FIT3155 - Advanced data structures and algorithms

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Overview

This unit builds on the concepts learnt in introductory algorithms and data structures study. It covers advanced algorithmic paradigms and problem-solving techniques required to address real-world programming challenges. It explores, in depth, the design and analysis of space-efficient data structures and time-efficient problem solving strategies to be used with them. … For more content click the Read More button below.

This unit builds on the concepts learnt in introductory algorithms and data structures study. It covers advanced algorithmic paradigms and problem-solving techniques required to address real-world programming challenges. It explores, in depth, the design and analysis of space-efficient data structures and time-efficient problem solving strategies to be used with them. Topics include amortized analysis, advanced sorting and searching algorithms, new tree/string/graph data structures and algorithms, and number-theoretic algorithms amongst others.

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

Requisites

Prerequisite

Contacts

Chief Examiner(s)

Dr Arun Konagurthu

Associate Professor Anuja Dharmaratne

Unit Coordinator(s)

Associate Professor Anuja Dharmaratne

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 efficient data structures and effective algorithmic paradigms;

Design and implement efficient algorithms and data structures for use on large data sets;

Apply advanced algorithms and data structures to tackle complex computational problems;

Prove the correctness of programs and reason about their space and time complexities.

Teaching approach

Active learning

Assessment

1 - In-semester assessment

2 - Examination (2 hours and 10 minutes)

Scheduled and non-scheduled teaching activities

Laboratories

Lectures

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.

Availability in areas of study

Advanced computer science, Computational science