BSc Information Technology Management for Business

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 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.

Syllabus (indicative curriculum content):  

• Introduction to programming in a leading data analytics programming language.   
• Programming basics: Data structures    
• Programming basics:  For-loops and conditional statements.  
• Programming basics:    

   Usage of packages (libraries), read/write external data and standard statistical summary functions.   

• Data visualisation.   
• Introduction to Network Analytics.   
• Introduction to Predictive modelling.
• Introduction to Text analytics.

Lectures: 22 Hours

Practical classes & workshops: 18 Hours

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, and which consist of a mix of open-ended questions, multiple choice questions, calculated response questions, fill in the blanks, and others. 

Summative Assessment:

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

Coursework accompanied of coding script (40%)

• General feedback released on Canvas
• Individual feedback released on Canvas

• An introduction to statistical learning : with applications in R – Author: James, Gareth Additional Person Name: Witten, Daniela,; Hastie, Trevor,; Tibshirani, Robert

 

• Introductory Statistics for Business and Economics : Theory, Exercises and Solutions – Author: Ubøe, Jan.

 

• Applied statistics for business and management using Microsoft Excel – Author: Herkenhoff, Linda, Additional Person Name: Fogli, John, 

 

• Networks : an introduction – Author: Newman, M. E. J. (Mark E. J

 

• An Introduction to Applied Multivariate Analysis with R – Author: Everitt, Brian. author. Additional Person Name: Hothorn, Torsten. Author

 

• Text mining in practice with R – Author: Kwartler, Ted, 

 

• Storytelling with Data: A Data Visualization Guide for Business Professionals – Author: Nussbaumer Knaflic, Cole 

 

• Wiley & Sons, Incorporated Place of Publication: Hoboken Publication Date: 2015 
   

Tuition fees for home students commencing their studies in September 2027 will be £10,050 per annum. Tuition fees for international students will be £37,000 per annum.

For general information please see the undergraduate finance pages.

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.

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).

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As an international student you may be eligible for our Global Futures Scholarships . This is open to students starting their studies in September 2027.