MODELLING AND DATA ANALYSIS

SHE Level 3
SCQF Credit Points 20.00
ECTS Credit Points 10.00
Module Code M3H623538
Module Leader Sinan Sinanovic
School School of Computing, Engineering and Built Environment
Subject Electronic Engineering
Trimester
  • B (January start)

Pre-Requisite Knowledge

Mathematics 1A, 1B, 2A, 2B

Summary of Content

An aim of the module is to allow the students to develop the analytical skills required to analyse and interpret data in an Engineering context. The module will use probabilistic, statistical, Fourier and modelling methods to give the students the ability to meaningfully abstract information and make inferences from their data. In conjunction with the theoretical aspects of data analysis this module will also provide the students with the appropriate implementation strategies to perform their data analysis using modern data analysis software tools. Both the theoretical and implementation components of the module will examine real world case studies, analysing, discussing and contextualising these examples.

Syllabus

The teaching syllabus will cover the following areas: Review of Linear Algebra: Matrices and vectors, multiplication and addition, transposition, inverse, eigenvalues and eigenvectors. Probability and Statistics: Axioms of probability, Sum and Product Rule, Independence, Joint Probability, Conditional Probability, Bayes Rule, Statistical Distributions (Gaussian, Poisson, Bernoulli), z-score Data Analysis: Removing and interpolating missing values, removing outliers, filtering (smoothing and shaping data) - moving average, median filter, de-trending data, robust statistics (trimmed mean, Winsor mean), data trending and visualisation Data Modelling: Correlation analysis, correlation coefficient, covariance, cross correlation and autocorrelation linear regression linear/polynomial (Least squares/Maximum Likelihood) Fourier Analysis: Sampling, Time domain to frequency domain using FFT, magnitude and phase, zero padding, harmonic analysis, power spectrum, spectrum analysis, power spectral density Implementation: Matlab programming, file input/output, string manipulation, random number generation, statistics, data analysis, modelling and Fourier implementations

Learning Outcomes

On completion of this module the student should be able to:Construct and simulate probabilistic models for various random phenomena Successfully select and apply appropriate data smoothing methods to remove noise from dataApply correlation and regression methods to explore relationships between time series dataUnderstand and utilise frequency domain concepts to extract information from data Implement the above methods using a suitable software package such as Matlab.

Teaching / Learning Strategy

The University 'Strategy for Learning' documentation has informed the learning and teaching strategy for this module. This course focusses predominately on improving the analytic data analysis skills of the students; therefore an appropriate mix of theoretical and practical implementation is required. The theoretical material will be introduced through lectures, with the implementation being introduced via practical exercises involving real world contextualised problem solving. Tutorials will be used to explain and elaborate on the lecture material and laboratory exercises. To ensure the module is industrially relevant for each programme, real world case studies will be utilised within the laboratories to reinforce the theoretical concepts and expose the students to open ended engineering problems. Full use will be made of GCU Learn to provide Lecture-based and related study materials, along with sample solutions of Tutorial and Laboratory exercises, thus encouraging the development of independent learning and allowing self-reflective feedback on student performance. Staff-based feedback on student performance for submitted work will be provided in line with the University feedback policy, with summative feedback and grades on the coursework assessment utilising GCU Learn. The additional interactive discussion features of GCU Learn will be utilised, as appropriate to the module, to stimulate independent and flexible student learning outwith scheduled class time.

Indicative Reading

Both the learned and the popular literature on data analysis, probability and statistics will be used as a source of information for private study. The titles and electronic resources below are to be considered as indicative only. -360 1. Probability and Statistics; John J. Schiller, R. Alu Srinivasan, Murray R Spiegel, 4 th Edition 2013 2. Essential Matlab for Engineers and Scientists; Brian Hahn, 5 th Edition 2012 3. Data analysis with Matlab; James Braselton, 2014

Transferrable Skills

Specialist knowledge and application. Critical thinking and problem solving. Critical analysis. Communication skills, written, oral and listening. Numeracy. Effective Information retrieval and research skills. Computer literacy. Self confidence, self discipline & self reliance (independent working). Awareness of strengths and weaknesses. Creativity, innovation & independent thinking. Presentation skills. Commercial awareness

Module Structure

Activity Total Hours
Tutorials (FT) 12.00
Independent Learning (PT) 132.00
Independent Learning (FT) 120.00
Assessment (FT) 20.00
Practicals (FT) 24.00
Assessment (PT) 20.00
Tutorials (PT) 12.00
Practicals (PT) 12.00
Lectures (FT) 24.00
Lectures (PT) 24.00

Assessment Methods

Component Duration Weighting Threshold Description
Coursework 1 n/a 25.00 35% Practical
Coursework 2 n/a 25.00 35% Practical
Exam (Exams Office) 2.00 50.00 35% Exam linked to Learning Outcomes