ES2F6-15 Engineering Mathematics and Data Analytics
Introductory description
Engineering Mathematics and Data Analytics
Module aims
To build on the fundamental material introduced in Engineering Mathematics in Year 1 thereby ensuring that students are equipped with the necessary analytical and computational tools to tackle advanced material in modules taught in later years. To present and provide skills in the application of more advanced mathematics and systems modelling concepts. To develop skills in the use of MATLAB for modelling and analysis of engineering systems. To introduce computer programming concepts and develop programming skills within MATLAB.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Applied linear algebra: linear matrix/vector equations and their solution (applications such as linear regression analysis, electrical circuits and fluid networks); eigenvalue/eigenvector analysis (applications such as oscillation in circuits, structural dynamics, solution of state variable models and stability analysis);
Data manipulation in MATLAB
Data analysis techniques: Regression, classification, PCA and design of experiments.
MATLAB as a system modelling and analysis tool.
Learning outcomes
By the end of the module, students should be able to:
- Recognise and apply advanced mathematical tools and techniques to solve engineering based problems.
- Develop complex mathematical models of engineering systems.
- Solve complex engineering problems using MATLAB.
- Apply data analytics techniques to datasets produced by engineering processes and systems
Indicative reading list
Croft, A. and Davison, R., “Mathematics for Engineers: and MyMathLab: A Modern Interactive Approach”, 3rd Ed., Pearson, ISBN-10: 1408263238, 2010.
James, G., “Modern Engineering Mathematics : 4th edition with MyMathLab”, Pearson, ISBN-10: 027373413X, 2010.
Magrab, E.B. et al., “An Engineer's Guide to MATLAB: International Edition”, 3rd Ed. Pearson, ISBN-10: 0137039549, 2010.
Subject specific skills
Follow a methodical approach to engineering problem solving.
Transferable skills
Prioritise quality. Follow rules, procedures and principles in ensuring work completed is fit for purpose, and pay attention to detail / error checks throughout activities.
Study time
| Type | Required |
|---|---|
| Lectures | 20 sessions of 1 hour (13%) |
| Supervised practical classes | 4 sessions of 2 hours (5%) |
| Work-based learning | 50 sessions of 1 hour (33%) |
| Other activity | 2 hours (1%) |
| Private study | 70 hours (47%) |
| Total | 150 hours |
Private study description
70 hours guided independent learning (including VLE use) including working through maths examples, preparation of data analytics coursework.
Other activity description
2x1h of on-line test
Costs
No further costs have been identified for this module.
You must pass all assessment components to pass the module.
Assessment group C1
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Data Analysis Assessment | 50% | Yes (extension) | |
|
10 page assignment |
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| Online Examination | 50% | No | |
|
QMP online examination ~Platforms - AEP,QMP
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Feedback on assessment
Advice and feedback are available on the lecture material and examination questions, via online web-forum based in module support Moodle pages.
Courses
This module is Core for:
- Year 2 of DESA-H360 Undergraduate Electromechanical Engineering (Degree Apprenticeship)