Unit of Study MATH3003 Computational Analysis (2025)

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Unit Snapshot

Unit type

UG Coursework Unit
Credit points

12
Faculty & College

Faculty of Science and Engineering
Placement

No

Pre-requisites

MATH2003 - Calculus and Linear Algebra I

Unit description

This unit introduces students to essential data management skills for statistical analyses and computational methods for problem-solving. Students will learn how to effectively manage data for basic statistical analyses and apply computational techniques using computer applications. They will gain proficiency in implementing numerical algorithms to solve problems such as linear and nonlinear equations, numerical differentiation, and integration. Additionally, students will develop the ability to formulate and solve mathematical problems, critically evaluating the results obtained.

Unit content

Module 1: Mathematical programming

Module 2: Further programming techniques

Module 3: Numerical integration and differentiation

Module 4: Introduction to probability

Module 5: Tests of the mean

Module 6: Regression

Availabilities

Location	Domestic	International
Gold Coast	Term4	Term4
Lismore	Term4	Term4
Online	Term4	N/A

Learning outcomes

Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.

On completion of this unit, students should be able to:

formulate and solve mathematical programming and statistical problems and critically evaluate the results

implement numerical algorithms for computing solutions of various problems incorporating numerical differentiation or integration

apply and evaluate basic statistical analyses

Identify and apply the appropriate method and accurately communicate the results using mathematical and statistical techniques

evaluate a real-world problem by formulating a mathematical model demonstrating the model feasibility and its' simulation

evaluate a real-world problem by utilising techniques including data-visualisation, measures of location and spread, hypothesis tests of the mean, analysis of variance and regression.

Teaching and assessment

Teaching method

Workshop 1 hour (Weekly)

Tutorial 2 hours (Weekly)

Assessment
Report	50%
Report	50%

Teaching method

Workshop 1 hour (Weekly)

Tutorial 2 hours (Weekly)

Assessment
Report	50%
Report	50%

Teaching method

Workshop 1 hour (Weekly)

Tutorial 2 hours (Weekly)

Assessment
Report	50%
Report	50%

Prescribed learning resources

Prescribed Texts

Prescribed text information is not currently available.

Prescribed Resources/Equipment

Prescribed resources/equipment information is not currently available.

Prescribed Learning Resources may change in future Teaching Periods.

Fee information

Domestic

Commonwealth Supported courses
For information regarding Student Contribution Amounts please visit the Student Contribution Amounts.

Fee paying courses
For postgraduate or undergraduate full-fee paying courses please check Domestic Postgraduate Fees OR Domestic Undergraduate Fees.

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