DATA9007 - Data Analytics & Visual.

Module Details

Module Code: DATA9007
Title: Data Analytics & Visual.
Long Title: Data Analytics & Visualisation
NFQ Level: Expert
Valid From: Semester 1 - 2024/25 ( September 2024 )
Duration: 1 Semester
Credits: 10
Field of Study: 4816 - Data Format
Module Delivered in: 1 programme(s)
Module Description: In this module, the learner will learn to aggregate, process, and present data while also exploring advanced visualisation techniques relevant to extracting business intelligence, for a manufacturing environment. Best practices in process and product quality monitoring for clinical and commercial manufacturing will also be demonstrated.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Apply data visualisation methods to data consistent with translating business data to business intelligence.
LO2 Critique data using appropriate visualization software.
LO3 Appraise current techniques in process performance and product quality monitoring.
LO4 Categorise and apply linear regression to support decision making
LO5 Describe and implement hypothesis tests to examine the results of business strategies
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
Data visualisation pre-processing techniques
Data cleaning techniques relevant to data visualisation - data aggregation, data sampling, find inconsistencies. Basic descriptive statistics (both graphical and numerical of data description).
Data collection and visualization
Collect and present data in a business context using for example: histograms, boxplots, scatter plots; analysing correlations and patterns between variables. Univariate, bivariate and multivariate ways of presenting data. Application of a software package such as MINITAB or R to the different aspects of statistical analysis dealt with in this course.
Advanced Visualisation Techniques & Dashboards
Utilise appropriate software-based tools for visualisation and dashboard creation e.g. Power BI & Tableau; examine how these packages can be connected to data sources, e.g. databases.
Process Performance & Product Quality Monitoring:
Variability, sampling data, establishment & implementation of a control strategy as a basis for process performance & product quality monitoring system, analysis of parameters & attributes, control charts, identification of variation, providing knowledge to enhance process understanding, best practices in process and product quality monitoring for clinical and commercial manufacturing.
Regression and Correlation
Simple linear regression, method of least squares, coefficient of determination, confidence intervals and prediction intervals, correlations coefficient, significance tests in regression and correlation
Hypothesis testing and Business Relevance:
Terminology and logic of hypothesis testing; null and alternative hypotheses, test statistic, reference distribution, p-value and its implications. Emphasis on t-tests and F-tests. Assumptions underlying tests, parametric versus non-parametric tests. ANOVA: one-way, two-way and general factorial models.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Multiple Choice Questions % of Total Mark 20
Timing Week 7 Learning Outcomes 1,2,3
Assessment Description
Multiple Choice Question exam: Data visualisation methods, formulating data & process performance and product quality monitoring techniques
Assessment Type Project % of Total Mark 25
Timing Week 10 Learning Outcomes 1,2,3,4,5
Assessment Description
Evaluate and implement a visualisation technique to solve a industry work based problem or using assigned data files as appropriate; research, critique and communicate the data analytics topic.
Assessment Type Presentation % of Total Mark 15
Timing Week 12 Learning Outcomes 1,2
Assessment Description
Class presentation of project work with a focus on translation of data into a way that business stakeholders can readily interpret.
Assessment Type Other % of Total Mark 40
Timing Week 13 Learning Outcomes 1,2,3,4,5
Assessment Description
Laboratory Assessment: practical competency through laboratory-based learning.
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 2.00 2
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 9.00 9
Total Hours 14.00
Total Weekly Learner Workload 14.00
Total Weekly Contact Hours 5.00
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 2.00 2
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 9.00 9
Total Hours 14.00
Total Weekly Learner Workload 14.00
Total Weekly Contact Hours 5.00
 
Module Resources
Recommended Book Resources
  • Allan Bluman. (2017), Elementary Statistics: A Step By Step Approach, 10th. [ISBN: 9781259755330].
  • Darren S. Starnes & Josh Tabor. (2019), The Practice of Statistics, 6th. [ISBN: 9781319269296].
  • J.R. Evans. (2012), Business Analytics: Methods, Models and Decisions, International Pearson, [ISBN: 0133051714].
  • Ben Fry. (2007), Visualizing Data, O'Reilly Media Sebastopol, CA, [ISBN: 0596514557].
  • Quentin Brook. (2022), LEAN SIX SIGMA AND MINITAB, 7th. [ISBN: 9780995789951].
Supplementary Book Resources
  • Nathan Yau. (2011), Vizualise, [ISBN: 0470944889].
  • Kabacoff, Robert I.. (2015), R in Action, 2nd. Manning, New York, [ISBN: 9781617291388].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_ERNPI_9 Master of Science in Regulated New Product Introduction & Technology Transfer 1 Mandatory