Module Details

Module Code: STAT9007
Title: Industrial Data Analysis
Long Title: Industrial Data Analysis
NFQ Level: Expert
Valid From: Semester 1 - 2019/20 ( September 2019 )
Duration: 1 Semester
Credits: 10
Field of Study: 4620 - Statistics
Module Delivered in: 2 programme(s)
Module Description: This module provides the learner with advanced training in statistical methods relevant to data analysis in the design and planning of industrial processes. It will address best practice statistical methodologies and applications within an industrial setting.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Use statistics to reduce complex industry related data situations to manageable formats in order to describe, explain or model them.
LO2 Derive descriptive statistics for various data types to complement industrial processes.
LO3 Perform and critique statistical tests on industry related data. Set up and critically analyse data sets in both a parametric and non-parametric way.
LO4 Use multiple regression and other advanced statistical techniques to allow prediction of a score of one variable on the basis of the scores on several other variables.
LO5 Effectively communicate statistical findings on industry relevant data in a clear concise manner using correct terminology based on the output from an appropriate programming language.
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
Overview
Statistics fills the crucial gap between information and knowledge. Industry cannot run effectively on the basis of hunches or trial and error. This topic covers how we collect, process, analyse and store data in line with current GDPR legislation. With a particular focus on the statistical analysis of data including which statistics to use, why to use those statistics, and when to use them.
Introduction to data analysis
Through using appropriate descriptive statistics, it is possible to analyse collected data effectively and produce a coherent report with suitable justification. This entails deriving the correct measures of centrality/variation, if applicable, along with interpreting bar charts, pie charts, histograms, stem-and-leaf plots, boxplots, x-bar charts, Shewhart control charts, etc.
Statistical inference (two samples)
Understand the idea behind hypothesis testing through worked examples of tests of normality/differences/relationships with various types of industry relevant data - i.e., independent and related t-tests; Mann-Whitney U and Wilcoxon test; Pearson and Spearman Rank correlation.
Multi-variable analysis
Set up and analyse various data sets in both a parametric and non-parametric way. In the case of non-parametric data, suitable data transformations will be investigated prior to the use of parametric tests. Analysis of Variance (ANOVA) with suitable posthoc testing. Between and within subjects factorial experiments. Investigation of the effect size of a result and the power to a test.
Multiple regression
Scatterplots and partial regression plots. Test for homoscedasticity. Detect for multicollinearity and outliers. Check that the residuals (errors) are approximately normally distributed and random. Interpret regression equations and use them to make predictions.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Project % of Total Mark 25
Timing Week 5 Learning Outcomes 1,2,5
Assessment Description
Use the appropriate descriptive statistics tools to suggest whether a difference exists between at least two industry related measurements. Summarise all results in a concise report.
Assessment Type Project % of Total Mark 25
Timing Week 9 Learning Outcomes 1,2,3,5
Assessment Description
Test the significance of a hypothesis/effect/power of a difference/relationship between two industry related measurements. Summarise results in a concise report.
Assessment Type Project % of Total Mark 50
Timing Sem End Learning Outcomes 1,2,3,4,5
Assessment Description
Using industry relevant data, test the significance of hypotheses that differences/relationships exist between more than two measurements. Apply posthoc tests to determine the presence of statistical differences, if applicable. Summarise results in a concise report.
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 Statistical theory and application Every Week 3.00 3
Lab Contact Laboratory workshops Every Week 3.00 3
Independent & Directed Learning (Non-contact) Non Contact Data analysis Every Week 8.00 8
Total Hours 14.00
Total Weekly Learner Workload 14.00
Total Weekly Contact Hours 6.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Statistical theory and application Every Week 3.00 3
Lab Contact Laboratory workshops Every Week 3.00 3
Independent & Directed Learning (Non-contact) Non Contact Data analysis Every Week 8.00 8
Total Hours 14.00
Total Weekly Learner Workload 14.00
Total Weekly Contact Hours 6.00
 
Module Resources
Recommended Book Resources
  • Jake VanderPlas. (2016), Python Data Science Handbook: Tools and Techniques for Developers, 1. O'Reilly Media, [ISBN: 9781491912058].
Supplementary Book Resources
  • Sarah Boslaugh. (2012), Statistics in a Nutshell, 2. O'Reilly Media, [ISBN: 978144931682].
  • Wes Mckinney. (2017), Python for Data Analysis, 2. O'Reilly Media, [ISBN: 978149195766].
  • Allen B. Downey. (2014), Think Stats, 2. O'Reilly Media, [ISBN: 978149190733].
  • Hadley Wickham, Garrett Grolemund. (2016), R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, 1. O'Reilly Media, [ISBN: 1491910399].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_EMENG_9 Master of Engineering in Mechanical Engineering 1 Group Elective 1
CR_EINMS_9 Postgraduate Certificate in Intelligent Manufacturing Systems 1 Mandatory