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Overview

This unit introduces students to problem solving concepts and techniques fundamental to the science of programming. In doing this it covers problem specification, algorithmic design, analysis and implementation. Detailed topics include analysis of best, average and worst-case time and space complexity; introduction to numerical algorithms; recursion; advanced data structures such … For more content click the Read More button below.

Offerings

S1-01-CLAYTON-ON-CAMPUS

S1-01-MALAYSIA-ON-CAMPUS

S2-01-CLAYTON-ON-CAMPUS

S2-01-MALAYSIA-ON-CAMPUS

Rules

Enrolment Rule

Contacts

Chief Examiner(s)

Dr Rafael Dowsley

Unit Coordinator(s)

Dr Lim Wern Han

Rebecca Robinson

Mr Nathan Companez

Notes

Studios are scheduled from week 2 to week 12.

Learning outcomes

On successful completion of this unit, you should be able to:
1.

Analyse general problem solving strategies and algorithmic paradigms, and apply them to solving new problems;

2.

Prove correctness of programs, analyse their space and time complexities;

3.

Compare and contrast various abstract data types and use them appropriately;

4.

Develop and implement algorithms to solve computational problems.

Teaching approach

Active learning

Assessment summary

This unit has threshold mark hurdles. You must achieve at least 45% of the available marks in the final scheduled assessment, at least 45% in total for in-semester assessments, and an overall unit mark of 50% or more to be able to pass the unit. If you do not achieve the threshold mark, you will receive a fail grade (NH) and a maximum mark of 45 for the unit.

Assessment

1 - Assignments

2 - Studio Preparation

3 - Scheduled final assessment (2 hours and 10 minutes)

4 - Assignment 1

5 - Assignment 2

6 - Assignment 3

7 - Scheduled final assessment (2 hours and 10 minutes)

Scheduled and non-scheduled teaching activities

Lectures

Studio activities

Workload requirements

Workload

Learning resources

Required resources

Recommended resources

Technology resources

Availability in areas of study

Computer science
Computational science