FIT1008
Fundamentals of algorithms
基本信息
| 学分 | 6 credit points |
|---|---|
| 开课学期 | First semester / Second semester |
| 校区 | Malaysia |
| 考核构成 | Coding Project 1 — 30% Theory Test 1 — 0% Learning Portfolio — 100% Theory test — 0% Weekly Quiz — 30% Theory Test 2 — 0% Coding Project 2 — 40% |
开课安排2 条
| 教学期 | 授课方式 | 状态 |
|---|---|---|
| Second semester | Teaching activities are on-campus (ON-CAMPUS) | 开课 |
| First semester | Teaching activities are on-campus (ON-CAMPUS) | 开课 |
以上为该校区在官方资料中登记的全部开课安排，不是汇总。同一门课可能在多个 教学期开课，也可能不同教学期的授课方式不同。
课程简介
From Semester 2, 2026: 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 recursion, introductory complexity analysis, and structures such as stacks, queues, 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.
Semester 1, 2026: This unit introduces you to core problem-solving, analytical skills, and methodologies useful for developing flexible, robust, and maintainable software. In doing this, it covers a range of conceptual levels, from fundamental algorithms and data structures, down to their efficient implementation as well as complexity. Topics include data types, data structures, algorithms, algorithmic complexity, recursion, and their practical applications.
以上为 Monash Handbook 的官方原文，版权属 Monash University，此处按本站要求转载并标注出处： 官方页面 ↗
学习成果6 条
官方原文（Learning outcomes），版权属 Monash University。
- ULO1 Semester 2: Analyse computational problems to identify suitable algorithmic strategies, data representations and performance considerations. Semester 1: Translate problem statements into algorithms and implement them in a high level programming language;
- ULO2 Semester 2: Demonstrate understanding of data structures and algorithms by implementing, using, and testing them in ways that support correctness, readability and maintainability. Semester 1: Determine appropriate basic abstract data types, including; stacks, queues, lists, binary trees, priority queues, heaps and hash tables; for specific contexts;
- ULO3 Semester 2: Design modular algorithmic solutions using appropriate abstract data types, including lists, stacks, queues, trees, heaps and hash tables. Semester 1: Theoretically and experimentally evaluate different implementations of basic abstract data types;
- ULO4 Semester 2: Demonstrate awareness and working knowledge of relevant tools and technologies, and use them effectively to increase productivity and improve quality, such as IDEs, AI, and Version Control Systems. Semester 1: Analyse the efficiency of algorithms by determining their best-case and worst-case big-O time complexity;
- ULO5 Semester 2: Plan, monitor, and reflect on the development of your algorithmic thinking and implementation practice through focused problem solving, feedback and iterative improvement.
- ULO6 Semester 2: Communicate algorithmic reasoning and correctness, implementation choices, performance trade-offs and testing evidence using appropriate technical terminology and representations.
教学方式与预期工作量
教学方式
Peer assisted learning
预期工作量
This unit has a requirement of 1-2 hours per week of asynchronous learning.
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.
官方原文，版权属 Monash University。
先修 / 同修要求
以下先修关系按官方来源的结构化先修字段解析，原始记号：(FIT1045 OR FIT1053) AND (FIT1058 OR MAT1830)
修完这门课可以衔接
先修链路
按官方先修字段的原始分组展开，AND / OR 的区别保留着—— 「A 或 B」和「A 与 B」在选课时是两回事。每门课点进去可以继续往下看。
互斥课程2 门
这些课和本课内容重叠，不能同时算进同一个学位（官方目录的 Prohibition 字段）。选了其中一门，另一门通常只能算选修学分甚至完全不计—— 这跟先修不同，先修是"没修过就不能选"，互斥是"修了也不能两门都算"。
原始记号：FIT1054 AND FIT2085
属于这些学位3 个
这门课出现在下列学位的官方结构里。反过来说：如果你读的是这些学位之一，它大概率是要修的 （必修还是选修取决于它在 Part 里的位置，点进去看结构）。
数据来源
- 数据来源
- 官方网页
handbook.monash.edu ↗ - 抓取时间
- 2026-09-13
- 可信度
- 程序抓取，未人工核实
查看官方完整描述 ↗ — 事实性字段（代码、学分、教学期、授课方式、考核权重、先修/同修/互斥关系）与 课程简介、学习成果、教学方式、预期工作量均取自官方 Handbook； 正文版权属 Monash University，此处转载并逐处标注出处。
发现信息有误？告诉我们。请用自己的话描述问题，不要上传成绩单、截图或校内系统文件。