Unit rationale, description and aim
This unit introduces students to the essential data structures and algorithmic techniques that form the foundation of intelligent, efficient, and ethically responsible software development. Students gain hands-on experience with fundamental structures—including arrays, linked lists, stacks, queues, hash tables, trees, and graphs—and learn to apply key algorithms for sorting, searching, recursion, and traversal. The unit also develops students’ ability to evaluate algorithm efficiency using Big-O notation and make informed decisions about time-space trade-offs in real-world computing scenarios. In addition, students explore the MapReduce programming model as a scalable solution for big data processing, with applications in areas such as healthcare analytics, digital health platforms, and medical decision-making. Aligned with United Nations Sustainable Development Goal 3: Good Health and Well-being, the unit encourages the creation of resource-aware computing solutions that serve the common good. This unit provides a critical foundation for advanced study in artificial intelligence, cybersecurity, and data science.
Learning outcomes
To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.
Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.
Explore the graduate capabilities.
Understand fundamental data structures and algorit...
Learning Outcome 01
Evaluate algorithmic efficiency using Big-O notati...
Learning Outcome 02
Design programs that apply appropriate data struct...
Learning Outcome 03
Apply algorithmic thinking to develop scalable and...
Learning Outcome 04
Apply the MapReduce programming model to address l...
Learning Outcome 05
Content
Topics will include:
· Big-O notation and algorithm complexity
· Arrays and linked lists
· Stacks, queues, and deques
· Hashing and hash tables
· Recursion and recursive problem-solving
· Trees: binary trees and binary search trees
· Heaps and priority queues
· Graphs: representation and traversal algorithms
· Sorting and searching algorithms
· Algorithm design techniques (e.g., divide and conquer, greedy, dynamic programming)
· MapReduce and big data processing
· Introduction to complexity classes (P, NP, NP-complete – conceptual only)
Assessment strategy and rationale
Assessments in this unit are designed to progressively build students’ knowledge and practical skills in data structures and algorithms, while reinforcing ethical awareness and real-world application. The strategy blends individual and collaborative tasks to develop technical competencies, critical thinking, and teamwork in authentic computing contexts.
The Programming Portfolio allows students to demonstrate their ability to apply foundational algorithmic concepts and data structures through professionally documented code and problem-solving exercises. The Algorithm Analysis Report, undertaken in groups, fosters critical analysis of algorithmic efficiency, ethical implications, and collaborative learning, reflecting team-based decision-making in industry. The Solution Implementation Project challenges students to design, implement, and justify an efficient and ethically responsible solution to a real-world problem, supporting reflection on social impact and portfolio-quality outcomes.
Together, these assessments target a range of graduate capabilities and ensure students are well-prepared to contribute meaningfully to the computing and data science sectors.
Overview of assessments
Type – Programming Portfolio Purpose – Enable...
Type – Programming Portfolio
Purpose – Enables students to demonstrate their ability to design and implement efficient solutions using core data structures and algorithms. Students showcase professional-level code and documentation.
This is an individual assessment.
30%
Type – Algorithm Analysis Report Purpose – Team...
Type – Algorithm Analysis Report
Purpose – Teams critically analyse algorithmic solutions for efficiency, ethics, and contextual factors. Fosters collaboration, evaluation, and ethical awareness.
This is a group assessment.
30%
Type – Solution Implementation Project Purpose –...
Type – Solution Implementation Project
Purpose – Students create and justify a scalable, ethically responsible solution to a real-world challenge using MapReduce and other techniques. Reflects on societal impact and sustainability.
This is an individual assessment.
40%
Learning and teaching strategy and rationale
This unit adopts a student-centred, blended learning approach that combines asynchronous online content with interactive, face-to-face workshops. Foundational knowledge is delivered through structured online materials—such as videos, guided exercises, and self-paced quizzes—enabling students to engage flexibly with key concepts at their own pace. Face-to-face workshops build on this foundational learning by offering hands-on practice, peer collaboration, and problem-solving activities. These sessions are designed to help students apply theoretical knowledge to practical challenges related to data structures and algorithms. This blended approach promotes active learning, critical thinking, and collaborative engagement, supporting the development of technical expertise and professional competencies aligned with vocational outcomes.