# Statistics 120 - Information Visualisation

## Introduction

This course provides an introduction to visualisation techniques
in statistics. Using an understanding of graphical perception,
we will examine graphical displays traditionally used in
statistics as well as some new ones. We will also look a some
computer intensive techniques, including animation, as a means
of gaining insight into data in higher dimensions. Topics will
consist of a selection taken from the following list.

- Why visualise?
- Computing for graphics
- Human vision and perception
- Displays for counts and proportions
- Displays for numeric data
- Time series displays
- Multivariate data
- Graphical examination of relationships
- Visualisation as an aid to theory
- Dynamic graphics

Examples will be drawn from across the physical and social
sciences.

## Prerequisites

The formal prerequisite for the course is a good pass in bursary
mathematics or one of the other first-year statistics courses. A
reasonable understanding of basic mathematics (geometry and
trigonometry) is expected and we will use a tiny amount of
calculus (differentiating
*x*^{2}), but will
explain this in class. The ability to write clearly is also
important.

## Timetable

The lectures for the class are at Monday, Wednesday and Friday
11-12 in Eng3403. The course has a computer laboratory
scheduled in the Statistics Department's computer tutorial
room (303.175) on Fridays from noon to 2pm.

## Assessment

The final grade for the course will be made up of 15% from
assignments, 20% from a mid-semester test, and 65% from the
final exam. A minimum of 45% must be obtained in the final
exam. The assignments will require a mix of written exposition
and graphical presentation. The final exam and test will consist
of a number of essay questions. The test for the course is
scheduled for Wednesday August 27, 11am - noon.

## Computing

An important focus of the course is on developing practical
computing skills and the class will involve a lot of
computing. This computing can be either be done in the
Statistics Department's computing laboratory or on your own PC
at home at home (Windows, Macintosh or Linux).

*
Ross Ihaka*

Room 275

Statistics Department