FIT2004 - Algorithms and data structures

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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.

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 as heaps and B-trees; hashing; sorting algorithms; searching algorithms; graph algorithms; and numerical computing.

Offerings

S1-01-CLAYTON-ON-CAMPUS

S1-01-MALAYSIA-ON-CAMPUS

S1-FF-CLAYTON-FLEXIBLE

S2-01-CLAYTON-ON-CAMPUS

S2-01-MALAYSIA-ON-CAMPUS

Rules

Enrolment Rule

Contacts

Chief Examiner(s)

Mr Nathan Companez

Unit Coordinator(s)

Dr Lim Wern Han

Notes

IMPORTANT NOTICE:
Scheduled teaching activities and/or workload information are subject to change in response to COVID-19, please check your Unit timetable and Unit Moodle site for more details.

Learning outcomes

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

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

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

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

Develop and implement algorithms to solve computational problems.

Teaching approach

Active learning

Assessment

1 - In-semester assessment

2 - Examination (2 hours and 10 minutes)

Scheduled and non-scheduled teaching activities

Lectures

Studio activities

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 online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.

Learning resources

Required resources

Technology resources

Availability in areas of study

Computer science
Computational science