as a core unit to students on MSc Business Analytics and as an elective with permission to students on other relevant MSc programmes. 

• Provide students with an understanding of learning as a core area of artificial intelligence and its applications in business.
• Introduce key machine learning techniques across three main categories: supervised learning, unsupervised learning, and reinforcement learning.
• Explore the challenges and solutions of applying machine learning to solve real-world business problems, with an emphasis on model interpretability, data quality issues, and imbalanced datasets. 

At the end of the course unit, student should be able to:

• Understand the fundamentals of data analytics and its application to business and management decision making,
• Understand a variety of data analysis techniques, such as data preprocessing, classification and clustering, prediction and forecasting, association rule mining & text mining, etc.,
• Discuss how visual analytics can be used to understand big data, extract insights and identify patterns,
• Demonstrate the ability to use specialised software tools to analyse large sets of data in real-world problems.

• Introduction to Machine Learning: providing an overview of the fundamental concepts and definitions of machine learning in the context of artificial intelligence.
• Supervised Learning: covering the basics from descriptive to predictive analytics and introducing various predictive modelling techniques, such as regressions, decision tree models, and neural networks.
• Unsupervised Learning: exploring clustering analysis, association analysis, and collaborative filtering.
• Reinforcement Learning: studying reinforcement learning and sequential decision-making in a stochastic environment.
• Ensemble Learning: exploring bootstrap aggregating, boosting, stacking, and XGBoost.
• Interpretability in Machine Learning: discussing the importance of interpretable machine learning for business applications.
• Machine Learning for Business: challenges and solutions: addressing key challenges in machine learning for business, such as data quality issues and imbalanced datasets, while exploring their potential solutions and future advancements. 

Formal Contact Methods

Minimum Contact hours: 20 

Delivery format: Lecture and Workshops 

• Understand the fundamentals of machine learning in the context of artificial intelligence
• Understand and critically evaluate key machine learning techniques across the three main categories: supervised learning, unsupervised learning, and reinforcement learning.
• Understand the importance of interpretable machine learning for business applications.

• Frame real-world business problems through a machine learning lens and identify the appropriate modelling paradigm for a given problem.

• Demonstrate the ability to use specialised software tools and programming packages to analyse large datasets and build machine learning models for real-world business problems.

• Develop digital and teamwork skills through case studies and coursework project.

50% Exam

50% Group Coursework

• Generic feedback posted on Blackboard regarding overall examination performance. 
• Written and/or verbal comments on assessed or non-assessed coursework. 
• Responses to student emails and questions from a member of staff including feedback provided to a group via an online discussion forum. 

Galit Shmueli,et al.; Data Mining for Business Analytics: Concepts, Techniques, and Applications - in R (e-book available from the university library) or in Python, John Wiley & Sons, 2018. 

INFORMS Analytics Magazine, http://www.analytics-magazine.org/  

Informal Contact Method

• Office Hours

• Online Learning Activities (blogs, discussions, self assessment questions)