| Semester 1 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
Basic knowledge of statistics from A-level mathematics or equivalent
N/A
This module aims t0o introduce students to a variety of data analysis techniques that can be used for modelling and analysis of large datasets, aka “big data”, typically encountered in the process industries.
Key themes for the module: Multivariate Data Analysis: Introduction: What problems can be addressed using these techniques; Preliminary Data Analysis – Handling of Inhomogeneous Data (Missing Data; Outliers; Noisy Data; Time Alignment); Graphical Procedures. Dimensionality Reduction (Principal Component Analysis); Modelling techniques: Multiple linear regression, Principal component regression; Projection to Latent Structures. Multivariate Statistical Performance Monitoring – Continuous and Batch Processes. Model simplification. Analysis of Variance. Confidence Intervals. Non-linear modelling techniques. Machine Learning techniques.
To develop the knowledge of the students, through their exposure to a raft of methodologies (data pre-screening, feature extraction and process modelling) that are applicable both in the laboratory and the production plant, thereby enabling them to help in the delivery of enhanced process performance, process understanding and process optimisation.
To develop an awareness of the advantages and disadvantages of the different methodologies (data pre-screening, feature extraction and process modelling) presented for the analysis of industrial data.
To develop the critical ability of the students enabling them to identify the most appropriate methodologies for the problem to be addressed (data pre-screening, feature extraction and process modelling).
The ability to understand the fundamental statistical techniques that form the basis of multivariate methods and how they relate to the analysis methods.
The ability to interrogate the results from the execution of a multivariate data analysis in the context of the problem being addressed, e.g. to realise an enhanced understanding of process operation, and to determine their validity and applicability for solving the problem.
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 30:00 | 30:00 | Problem Solving Exercise 2 and subsequent writing up in report format -summative assessment |
| Guided Independent Study | Assessment preparation and completion | 1 | 10:00 | 10:00 | Problem Solving Exercise, formative assessment on pre-treatment of data |
| Scheduled Learning And Teaching Activities | Lecture | 18 | 1:00 | 18:00 | Present in Person |
| Scheduled Learning And Teaching Activities | Small group teaching | 6 | 2:00 | 12:00 | Numerical practice sessions - Computing Labs |
| Guided Independent Study | Independent study | 1 | 30:00 | 30:00 | Review lecture material and prepare for small group teaching |
| Total | 100:00 |
Lectures convey the statistical concepts and theory and their application in process engineering. Numerical practice sessions support the learning introduced in lectures through the students having the opportunity to apply the concepts to a number of problems varying in terms of complexity. The numerical practice sessions allow the completion some of the assignment work.
The format of resits will be determined by the Board of Examiners
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Computer assessment | 1 | M | 100 | Assessed report - Process Data Modelling (set Week 6) -2000 words |
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Computer assessment | 1 | M | Pass/Fail formative report on pre-screening of data |
Assignments allow engineering problems to be set and solved using computer software. They also provide the opportunity for the key skills listed above to be assessed and implemented. The Formative assessment will run as a lead-in to the summative assessment and will be used to assess the students comprehension of the techniques discussed in the lectures whilst preparing the data for subsequent analysis.
N/A
Disclaimer: The information contained within the Module Catalogue relates to the 2023/24 academic year. In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described. Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, and student feedback. Module information for the 2024/25 entry will be published here in early-April 2024. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.