Module Details

Module Code: DATA8009
Title: Process Data Analysis with R
Long Title: Process Data Analysis with R
NFQ Level: Advanced
Valid From: Semester 1 - 2020/21 ( September 2020 )
Duration: 1 Semester
Credits: 10
Field of Study: 4816 - Data Format
Module Delivered in: 1 programme(s)
Module Description: In this module students will learn how to format, manipulate, visualise and analyse process data using the statistical software package R.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Analyse process data using R, data management, databases data protection and data integrity.
LO2 Evaluate the functionality of the R statistical programming language.
LO3 Perform data cleaning, manipulation and wrangling techniques to specified data problems.
LO4 Implement appropriate data modelling including visualisaton techniques for manufacturing process data.
LO5 Develop best practice in terms of reproducible documentation and version control.
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
Base R
Learn how to navigate an IDE with R including how to load/save a file, load a package, access help etc. Examine the base R objects - vectors, matrices, arrays, lists, factors and tables; their respective characteristics, naming conventions and structures. Understand subsetting, filtering and creation of these objects. Examine the implementation of control structures (loops and functions) in R. Investigate how R can be used for mathematical and statistical calculations.
Data Cleaning and Manipulation in R
Understand the tidyverse suite of packages and how they can be used for data wrangling and data manipulation. Learn how to use regular expressions and pattern recognition in R for data cleaning purposes.
Visualisation
Learn how basic plots are generated in R - histograms, X-Y plots. Understand the ggplot2 package for advanced plotting.
Statistical Testing
Understand how R can be used for sampling and simulation techniques such as bootstrapping, Monte Carlo method, simulating sample distributions, checking hypothesis testing. Investigate how R can be used in statistical modelling techniques (e.g., naive Bayes classifers).
Reproducible Documentation and Version Control
Learn how R and R Markdown can be used to produce documents for reproducible research and results. Implement version control through the integration of Git in R.
Modelling
Learn how statistical models can be used to gain insights from data and to generate predictions. Model process data analysis including statistical process control and process capability.
Compliance
Learn about the requirements of regulators in different fields e.g., GDPR, data integrity.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Project % of Total Mark 50
Timing Week 7 Learning Outcomes 1,2,3
Assessment Description
Perform data wrangling, data manipulation and apply an appropriate visualisation technique to examine a real-world data set. Produce a short report on work performed.
Assessment Type Project % of Total Mark 50
Timing Sem End Learning Outcomes 3,4,5
Assessment Description
Design and implement an appropriate data modelling and visualisation solution to a specified data set. Document findings in a scientific 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 Theory on course topics and discussion of industry relevant examples. Every Week 3.00 3
Lab Contact Development of practical competency through laboratory-based learning. Every Week 3.00 3
Independent & Directed Learning (Non-contact) Non Contact Review of lecture notes and recommended material and preparation of reports for selected laboratory sessions and in-class topics. 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 Theory on course topics and discussion of industry relevant examples. Every Week 3.00 3
Lab Contact Development of practical competency through laboratory-based learning. Every Week 3.00 3
Independent & Directed Learning (Non-contact) Non Contact Review of lecture notes and recommended material and preparation of reports for selected laboratory sessions and in-class topics. 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
  • Crawley, Michael. (2012), The R Book, 2nd. Wiley, [ISBN: 9780470973929].
  • Kabacoff, Robert. (2011), R in Action, 2nd. Manning Publications, [ISBN: 1935182390].
Supplementary Book Resources
  • Burdick, R.K., LeBlond, D.J., Pfahler, L.B., Quiroz, J., Sidor, L., Vukovinsky, K., Zhang, L.. (2017), Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry, Springer, [ISBN: 9783319501864].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_SPRDA_8 Certificate in Process Data Analytics 1 Mandatory