ADS3001
Advanced data challenges
基本信息
| 学分 | 12 credit points |
|---|---|
| 开课学期 | First semester / Second semester |
| 校区 | Malaysia |
| 考核构成 | Supervisor report — 10% Assignments — 70% Reflective journal — 20% |
开课安排2 条
| 教学期 | 授课方式 | 状态 |
|---|---|---|
| Second semester | Teaching mostly conducted outside of a classroom/campus environment (IMMERSIVE) | 开课 |
| First semester | Teaching mostly conducted outside of a classroom/campus environment (IMMERSIVE) | 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 final in a series of Data Challenges units which draws together your mathematical, computational and applied studies, and builds on the industry-relevant data science case studies explored during the first two years of the Bachelor of Applied Data Science. You will apply this knowledge working in industry and academic placements.
You will further develop and apply your analytic and technical skills to interrogate and understand large and complex real-world data sets drawn from academic, governmental and business problems. You will continue to develop your communication skills through a combination of written, oral and multimedia presentations, which communicate your analysis and conclusions to a range of potential stakeholders. Finally, you will work in teams to enhance your project management, collaborative and leadership skills.
The placements will embed you in data science teams in a range of government, industry and academic settings. These placements will be complemented by weekly seminars to provide insight into real problems faced by experts in the field.
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7 条
官方原文（Learning outcomes），版权属 Monash University。
- ULO1 Critically analyse data-oriented projects to break these down into achievable tasks;
- ULO2 Demonstrate the ability to work in a team to plan and complete a complex data-orientated project;
- ULO3 Analyse the ethical issues associated with data science decisions that arise;
- ULO4 Clearly communicate complex ideas to potential stakeholders using a variety of approaches;
- ULO5 Effectively manipulate, analyse and visualise data;
- ULO6 Implement a range of advanced machine learning algorithms;
- ULO7 Undertake independent research on data science techniques and relevant domain knowledge.
教学方式与预期工作量
教学方式
Active learning - As this is the capstone unit for the Bachelor of Applied Data Science, the primary objective is to apply the learning from throughout the course and therefore the most appropriate learning method is active learning. This is achieved through the two forms of activity, placements and weekly seminars.
You will be embedded in organisational teams and undertake active learning. You will apply your accumulated knowledge to work in teams, plan and complete projects, interrogate large and complex datasets, implement advanced machine learning algorithms on the data, research new data science techniques to apply to your project, deal with any ethical issues associated with the data science decisions and communicate their results to a range of stakeholders through written and oral presentations (LO1-LO7). Furthermore, they will be exposed to a range of techniques and issues specific to their placement (e.g., LO2, LO3, LO4, LO7).
预期工作量
• Two days per week (approx. 17 hours) of placements;
• One hour of attendance at seminars and
• Six hours of independent project work and reflective practice per week.
官方原文，版权属 Monash University。
先修 / 同修要求
官方资料未列出该课程的先修要求。
先修链路
官方资料未列出该课程的先修要求，因此没有链路可画。
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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