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.

The course provides a computer-based, application-oriented introduction to business statistics and forecasting with focus on core predictive models.

 

BMAN71791 Programme Req: BMAN71791 is only available as a core unit to students on MSc Business Analytics and as an elective to students on MSc Data Science (Business & Management pathway)

Undergraduate level Multivariate Calculus, Statistics and Algebra.
 

 

This course unit covers statistical analysis and modelling techniques with an emphasis on multivariate statistical applications in business and predictive analytics. We also consider time-series models. The aim of the course is to develop students' understanding of data analysis, statistical hypothesis testing and multivariate and predictive techniques.

 

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

•  Understand the fundamentals of basic statistical techniques and models.
•  Understand and design models for predictive analytics, particularly to be comfortable with multivariate linear regression.
•  Obtain hands-on experience with the statistical analysis software R and the R Studio interface, to perform basic statistical analyses.
 

 

Formal Contact Methods

Minimum Contact hours: 20 

Delivery format: Lecture and Workshops 

60% Exam

40% Coursework

 

•  Informal advice and discussion during a lecture, seminar, workshop or lab.
•  Online discussion forum.
•  Written and/or verbal comments on assessed or non-assessed coursework.
•  Generic feedback posted on Blackboard regarding overall examination performance.

Core Texts

Anderson, D. R. (Ed.). (2010). Statistics for business and economics (2nd ed.). Andover: South-Western Cengage Learning.

 Everitt, B. and Hothorn, T. (2011) An Introduction to Applied Multivariate Analysis with R . New York, NY, Springer New York. doi:10.1007/978-1-4419-9650-3.

 Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning : Data Mining, Inference, and Prediction  (Second Edition) Springer, New York. doi:10.1007/978-0-387-84858-7

Shumway, R. H. and Stoffer, D. S. (2017) Time series analysis and its applications with R examples (Fourth Edition). Cham, Switzerland, Springer. doi:10.1007/978-3-319-52452-8.


Additional texts

The books by Hadley Wickham (Chief Scientist at RStudio, and an Adjunct Professor of Statistics at the University of Auckland, Stanford University, and Rice University, and creator of many of the main R libraries and the philosophies behind them -  http://hadley.nz/) are excellent. They are available free online e.g.
·    R for Data Science https://r4ds.had.co.nz/
·    ggplot2: Elegant Graphics for Data Analysis (Use R!) https://ggplot2-book.org/


You can also find well-informed answers to most technical queries you may have (with R or statistics) by googling! There are also very many other free resources and ‘how to’ examples on the web for data visualisation with R.
 
On the forecasting side, the book by Rob J Hyndman and George Athanasopoulos (Monash University, Australia) is excellent and its application and code uses R. It is free online:
·    Forecasting: Principles and Practice (2nd Edition) https://otexts.com/fpp2/

Husson, F. (2011) Exploratory multivariate analysis by example using R. Sébastien. Lê & Jérôme. Pagès. Boca Raton , CRC Press.
 
David Ray Anderson (2010) Statistics for business and economics (Second Edition). Andover, South-Western Cengage Learning.
 
Shoesmith E., Sweeney D., Anderson D., Williams T., et al. (2014) Statistics for business and economics. (Third Edition). Andover , Cengage Learning.
 
Hogg, R., McKean, J., and Craig, A. (2005) Introduction to Mathematical statistics. (Sixth Edition). Upper Saddle River, N.J, Prentice Hall.
 
Wooldridge, J.M. (2016) Introductory econometrics : a modern approach  (Sixth Edition). Boston, MA, Cengage Learning.
 

Informal Contact Methos

Office Hours

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