BIN3890
Research methods in bioinformatics and big data analysis
基本信息
| 学分 | 6 credit points |
|---|---|
| 开课学期 | Second semester |
| 校区 | Malaysia |
| 考核构成 | Poster presentation — 25% Oral presentations — 10% Supervisors assessment — 25% Assignment report — 40% |
开课安排1 条
| 教学期 | 授课方式 | 状态 |
|---|---|---|
| Second 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
In Research methods in bioinformatics and big data you will apply the knowledge and analytical skills learnt in BIN3800 to carry out an in depth computational analyses of existing genomics, transcriptomics or proteomics datasets. This will be an entirely dry lab unit and computer resources at the Genomics facility and its associated bioinformatics and big data laboratory and the Monash Malaysia Advanced Computing Platform will be made available to you in your data analyses. This unit will provide you an opportunity to apply your bioinformatics knowledge to access and analyse large datasets and develop project management skills and confidence in analysis of big data and subsequently improve chances of your employability by academia, or industry.
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5 条
官方原文（Learning outcomes），版权属 Monash University。
- ULO1 Plan and undertake bioinformatics data analyses;
- ULO2 Access and analyze large genomics, transcriptomics and proteomics datasets using command line queries;
- ULO3 Explain how to check quality and interpret results obtained from the analyses of large genomics datasets;
- ULO4 Demonstrate project management skills;
- ULO5 Prepare and present a poster presentations.
教学方式与预期工作量
预期工作量
• 1-hour applied session and
• 11-hours of analyses activity (self directed learning) 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。
同修要求（必须在同一学期一起修）
同修课和先修课不是一回事：先修是修过才能选，同修是必须同期一起选。原始记号：GEN2052
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。
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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