Introduction to Data Analysis and Statistics for Business - BUSN3670

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2024 to 2025
Autumn Term 4 15 (7.5) Epameinondas Katsikas checkmark-circle


Data analytics are a fundamental tool for any organisation that plans to make strategic use of their data assets and enable data-driven decision-making. You cover the core concepts of data analytics (descriptive, predictive, and prescriptive) including: data management; descriptive statistics; inferential statistics; exploratory data analysis; regression modelling; machine learning; programming data-driven solutions; and developing data-driven recommendations.

Our workshops give you experience in using an industry standard programming language, as well as Graphical User Interface - based tools. This gives you the opportunity to choose the most appropriate method for you own future employability needs. You'll learn and develop employability skills in selecting and using appropriate statistical tools to analyse data, effective use of data visualisation techniques and the formulation of data management strategies for business data analytics. You'll be able to critically analyse a problem domain and apply the data analytics approach to support data-driven decision making and facilitate strategy implementation and position your business for growth.


Contact hours

Total contact hours: 27
Private study hours: 123
Total study hours: 150

Method of assessment

Main assessment methods:
On-Line Moodle Test (20%)
Individual Stats Report (1000 words) (20%)
Examination, 2 Hour (60%)

Reassessment method:
100% examination

Indicative reading

Freeman J. et al. (2014) Statistics for Business and Economics. London: Cengage Learning

Swift L. and Piff S. (2014) Quantitative Methods for Business, Management & Finance. Basingstoke: Palgrave Macmillan

See the library reading list for this module (Medway)

Learning outcomes

The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
- Summarise and analyse data and present it effectively to others.
- Use statistical techniques to draw well-founded inferences from quantitative data.
- Identify sources of published statistics, understand their context and report on their wider relevance.
- Apply key mathematical formulae to calculate financial variables for decision-making.

The intended generic learning outcomes.
On successfully completing the module students will be able to:
- Demonstrate numeracy and quantitative skills.
- Scan and organise data and abstract meaning from information.
- Work and study independently, and utilise resources effectively.


  1. Credit level 4. Certificate level module usually taken in the first stage of an undergraduate degree.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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