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# Data Analysis and Numerical Methods - ELE00053I

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• Department: Electronic Engineering
• Module co-ordinator: Dr. Stuart Porter
• Credit value: 20 credits
• Credit level: I
• Academic year of delivery: 2024-25
• See module specification for other years: 2023-24

## Module summary

This module introduces the key statistical and numerical methods necessary for processing, interpreting, and communicating data relevant to engineering systems. The module includes an introduction to programming and scripting in MATLAB.

• None

## Module will run

Occurrence Teaching period
A Semester 2 2024-25

## Module aims

Data is central to engineering and the physical sciences, and the ability to gather, process, and interpret data is an essential skill for tackling engineering problems. This module develops students’ understanding and confidence in some of the key statistical and numerical methods necessary to use data effectively.

Subject content aims:

• Develop an appreciation for data analysis and numerical methods as an engineering discipline.

• Explore the key statistical and numerical methods relevant to engineering systems.

• Encourage critical thinking about the interpretation and communication of data.

Graduate skills and qualities:

• Gain familiarity and confidence with MATLAB as an industry standard computational tool.

• Apply analytical and technical methods to statistical and computational engineering problems.

• Demonstrate effective communication of technical aspects of data analysis.

## Module learning outcomes

On successful completion of this module, students will be able to:

• Explain the importance of data in the context of engineering and the physical sciences.

• Interpret and visualise fundamental aspects of data using statistical methods.

• Communicate ideas clearly and effectively using quantitative language and statistical concepts.

• Implement basic algorithms and numerical methods in MATLAB.

• Use MATLAB to process and visualise data relevant to engineering systems

## Module content

Illustrative content (subject to revision following further consultation):

• Probability: Axioms and definitions (adding probabilities, mutually exclusive events), permutations and combinations, binomial theorem; discrete random variables; continuous random variables.

• Introduction to Statistics: types of data, qualitative and quantitative data, discrete and continuous data; averages (mean, mode, median, and rms); variance and standard deviation; quartiles and skew; covariance and correlation; the idea of uncertainty, and combination of errors.

• Statistical Distributions: law of large numbers; expectation values and mean, probability density distributions; uniform distribution; binomial distribution; Poisson distribution; Gaussian distribution.

• Numerical Methods and Optimisation: Root finding; function minimization; sorting algorithms; binary search tree; least-squares regression; travelling salesman, Monte Carlo sampling, stochastic processes.

• Introduction to MATLAB: Inputs and outputs; abstract data types; control statements; processing large data sets; visualising data; implementation of standard algorithms in sorting and root finding.

## Assessment

Task Length % of module mark
Closed/in-person Exam (Centrally scheduled)
Closed exam : Data Analysis and Numerical Methods Exam
2 hours 50
Essay/coursework
Coursework 3
N/A 5
Essay/coursework
Coursework 4
N/A 5
Essay/coursework
Coursework 1
N/A 5
Essay/coursework
Coursework 10
N/A 5
Essay/coursework
Coursework 2
N/A 5
Essay/coursework
Coursework 5
N/A 5
Essay/coursework
Coursework 6
N/A 5
Essay/coursework
Coursework 7
N/A 5
Essay/coursework
Coursework 8
N/A 5
Essay/coursework
Coursework 9
N/A 5

None

### Reassessment

Task Length % of module mark
Closed/in-person Exam (Centrally scheduled)
Closed exam : Data Analysis and Numerical Methods Reassessment Exam
2 hours 100

## Module feedback

'Feedback’ at a university level can be understood as any part of the learning process which is designed to guide your progress through your degree programme. We aim to help you reflect on your own learning and help you feel more clear about your progress through clarifying what is expected of you in both formative and summative assessments. A comprehensive guide to feedback and to forms of feedback is available in the Guide to Assessment Standards, Marking and Feedback.

The School of PET aims to provide some form of feedback on all formative and summative assessments that are carried out during the degree programme. In general, feedback on any written work/assignments undertaken will be sufficient so as to indicate the nature of the changes needed in order to improve the work. The School will endeavour to return all exam feedback within the timescale set out in the University's Policy on Assessment Feedback Turnaround Time. The School would normally expect to adhere to the times given, however, it is possible that exceptional circumstances may delay feedback. The School will endeavour to keep such delays to a minimum. Please note that any marks released are subject to ratification by the Board of Examiners and Senate. Meetings at the start/end of each term provide you with an opportunity to discuss and reflect with your supervisor on your overall performance to date.

## Indicative reading

TBC

The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.