| Type: | Undergraduate Subject |
|---|---|
| Code: | ENAI11-102 |
| EFTSL: | 0.125 |
| Faculty: | Bond Business School |
| Credit: | 10 |
| Study areas: |
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| Subject fees: |
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Description
This subject introduces students to the core principles of algorithms and data structures, equipping them with the skills to design efficient solutions to computational problems. The subject covers fundamental data structures such as arrays, stacks, queues, trees, and graphs. It also covers classic algorithms for sorting and searching, and their time and space complexity are discussed. Key algorithmic paradigms, including divide and conquer, greedy methods, and dynamic programming, are introduced. AI tools are integrated in the learning, supporting the development process and code generation, debugging, and optimisation of code. Through hands-on programming and reflective practice, students will gain technical proficiency and an understanding of how AI can enhance coding practice.
Subject details
Learning outcomes
- Apply fundamental data structures such as arrays, stacks, queues, trees, and graphs to solve common computational problems.
- Analyse the time and space complexity of algorithms using Big-O notation to evaluate their efficiency.
- Implement common sorting and searching algorithms using an appropriate programming language.
- Apply algorithmic design paradigms, such as divide-and-conquer, greedy algorithms, and dynamic programming, to develop programming solutions.
- Evaluate AI tools for their use in the design, implementation, and refinement of algorithms applied to common business tasks.
- Communicate algorithmic solutions to technical and non-technical audiences using diagrams, pseudo-code and computational analysis to show how proposed solutions address specified business problems.
Enrolment requirements
| Requisites: |
Nil |
|---|---|
| Restrictions: |
Nil |