CME8124 : Big Data Analytics in the Process Industries
- Offered for Year: 2024/25
- Module Leader(s): Dr Chris O'Malley
- Lecturer: Dr Jie Zhang
- Owning School: Engineering
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
Aims
This module aims to provide a practical introduction 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.
Outline Of Syllabus
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.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 30:00 | 30:00 | Problem Solving Exercise and subsequent writing up in report format. Summative assessment on Process Modelling |
| 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 | Lectures |
| Scheduled Learning And Teaching Activities | Workshops | 6 | 2:00 | 12:00 | Computing Labs |
| Guided Independent Study | Independent study | 1 | 30:00 | 30:00 | Review lecture material and prepare for workshops |
| Total | 100:00 |
Teaching Rationale And Relationship
Lectures convey the statistical concepts and theory and their application in process engineering. Hands on practical 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 practical workshop sessions allow the completion of some of the assignment work.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Computer assessment | 1 | M | 100 | Assessed report - Process Data Modelling (set Week 6) -2000 words |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Computer assessment | 1 | M | Pass/Fail formative report on pre-screening of data |
Assessment Rationale And Relationship
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 their data for subsequent analysis.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CME8124's Timetable