Module Catalogue 2024/25

CME8124 : Big Data Analytics in the Process Industries

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

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

Modules you must have done previously to study this module

Pre Requisite Comment

Basic knowledge of statistics from A-level mathematics or equivalent


Modules you need to take at the same time

Co Requisite Comment



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.

Learning Outcomes

Intended Knowledge Outcomes

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 (M1).

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 judge appropriate techniques to use to reach substantiated conclusions which could lead to the delivery of enhanced process performance, process understanding and/or process optimisation (M2).

To develop the critical ability of the students enabling them to select and apply the most appropriate methodologies for the problem to be addressed (data pre-screening, feature extraction and process modelling) (M3).

Intended Skill Outcomes

The ability to utilise the statistical techniques that form the basis of multivariate methods (M12) and to select appropriate methods for a given problem recognising their limitations (M13).

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 evaluate the effectiveness of the methods used to model the problem (M17).

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture181:0018:00Lectures
Guided Independent StudyAssessment preparation and completion130:0030:00Problem Solving Exercise and subsequent writing up in report format. Summative assessment on Process Modelling
Guided Independent StudyAssessment preparation and completion110:0010:00Problem Solving Exercise Formative Assessment on Pre-treatment of data
Scheduled Learning And Teaching ActivitiesWorkshops62:0012:00Computing Labs
Guided Independent StudyIndependent study130:0030:00Review lecture material and prepare for workshops
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.

Reading Lists

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Computer assessment1M100Assessed 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 assessment1MPass/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.


Past Exam Papers

General Notes


Welcome to Newcastle University Module Catalogue

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You may have some queries about the modules available to you. Your school office will be able to signpost you to someone who will support you with any queries.


The information contained within the Module Catalogue relates to the 2024 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 2025/26 entry will be published here in early-April 2025. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.