Module Details

Module Code: STAT8017
Title: Food Business Analytics
Long Title: Food Business Analytics
NFQ Level: Advanced
Valid From: Semester 2 - 2024/25 ( January 2025 )
Duration: 1 Semester
Credits: 5
Field of Study: 4620 - Statistics
Module Delivered in: no programmes
Module Description: This module will provide learners with an understanding of the use of statistics, analytics, data visualisation and data storytelling in the context of the Food Business industry. Students will gain knowledge and experience of the role that data plays in business decisions - including sourcing and collecting data, interrogating data through exploratory data analysis, and visualising data for effective communication. Students will be equipped with a set of tools that will enable them to make business decisions that are supported by appropriate data usage.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Apply the methods of descriptive statistics to organise, summarise, present and analyse data.
LO2 Calculate and interpret summary statistics relevant to the food business industry.
LO3 Collect, manage, interrogate and visualise real world data.
LO4 Create effective data driven narratives that will enable decision making in the food business industry.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Types of Data
Introduction to Qualitative and Quantitative data - overview of nominal, ordinal, discrete, and continuous data types. Differences between structured and unstructured data in the context of data within the food business industry.
Data Visualisation Concepts
History of data visualisation. Understand the various categories used in the field e.g., infographics and visual analytics. Overview of theory and best practice in the field of data visualisation. Investigate and implement computer based tools for visualisation.
Data Collection and Presentation
Collection and presentation of data in the business context. Basic descriptive statistics (both graphical and numerical). Examine relevant case studies from business/finance applications to assess best practice.
Data Visualisation: Traditional Statistical Approaches
Explore and implement various statistical data visualisation techniques including: histograms, boxplots, scatter plots, techniques to present univariate, bivariate and multivariate data, and analysis of patterns and correlations between variables.
Advanced Visualisation Techniques and Dashboards
Utilise appropriate software-based tools for visualisation and dashboard creation e.g. Excel, Power BI, Tableau. Examine how these packages can be connected to data sources.
Data Storytelling and Decision Making
Introduction to the role that effective data storytelling can play in the decision making process within the context of the food business industry.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Practical/Skills Evaluation % of Total Mark 30
Timing Week 7 Learning Outcomes 1,2
Assessment Description
Assessment on the use of software in the calculation of descriptive statistics and presentation of data.
Assessment Type Project % of Total Mark 40
Timing Week 12 Learning Outcomes 3,4
Assessment Description
Project on the collection, interrogation and visualisation of data relevant to Food Business.
Assessment Type Presentation % of Total Mark 30
Timing Week 12 Learning Outcomes 3,4
Assessment Description
Data storytelling presentation based on project and data narrative that will drive business decision making.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivery of content and material underpinning learning outcomes Every Week 1.00 1
Lab Contact Practical skills development Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Review of course material Every Week 4.00 4
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivery of content and material underpinning learning outcomes Every Week 1.00 1
Lab Contact Practical skills development Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Review of course material Every Week 4.00 4
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
 
Module Resources
Recommended Book Resources
  • Cole Nussbaumer Knaflic. (2015), Storytelling with Data, John Wiley & Sons, [ISBN: 1119002257].
  • Jorge Camões. (2016), Data at Work, New Riders, [ISBN: 0134268636].
Supplementary Book Resources
  • Nathan Yau. (2024), Visualize This, Wiley, [ISBN: 1394214863].
This module does not have any article/paper resources
Other Resources