
Module Availability 
Autumn Semester 


Assessment Pattern 
Assessment Pattern
Unit(s) of Assessment

Weighting Towards Module Mark (%)

Assignment

40

Test

60

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.




Prerequisites/Corequisites 
Prerequisite/Corequisites
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 (7^{th} ed.), Prentice Hall (2004).
 A. F. Siegel, Statistics and Data Analysis: an introduction (2^{nd} ed.), Wiley (1996).
 R. Peck, C. Olsen, and J. Devore, Introduction to Statistics and Data Analysis (3^{rd} ed.), Thomson (2008).





Last Updated 
12 AUG 10 JG 
