FIT2115 · Data structures and algorithms 1

Overview

Data structures and algorithms are the tools that allow programs to solve problems efficiently, reliably and at scale. This unit develops the core algorithmic thinking and implementation skills needed to move from a problem statement to a well-structured computational solution. You will learn to represent problems using appropriate data structures, design algorithms that use those structures effectively, and reason about how choices affect correctness, performance and maintainability. The unit covers abstract data types, recursion, introductory complexity analysis, and structures such as stacks, queues, lists, trees, heaps and hash tables. You will evaluate algorithm behaviour both theoretically and experimentally, building a practical understanding of time, space and trade-offs. Through structured programming activities, you will strengthen your ability to design, implement, test and explain algorithmic solutions. The unit builds disciplined habits of precise reasoning, careful coding, performance awareness and reflection, preparing you for later study in advanced algorithms, software design, artificial intelligence, systems and computational problem solving.

Offerings

Assessment

The Handbook publishes no assessment items for this unit yet.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Analyse computational problems to identify suitable algorithmic strategies, data representations and performance considerations;
2. Demonstrate understanding of data structures and algorithms by implementing, using and testing them in ways that support correctness, readability and maintainability;
3. Design modular algorithmic solutions using appropriate abstract data types, including lists, stacks, queues, trees, heaps and hash tables;
4. Explain how data structure and algorithm choices affect program behaviour, resource use, scalability and computational performance, using scaffolded AI-supported activities to strengthen understanding while validating outputs through independent reasoning;
5. Plan, monitor and reflect on the development of your algorithmic thinking and implementation practice through focused problem solving, feedback and iterative improvement;
6. Communicate algorithmic reasoning, implementation choices, performance trade-offs and testing evidence using appropriate technical terminology and representations.

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.

Student experience, not official rules. Nothing here changes what the Handbook says.