| Title: | Data Analytics & Chemometrics |
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| Long Title: | Data Analytics & Chemometrics |
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| Field of Study: |
Statistics
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| Valid From: |
Semester 1 - 2020/21 ( September 2020 ) |
| 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). |
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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
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| 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.
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| No requirements listed |
Module Content & Assessment
| Indicative Content |
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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
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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.
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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.
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Regression
Simple Linear Regression & Correlation. Assumptions, interpreting coefficients, model fitting, model diagnostics.
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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.
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| Assessment Breakdown | % |
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| Course Work | 100.00% |
| Course Work |
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| 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.
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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 |
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- 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 |
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- 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 |
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- 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 |
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- Richard G. Brereton 2014, A short history of chemometrics: a personal view, Journal of Chemometrics, Vol 28
- Harald Martens Quantitative Big Data: where chemometrics can contribute, Journal of Chemometrics, Vol 29
| | Other Resources |
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- Website: WileyJournal of Chemometrics
- Website: Minitab blog posts for learning
statistics
- Website: Wolfram Alpha
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Module Delivered in
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