BSc Information Technology Management for Business

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.

The  Manchester Bursary  is available to UK students registered on an undergraduate degree course at Alliance MBS who have had a full financial assessment carried out by Student Finance England.

In addition, Alliance MBS will award a range of  Social Responsibility Scholarships  to UK and international/EU students.

These awards are worth £2,000 per year across three years of study. You must achieve AAA at A-level (or equivalent qualification) and be able to demonstrate a significant contribution and commitment to social responsibility.

The School will also award a number of  International Stellar Scholarships  to international students achieving AAA at A-level (or equivalent qualification). Applicants who exceed AAA and/or have supplementary qualifications (such as EPQ) will receive additional consideration.

Additional eligibility criteria apply - please see our  scholarship pages  for full details.

With the increasing availability of personal, social and business data, data analytics has become an essential and important part of business analytics and business intelligence. This importance is driven by the versatility and flexibility provided by the large variety of data analytics techniques and the more frequent and mainstream use of data analytic programming languages such as R, for problem-solving. This course unit will therefore continue to introduce students to new methods for data analytics, emphasizing the potential flexibility provided by a mainstream data analytics programming language. This unit will provide students with an introduction to a statistical/data analytics programming language in order to tackle multiple types of data sources and to be able to provide insights into different business avenues.  

Students undertaking this unit will increase their data analytics toolbox, will learn how to use one of the most popular data analytic programming languages, and will learn how to create different visualisations and dynamical reports for the effective communication of business insights. 

Pre-requisite units: BMAN11060 Fundamentals of Data Analytics

Core/Compulsory/Optional/ Free Choice: Core and only available for BSc ITMB with/without IPE.

Programmes to which this course unit contributes (including cross faculty/school): BSc (Hons) Information Technology Management for Business/BSc (Hons) Information Technology Management for Business with Industrial Experience 

Students will learn how to use a prominent data analytics programming language. They will gain practical experience in different analytical techniques, such as network analytics and predictive modelling. This course, in addition to the analytical techniques, will also emphasize in the creation and usage of programmable visualisations for the communication of business insights by means of lab studies and/or reports.

• Introduction to programming in a leading data analytics programming language.  
• Programming basics (Data structures, Functions, For-loops and conditional statements).
• Usage of packages (libraries), read/write external data and standard statistical summary functions.  
• Data management and preparation.  
• Data preprocessing.
• Data visualisation.
• Network Analytics.
• Predictive modelling.
• Text analytics.

Lectures: 22  

Practical classes & workshops: 18  

Independent study hours: 160 hours 

• Explain the fundamental functionalities of one of the most popular programming languages for data analytics.
• Explain the core theoretical principles underlying data analytics models and tools.

• Analyse and model business datasets using data analytics models and tools to support decision-making. 

• Apply data analytical methods for descriptive tasks.
• Apply data analytical methods for predictive tasks.

• Demonstrate the ability to use one of the most popular data analytics programming languages.
• Demonstrate the ability to analyse different types of data.
• Demonstrate the ability to read data visualisations.
• Demonstrate the ability to produce effective data visualisations. 

Formative Assessment:

Online quizzes reinforcing the understanding of content provided and of reading material

Summative Assessment:

Two mid-term quizzes (2x 30% = 60%)

Individual report accompanied of coding script (40%)

• General feedback released on VLE 
• Individual feedback released on VLE 

Anderson, D. R.. (2010). Statistics for business and economics (Second edition). Andover: South- Western Cengage Learning.  

Knaflic. (2015).Storytelling with data: a data visualization guide for business professionals. Wiley.  

Newman, M.E.J (2010) Networks : an introduction . Oxford, Oxford University Press.  

Everitt, B. and Hothorn, T. (2011) An Introduction to Applied Multivariate Analysis with R . New York, NY, Springer New York.  

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013) An Introduction to Statistical Learning : with Applications in R . New York, NY, Springer New York.  

Alan Agresti (2007) Introduction to categorical data analysis (Third Edition). Hoboken, NJ, Wiley. 

For Academic Year 2025/26 

Updated: March 2025