Bachelor of Science (BSc)

BSc Information Technology Management for Business

  • Duration: 3 years
  • Year of entry: 2025
  • UCAS course code: GN51 / Institution code: M20
  • Key features:
  • Scholarships available

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Course unit details:
Data Analytics with Programming Tools

Course unit fact file
Unit code BMAN24771
Credit rating 20
Unit level Level 5
Teaching period(s) Semester 1
Available as a free choice unit? No

Overview

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/co-requisites

Unit title Unit code Requirement type Description
Fundamentals of Data Analytics BMAN11060 Pre-Requisite Compulsory

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 

Aims

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.

Learning outcomes

Syllabus

  • 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.

Teaching and learning methods

Lectures: 22  

Practical classes & workshops: 18  

Independent study hours: 160 hours 

Knowledge and understanding

  • 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.

Intellectual skills

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

Practical skills

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

Transferable skills and personal qualities

  • 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. 

Assessment methods

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%)

Feedback methods

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

Recommended reading

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. 

Study hours

Scheduled activity hours
Lectures 22
Practical classes & workshops 18
Independent study hours
Independent study 160

Teaching staff

Staff member Role
Eghbal Rahimikia Unit coordinator
Manuel Lopez-Ibanez Unit coordinator

Additional notes

For Academic Year 2025/26 

Updated: March 2025

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