Modelling with Big Data Analytics

Module title	Modelling with Big Data Analytics
Module code	BEP3140
Academic year	2021/2
Credits	15
Module staff

Duration: Term	1	2	3
Duration: Weeks	11

Number students taking module (anticipated)	30

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.

Module aims - intentions of the module

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.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

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

On successfully completing the module you will be able to...

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

On successfully completing the module you will be able to...

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

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

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

Scheduled Learning and Teaching Activities	Guided independent study	Placement / study abroad
22	128	0

Details of learning activities and teaching methods

Category	Hours of study time	Description
Scheduled Learning and Teaching Activity	11	Lecture and workshops (11 x 1 hour)
Scheduled Learning and Teaching Activity	11	Tutorials (11 x 1 hour)
Guided Independent Study	128	Reading, research and assessment preparation

Formative assessment

Form of assessment	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
In class quizzes and multiple choice exercises	During each class	1-9	Verbal, in class

Summative assessment (% of credit)

Coursework	Written exams	Practical exams
40	0	60

Details of summative assessment

Form of assessment	% of credit	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
Time Constrained Assessment (TCA)	40	1 hour open book exam	1,3,7,9	Written
Practical exam	60	2 hour lab based, open book practical exam	1-9	Written

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

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

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 (2^nd 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 (4^th Edition). Elsevier

Indicative learning resources - Other resources

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

Key words search

Business Analytics, Big Data, Modelling

Credit value	15
Module ECTS	7.5
Module pre-requisites	None
Module co-requisites	None
NQF level (module)	6
Available as distance learning?	No
Origin date	10/05/2021
Last revision date	15/07/2021