FIT5145
Foundations of data science
基本信息
| 学分 | 6 credit points |
|---|---|
| 开课学期 | First semester / Second semester |
| 校区 | Malaysia |
| 考核构成 | Data Analysis with R (Coding task I) — 15% Report on the Data Science Project — 30% Propose a Data Science Project — 15% Data Analysis with Tools and Scripting (Coding task II) — 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
This unit looks at processes, case studies and simple tools to understand the many facets of working with data, and the significant effort in Data Science over and above the core task of Data Analysis. Working with data as part of a business model and the lifecycle in an organisation is considered, as well as business processes and case studies. Data and its handling is also introduced: characteristic kinds of data and its collection, data storage and basic kinds of data preparation, data cleaning and data stream processing. Styles of data analysis and outcomes of successful data exploration and analysis are reviewed. Standards, tools and resources are also reviewed. Basic curation and management are reviewed: archival and architectural practice, policy, legal and ethical issues.
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 Analyse the role of data in organisations, including curation and management issues;
- ULO2 Apply basic tools for performing exploratory data analysis and visualisation;
- ULO3 Apply basic predictive modelling methods and integrate data analysis results to guide real-world decision making;
- ULO4 Apply basic tools for managing and processing big data;
- ULO5 Identify data resources, data storage, processing requirements, and recognised standards for a data science project;
- ULO6 Recognise and analyse ethical and security challenges inherent to data science.
教学方式与预期工作量
教学方式
Active learning
预期工作量
Applied sessions are scheduled from week 2 to week 12.
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：FIT9136 OR FIT9131 OR FIT9133
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3 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：ETC5510 AND ITI5145 AND ITO5145
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。
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。