Modelling with Big Data Analytics

Module description

This module will build on your knowledge of statistics and business analytics, exploring the use of data and modelling in areas such as marketing, operations and HR. You will learn further techniques for working with big data, including interacting with databases, working with unstructured data, and effective use of Python and R for modelling. You will also learn about further modelling approaches, including topics in machine learning and text analytics. There are no pre-requisites but useful complementary modules to have taken in the first year include GEO1419 Introduction to Data Science. Complementary modules in the second year include BEP2140 Business Analytics.

Full module specification

Module title:Modelling with Big Data Analytics
Module code:BEP3140
Module level:3
Academic year:2021/2
Module lecturers:
Module credit:15
ECTS value:






Duration of module: Duration (weeks) - term 1:


Module aims

By taking this module, you will gain an in-depth understanding of Big Data and their relevance in contemporary business environments. You will develop your statistical and computational skills while learning how to use different analytics tools to store, select, process and interpret Big Data. Specifically, you will work in Python and R, which are open-source programming languages widely used in business analytics environments to carry out statistical analysis and data science in relation to Big Data. You will also be introduced to artificial intelligence technologies such as machine learning, to automate analytical model building, and text analytics, to transform unstructured text into data suitable for analytics. Throughout the module, you will learn about the value of Big Data analytics when applied to different business areas such as marketing, operations and HR.

ILO: Module-specific skills

  • 1. Explain what Big Data is and how it is utilised in business environments
  • 2. Recognise different types of Big Data analytics tools (e.g. Phyton, R)
  • 3. Consider ethical implications of Big Data analytics

ILO: Discipline-specific skills

  • 4. Perform data preparation, modelling and interpretation
  • 5. Apply Big Data analytics to business areas such as marketing, operations and HR
  • 6. Conduct data analytics in a secure and privacy-sensitive manner

ILO: Personal and key skills

  • 7. Practice critical and creative thinking when extracting information from data
  • 8. Apply digital tools & online resources to a range of analytical situations and data processing/modelling scenarios
  • 9. Apply and maintain ethical standards in data analysis and modelling

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning and Teaching Activity11Lecture and workshops (11 x 1 hour)
Scheduled Learning and Teaching Activity11Tutorials (11 x 1 hour)
Guided Independent Study128Reading, research and assessment preparation

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
In class quizzes and multiple choice exercisesDuring each class1-9Verbal, in class

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Time Constrained Assessment (TCA)401 hour open book exam1,3,7,9Written
Practical exam602 hour lab based, open book practical exam1-9Written

Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
TCA (1 hour) (40%)TCA (1 hour open book assessment) (40%)1,3,7,9August re-assessment period
Practical exam (2 hours) (60%)Practical exam (2 hour lab based open book practical exam) (60%)1-9August reassessment period

Syllabus plan

Topics discussed on the module include (not exclusively):

  • Introduction to Big Data
  • Big Data storing, selection and ethical issues: data privacy and security
  • Statistical methods for Big Data analytics
  • Computing technologies for Big Data analytics
  • Python and R for modelling
  • Big Data analytics and Machine Learning 
  • Social network analysis
  • Text analysis 
  • Big Data and modelling applications: examples from marketing, operations and HR

Indicative learning resources - Basic reading

The following books are a useful resource for this course:

  • Devlin, B. (2014). Business unIntelligence: Insight and Innovation Beyond Analytics and Big Data. LLC, Technics Publications.
  • Downey, A. (2012). Think Python (2nd Edition). O'Reilly
  • Lemahieu, W., Broucke, S., & Baesens, B. (2018). Principles of Database Management: The Practical Guide to Storing, Managing and Analyzing Big and Small Data. Cambridge University Press.
  • Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt
  • Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. O'Reilly
  • Witten, I., Frank, E., Hall, M., & J Pal, C. (2017). Data Mining Practical Machine Learning Tools and Techniques (4th Edition). Elsevier


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Indicative learning resources - Other resources

A more comprehensive bibliography will be available to students taking this course.

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