ADS2001
Data challenges 3
基本信息
| 学分 | 6 credit points |
|---|---|
| 开课学期 | First semester |
| 校区 | Malaysia |
| 考核构成 | Assignment — 80% Reflective journal — 20% |
开课安排1 条
| 教学期 | 授课方式 | 状态 |
|---|---|---|
| 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
This is the third in a series of Data Challenges units which builds on industry-relevant data science case studies explored during the first two semesters of the course. Team-based learning is used to further develop and apply the suite of analytical skills required to discover the underlying answers to questions raised by large and complex sets of data. A broad range of problems will be presented from both STEM and humanities disciplines, including both academic and industry focused examples, and guest lectures will give you an insight into real problems faced by experts in the field. You will continue to discuss the technical and ethical elements of data, with a focus on data collection methods, scientific thinking, and issues surrounding privacy and to communicate project outcomes through a combination of written, oral and multimedia visualisations. You will also work in teams to apply the key principles, tools and techniques of data science to complex industry problems, and develop their capacity to communicate their analysis and ethical considerations to a range of potential stakeholders.
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 Apply scientific thinking to data-oriented projects and tasks;
- ULO2 Demonstrate the ability to work in a team to achieve a goal;
- ULO3 Analyse the ethical issues associated with data science decisions;
- ULO4 Demonstrate a variety of approaches used to communicate complex ideas to potential stakeholders through a variety of techniques including role-play;
- ULO5 Perform a range tasks to analyse and visualise data;
- ULO6 Design approaches for collecting and analysing data for a range of industry problems.
教学方式与预期工作量
教学方式
Active learning - Studio-based learning and active learning more generally has comprehensively been demonstrated to deliver superior learning outcomes in comparison to traditional didactic delivery methods.
预期工作量
• Three hours of online learning to be completed pre-studio;
• One three-hour studio and
• Approximately six hours of project work and reflective practice per week
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：ADS1002
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。
属于这些学位1 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。
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