INMR95-Business Data Analytics

Module Provider: Business Informatics, Systems and Accounting
Number of credits: 20 [10 ECTS credits]
Terms in which taught: Autumn term module
Non-modular pre-requisites:
Modules excluded:
Current from: 2023/4

Module Convenor: Dr Markos Kyritsis

Type of module:

Ultimately, the aim of this course is to provide students with an understanding of how to manage data, analyse data, develop predictive models, and then use predictive models to develop future recommendations for business related problems.


Summary module description:

This module introduces key concepts, methods, and tools for business data analytics. Data analytics are a fundamental tool for any organisation that plans to make strategic use of their data assets, and enables data-driven decision making. Core concepts that lend themselves to the three stages of data analytics (i.e., descriptive, predictive, and prescriptive) will be covered, including: data management; descriptive statistics; inferential statistics; exploratory data analysis; regression modelling; machine learning; programming data-driven solutions; and developing data-driven recommendations. The workshops will give students experience in using an industry standard programming language, as well as GUI-based tools, thus providing them with the opportunity to choose the most appropriate method for their own future employability needs.


To satisfy this general aim, students will acquire key knowledge and skills in:

• Accessing, storing, and handling univariate and multivariate data 

• Exploring and analysing data 

• Visualising data 

• Developing and comparing predictive models 

• Formulating data-driven decision making strategies 

Assessable learning outcomes:

On completion of this module, the student should be able to: 

  •  Select and use appropriate statistical tools to analyse data 

  • Demonstrate effective use of data visualisation techniques 

  • Formulate data management strategies for business data analytics 

  • Critically analyse a problem domain and apply the data analytics approach to support data-driven decision making 

Additional outcomes:

The student should: 

  • Become familiar with the industry standard data analytics and visualisation tools 

  • Become familiar with concepts and tools in data management 

  • Become familiar with software development tools and approaches surrounding data analytics 

Outline content:

Data Management 

  1. Data types and sampling methods 

  2. Storing, handling, and preparing data for analysis 

  3. Data visualisation Techniques 

Descriptive Analytics 

  1.  Descriptive Statistics 

  2.  Statistical Inference 

  3. Exploratory Data Analysis 

Predictive Analytics 

  1. Regression Modelling 

  2. Machine Learning 

Prescriptive Analytics 

  1. Programming for data analytics 

  2. Recommendations and solutions development 

Brief description of teaching and learning methods:

This module will be a combination of lectures, tutorials and practical workshops that will enable students to acquire key concepts and practical skills in data analytics. It assumes no prior knowledge or experience in data analytics, therefore students are expected to do a fair amount of wider reading. Data sets related to business problems will be provided as ‘case studies’ to individual students, who will then have to apply descriptive, predictive, and prescriptive analytics in order to form recommendations. The process will be documented and submitted as a report that is worth 100% of their grade.

Contact hours:
  Autumn Spring Summer
Lectures 10
Tutorials 10
Practicals classes and workshops 10
Guided independent study:      
    Wider reading (independent) 50
    Wider reading (directed) 20
    Advance preparation for classes 10
    Preparation for tutorials 20
    Preparation of practical report 30
    Essay preparation 40
Total hours by term 200 0 0
Total hours for module 200

Summative Assessment Methods:
Method Percentage
Report 100

Summative assessment- Examinations:


Summative assessment- Coursework and in-class tests:

In Spring term, submission of an individual report of 4,000 words comprising the analysis, model building, simulations, and recommendations for addressing the business question using a data-driven approach. The word limit is absolute (i.e., we do not allow +/-10%), and anything outside the word limit will not be marked

Formative assessment methods:

Students will be given feedback on the progress of their individual project through tutorials and practical sessions.

Penalties for late submission:

Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information:

Assessment requirements for a pass:

50% in coursework

To pass the module, students must demonstrate satisfactory understanding of concepts as well as demonstration of basic use of tools for data analytics and interpretation of results. 

For Merit level performance, students must demonstrate competence in formulating business data analytics solutions through suitable application of tools and critical appreciation of results. 

For Distinction level performance, students must demonstrate a high level of competence in critical formulation of business data analytics solutions and a highly competent application of appropriate tools and critical analysis when interpreting the results. 


Reassessment arrangements:

(During the August University Resit Period):

Resubmission of coursework report. 


Additional Costs (specified where applicable):

Cost Amount
1. Required text books: James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: springer.   £53.99


Last updated: 30 March 2023


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