Module Details

Module Code: DATA8010
Title: Introduction to Data Analytics
Long Title: Introduction to Data Analytics
NFQ Level: Advanced
Valid From: Semester 1 - 2020/21 ( September 2020 )
Duration: 1 Semester
Credits: 5
Field of Study: 4816 - Data Format
Module Delivered in: 1 programme(s)
Module Description: This module explores the impact of analytics in industry while examining several insightful case studies from industry.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Apply techniques from statistics and data science for various types of data from industrial processes.
LO2 Critically analyse case studies where data science and analytics are used in the setting of an industrial process.
LO3 Describe the importance of a data system in an individual site and network running manufacturing processes.
LO4 Apply best practice and high-tech solutions to data management and modelling.
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
How data is collected, processed, analysed and stores in line with current regulations around data integrity. Select the appropriate statistical technique to analyse a problem.
Data Analytics and Systems
The structure of data within systems and sourcing data from systems including data lakes and data warehouses.
Big Data Analysis
A big data strategy with appropriate tools such as Hadoop and Teradata.
Presentation Skills
Effective communtication to a wide audience of both data analytics specialists and a general audience.
Case Studies
A study of how data analytics is used in a real world setting with data systems and modelling skills being brought together to generate actionable information.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Project % of Total Mark 30
Timing Week 8 Learning Outcomes 1,2,4
Assessment Description
A written case study exploring the setting up of a data system within an organisation and examining the architecture, implementation and use of the system in everyday operations.
Assessment Type Project % of Total Mark 70
Timing Week 13 Learning Outcomes 1,2,3,4,5
Assessment Description
A written case study focusing on an instance in which data systems and analysis have been installed and appropriately qualified within an industrial setting. Explore how this has been implemented using best practice and where appropriate communicate additional methods that could have improved the instance. Summarise the findings of the analysis in a clear written report and oral presentation.
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 2.00 2
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 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
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 2.00 2
Lab Contact Development of practical competency through laboratory-based learning. Every Week 1.00 1
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 4.00 4
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
 
Module Resources
Recommended Book Resources
  • Richard K. Burdick, David J. LeBlond, Lori B. Pfahler et al.. (2017), Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry, 1st. Springer, [ISBN: 3319501844].
Supplementary Book Resources
  • John A Rice. (2007), Mathematical Statistics and Data Analysis, 3rd. Duxbury Press, [ISBN: 9780495118688].
  • Samson Weisberg. (2013), Applied Linear Regression, 4th. Wiley, [ISBN: 9781118386088].
Supplementary Article/Paper Resources
This module does not have any other resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_SPRDA_8 Certificate in Process Data Analytics 2 Mandatory