Overview

This unit takes a mathematical approach to equip students with the skills they need to perform complex analysis of datasets, be they scientific, industrial, financial or personal. This unit introduces students to the key mathematical methods used in formulating and solving such problems.

Teaching periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date
Semester 2
Location
Hawthorn
Start and end dates
29-July-2024
27-October-2024
Last self-enrolment date
11-August-2024
Census date
31-August-2024
Last withdraw without fail date
13-September-2024
Results released date
03-December-2024

Learning outcomes

Students who successfully complete this unit will be able to:

  • Examine how different types of analysis can be applied to sets of data to help quantify decision making in business, government, science and engineering.
  • Solve problems through quantifying and understanding different classes of data.
  • Use stochastic calculus and probability distributions to model data, analyse systems and predict outcomes, while evaluating the performance of these predictive models.
  • Manipulate datasets and effect computation using software packages such as Matlab and R.

Teaching methods

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
On-campus
Lecture
3.00 12 weeks 36
On-campus
Class
1.00 12 weeks 12
On-campus
Class
1.00 6 weeks 6
Online
Learning activities
2.00 12 weeks 24
Unspecified Activities
Independent Learning
6.00 12 weeks 72
TOTAL150

Assessment

Type Task Weighting ULO's
Assignment 1Individual/Group 10 - 20% 1,2,3,4 
Assignment 2Individual/Group 10 - 20% 1,4 
ExaminationIndividual 40 - 50% 1,2,3,4 
Online QuizzesIndividual 5 - 10% 1,2,3,4 
Test 1Individual 10 - 20% 1,2 
Test 2Individual 10 - 20% 1,3 

Hurdle

As the minimum requirements of assessment to pass a unit and meet all ULOs to a minimum standard, an undergraduate student must have achieved:

(i) an aggregate mark of 50% or more, and(ii) at least 40% in the final exam.Students who do not successfully achieve hurdle requirement (ii) will receive a maximum of 45% as the total mark for the unit.

Content

  • Probability theory (random variabes, distributions and the central limit theorem)
  • Analysis of time series and stochastic processes
  • Modeling with autoregressive models
  • Manipulation of datasets in statistical programming languages such as R and Matlab.

Study resources

Reading materials

A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.