Units / FIT3234
FIT3234 · Data structures and algorithms 2
2027 Handbook6 credit pointsLevel 3Faculty of Information Technology
Overview
This unit deepens study of algorithms and data structures by moving from introductory programming patterns to more formal design, analysis, and proof-oriented reasoning. It covers advanced data structures, graph and numerical algorithms, recursion, and systematic analysis of best-case, average-case, and worst-case time and space complexity. It also introduces selected advanced topics such as amortised reasoning and more sophisticated problem-solving strategies. You will learn to compare algorithmic paradigms, justify correctness, and implement efficient solutions to non-trivial computational problems, building the intellectual bridge from foundational coding to essential computer science knowledge.
Offerings
| Campus | Teaching period | Mode |
|---|---|---|
| Clayton | First semester | Some activities have a choice of on-campus or online teaching activities (FLEXIBLE) |
| Clayton | Second semester | Some activities have a choice of on-campus or online teaching activities (FLEXIBLE) |
| Malaysia | Second semester | Teaching activities are on-campus (ON-CAMPUS) |
| Malaysia | First semester | Teaching activities are on-campus (ON-CAMPUS) |
Assessment
The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.
| # | Assessment | Type | Weight | Hurdle |
|---|---|---|---|---|
| 1 | Learning Portfolio | Portfolio | 100% | — |
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Requisites
prerequisite
prohibition
Learning outcomes
- Communicate algorithmic reasoning, complexity arguments and design choices using mathematical notation, code and structured prose appropriate to a technical audience;
- Investigate complex computational problems by comparing algorithmic paradigms, evaluating alternative approaches and justifying chosen solutions with both theory and experiment;
- Document the design of non-trivial algorithms and data structures through specifications, invariants and complexity arguments that support implementation and review;
- Construct, verify and reason about formal models of algorithmic behaviour, including best-, average- and worst-case analyses and amortised reasoning.
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 teaching activities.
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