ADS1001
Data challenges 1
基本信息
| 学分 | 6 credit points |
|---|---|
| 开课学期 | First semester |
| 校区 | Malaysia |
| 考核构成 | Reflective journal — 20% Continuous assessment — 80% |
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 first in a series of data challenges units which collectively develop a broad range of knowledge and transferable skills through studio-based learning, applied problem-solving and the exploration of a broad diversity of cross-disciplinary and industry-relevant data science case studies over the course. In recent years the world has seen an explosion in the quantity and variety of data routinely recorded and analysed by research and industry. The data may come from a variety of sources, including scientific experiments and measurements, legal documents, archives, human interactions such as browsing data or social networks on the Internet, mobile phone usage or financial transactions. Data science provides the analytical and visualisation techniques required by practitioners to obtain insights into their data. This inquiry-based boot camp unit will include an introduction to important elements of data science, how it is impacting on society, and the role it will play in addressing problems and issues across the sciences, business arena and industry. You will be exposed to the characteristics of data science over and above the core task of data analysis. Through interdisciplinary team-based workshops you will begin to collaboratively explore examples of complex problems which have been solved through the fusion of data science, mathematics and statistics, social, business, IT and interdisciplinary knowledge. You will apply the key principles, tools and techniques of data science to authentic problems and implement approaches and solutions, communicating outputs effectively for a range of 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 Identify the principles of scientific thinking and apply them in the context of data science;
- ULO2 Reflect upon how to create and deliver results in interdisciplinary teams;
- ULO3 Critique the ethical and multicultural dimensions associated with data science decisions, use and quality and their possible impacts on organisations and society;
- ULO4 Communicate outcomes effectively in a range of formats including orally, visually and in written form;
- ULO5 Identify the various steps to perform data analysis and visualisation;
- ULO6 Explore the importance of data in a variety of fields including science, IT and business.
教学方式与预期工作量
教学方式
Online learning
预期工作量
• Three hours of online learning to be completed pre-workshop;
• One three-hour workshop and
• Approximately six hours of project work and reflective practice.
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。
修完这门课可以衔接
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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