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2010/1 Module Catalogue
Module Provider: Computing Short Name: COM2023
Level: HE2 Module Co-ordinator: DERKS GL Dr (Maths)
Number of credits: 10 Number of ECTS credits: 5
Module Availability
Autumn Semester
Assessment Pattern

Assessment Pattern


Unit(s) of Assessment


Weighting Towards Module Mark (%)










Qualifying Condition(s) 


A weighted aggregate mark of 40% is required to pass this module.


Module Overview

Module Overview


This module is an introduction to Data Analysis and Statistics.





COM1021 Mathematical Methods for Computing I


Module Aims

Module Aims


The module aims to give an introduction to the fundamental ideas of data analysis through investigations of data followed by an introduction to probability and distribution theory. The module also aims to introduce the students to performing statistical and data analysis with computer software.


Learning Outcomes

Learning Outcomes


At the end of the module the students should be able to summarise and clearly present data, undertake a simple analysis of real data sets and should be familiar with basic results in probability and distribution theory. They should be able to use the computer software effectively to analyse basic problems in data analysis.



Module Content

Module Content


  • Introduction to data and data types. Data quality, data collection, sampling and bias.


  • Summary statistics, graphical and numerical. Scatterplots, correlation.


  • Introduction to probability.


  • Distributions: Discrete: Binomial, Poisson and uniform;
    Continuous: Normal distributions. Expectation and variance.


  • Linear regression.


  • Introduction to time series.


  • Introduction to the statistical software (within Matlab).



Methods of Teaching/Learning

Methods of Teaching/Learning


Teaching is by lectures, tutorials and computing labs. Learning to take place through lectures, tutorials, practical computer lab sessions, exercises and background reading.



There will be 2 lectures and 1 tutorial for 11 weeks. The tutorial is computer lab based.


Selected Texts/Journals

Selected Texts/Journals


Lecture notes will be provided.



Background reading:



Electronic resources:



Other books:


  • I. Miller, M. Miller, John E. Freund’s Mathematical Statistics with Applications (7th ed.), Prentice Hall (2004).


  • A. F. Siegel, Statistics and Data Analysis: an introduction (2nd ed.), Wiley (1996).


  • R. Peck, C. Olsen, and J. Devore, Introduction to Statistics and Data Analysis (3rd ed.), Thomson (2008).





Last Updated
12 AUG 10 JG