ETX2250 - Data visualisation and analytics

Overview

Business analytics can unlock the hidden insights in data to give businesses a competitive advantage. Many businesses have masses of data about customers and operations, and need skilled analysts to uncover insights and make informed predictions. This unit uses data visualisation to explore and analyse data sets of all sizes, … For more content click the Read More button below. It will introduce an appropriate software environment for data visualisation, and analytics, and cover visualisation and analysis techniques for categorical and numerical variables. Visualisation methods to be covered include some of Box-and-whisker plots, Mosaics, Rotatable 3D scatter plots, Heat maps, Motion charts, cluster and association charts. Models to be covered may include linear regression models, classification and regression trees, and random forests. Methods for evaluating model performance will also be discussed. Examples from marketing, finance, economics and related disciplines will be included.

This unit uses data visualisation to explore and analyse data sets of all sizes, and it introduces some business analytic models for interpretation and prediction.

It will introduce an appropriate software environment for data visualisation, and analytics, and cover visualisation and analysis techniques for categorical and numerical variables. Visualisation methods to be covered include some of Box-and-whisker plots, Mosaics, Rotatable 3D scatter plots, Heat maps, Motion charts, cluster and association charts. Models to be covered may include linear regression models, classification and regression trees, and random forests. Methods for evaluating model performance will also be discussed. Examples from marketing, finance, economics and related disciplines will be included.

Offerings

S2-01-CAULFIELD-ON-CAMPUS

SSA-02-CAULFIELD-FLEXIBLE

Requisites

Prerequisite

STA1010

6 CP

Statistical methods for science

Business and economic statistics

Introduction to statistical analysis

FIT1006

6 CP

Business information analysis

SCI1020

6 CP

Introduction to statistical reasoning

Prohibition

Rules

Enrolment Rule

Contacts

Chief Examiner(s)

Shanika Wickramasuriya

Kate Saunders

Learning outcomes

On successful completion of this unit, you should be able to:

select, create and interpret appropriate types of visual representation for a given set of data

select and develop model types with explanatory and/or predictive ability

make appropriate use of in-sample and out-of-sample evaluation of models

apply the above research skills to produce innovative solutions in finance, marketing, economics and related areas

use visualisation and modelling to effectively communicate the results of their investigations

explain the sequence of procedures that should be applied to analyse a given dataset.

Teaching approach

Problem-based learning

Active learning

Assessment

1 - Within semester assessment

2 - Examination

Scheduled and non-scheduled teaching activities

Seminars

Tutorials

Workshops

Workload requirements

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.

Learning resources

Technology resources

Other unit costs

Costs are indicative and subject to change.
Electronics, calculators and tools: $100