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STAT8015 - Data Analytics & Chemometrics

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Title:Data Analytics & Chemometrics
Long Title:Data Analytics & Chemometrics
Module Code:STAT8015
 
Duration:1 Semester
Credits: 5
NFQ Level:Advanced
Field of Study: Statistics
Valid From: Semester 1 - 2020/21 ( September 2020 )
Module Delivered in 1 programme(s)
Module Coordinator: David Goulding
Module Author: Catherine Palmer
Module Description: This module deals with statistical methods used to extract useful information from chemical data in an industrial setting. The learner will develop skills in exploring and analysing data sets using a software package. They will also gain experience in interpreting and communicating the results of statistical analyses.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Review the pertinent issues relating to Data Analytics.
LO2 Explore data sets and use probability distributions to model random variables.
LO3 Recognise experimental design models and analyse associated sets of data.
LO4 Perform correlation and regression analysis.
LO5 Interpret the results of statistical analyses performed by a software package and communicate the results.
Pre-requisite learning
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 CIT 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
 

Module Content & Assessment

Indicative Content
Data Analytics
Introduction to data analytics. Key terminology and technologies, Structured data types, data integrity, data privacy and security. Investigate how data analytics techniques are used in the real-world setting, e.g. case study of Big Data
Statistical Interrogation of Data and Sampling
Introduction: types of data, descriptive and inferential statistics. Probability distributions (discrete and continuous, Binomial and Normal). Sampling approaches (Random, Systematic, Convenience, Cluster, and Stratified). Emphasis on random sampling and the importance of this assumption for the statistical techniques used. Sampling distributions.
Experimental Data Analysis
Statistics as part of the scientific method, data analysis protocol. Observational (vs) experimental data. Confidence intervals –incorporating sample size estimates for precision. The hypothesis-testing framework, one and two sample tests – mean and proportion – power and sample size estimates. The fundamentals of experimental design, one-way and two-way ANOVA, randomised block design, full-factorial design and post-hoc testing. Gauge R&R.
Regression
Simple Linear Regression & Correlation. Assumptions, interpreting coefficients, model fitting, model diagnostics.
Software Analysis
The use of statistical software in the analysis and interpretation of chemical spectral data, based on the application of the various statistical procedures dealt with in the module, will be illustrated through a suitable package e.g. Minitab, R, JMP.
Assessment Breakdown%
Course Work100.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Short Answer Questions Covering descriptive statistics and probability distributions. 1,2 10.0 Week 3
Short Answer Questions Covering confidence and hypothesis tests 2,3 10.0 Week 6
Project Analyse a data set and report the results 2,3,5 20.0 Week 7
Project Analyse a data set and present the results 2,3,4,5 20.0 Week 11
Short Answer Questions Covering hypothesis testing, experimental design and regression. 2,3,4,5 40.0 Week 13
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 institute reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Formal Lecture 1.0 Every Week 1.00
Lab Analysis of data using statistical software 1.0 Every Week 1.00
Independent & Directed Learning (Non-contact) Study, Solving sample problems 5.0 Every Week 5.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 2.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Formal lecture 1.0 Every Week 1.00
Lab Analysis of data using statistical software 1.0 Every Week 1.00
Independent & Directed Learning (Non-contact) Study, Solving sample problems 5.0 Every Week 5.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 2.00
 

Module Resources

Recommended Book Resources
  • James Miller and Jane Miller 2018, Statistics and Chemometrics for Analytical Chemistry [ISBN: 978-129218671]
  • Montgomery D.C. & Runger G.C. 2014, Applied Statistics and Probability for Engineers [ISBN: 978-1-118-744]
Supplementary Book Resources
  • Andy Field, Jeremy Miles and Zoë Field 2012, Discovering Statistics Using R [ISBN: 9781446200469]
  • Matthias Otto 2016, Chemometrics: Statistics and Computer Application in Analytical Chemistry [ISBN: 9783527340972]
Recommended Article/Paper Resources
  • Naveen Kumar, Ankit Bansal, G. S. Sarma, and Ravindra K. Rawal 2014, Chemometrics tools used in analytical chemistry: An overview, Talanta, Vol 123
Supplementary Article/Paper Resources
Other Resources
 

Module Delivered in

Programme Code Programme Semester Delivery
CR_SQSDA_8 Higher Diploma in Science in Quality Systems Validation with Data Analytics 1 Mandatory

Cork Institute of Technology
Rossa Avenue, Bishopstown, Cork

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Email: help@cit.edu.ie