FIT2004 is not in the 2027 Handbook - Monash may have renumbered or withdrawn it. This is what the 2026 Handbook published; check the 2027 Handbook or your faculty before planning next year.
Faculty notice: this unit is changing
No longer offered
BCS, BSE
Replace with FIT3234 (S1 and S2)
From: Re-enrolment and unit changes - Information Technology (undergraduate) · Last checked: 7 Oct 2026 UTC
Official wording from the faculty, shown as published. It is the page of the Faculty of IT on monash.edu, so confirm with your faculty that it applies to your campus and intake year. Replacements are decided by the faculty, not by this site.
Units / FIT2004
FIT2004 · Algorithms and data structures
2026 Handbook6 credit pointsLevel 2Faculty of Information Technology
Overview
This unit introduces you 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.
Areas of study: Computer science Computational science
Offerings
| Campus | Teaching period | Mode |
|---|---|---|
| Malaysia | First semester | Teaching activities are on-campus (ON-CAMPUS) |
| Malaysia | Second semester | Teaching activities are on-campus (ON-CAMPUS) |
| 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) |
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 Project Portfolio | Portfolio | 100% | — |
| 2 | In class tests | Quiz / Test | 0% | Competency |
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Requisites
Learning outcomes
- 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.
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. Applied sessions are scheduled from Week 2 to Week 12.
| Activity | Duration |
|---|---|
| Workshops | 24 hours |
| Applied sessions | 33 hours |
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