PSY4210
Statistics and data science for psychology
基本信息
| 学分 | 6 credit points |
|---|---|
| 开课学期 | First semester |
| 校区 | Malaysia |
| 考核构成 | e-Exam (100 minutes) — 25% Software homework exercises (Approximately 3 hours per week) — 20% Lab reports (Report A x 350 to 400 words 20% and Report B x 750 words 35% - Maximum 1,150 words) — 55% |
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
In this unit you will be provided with an applied understanding of statistics and data science methods commonly used in Psychological Sciences.
The unit has an applied focus and emphasises learning both the theory and interpretation of statistics as well as the practical application of statistics and data management through programming and software (data science). You will engage in weekly workshops that scaffold your skills and gain feedback from other students. Afterwards, you will complete independent assessments to showcase your: development of technical data management and analysis skills; interpretation of data; writing and criticism; and understanding and application of statistical theory as it relates to complex real-world psychological problems.
The content is contextualised through applied problems and examples from Psychological Science, including a diverse range of psychosocial and health-related variables, to help you understand how the tools can be used to support professional psychologists and research.
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 Critique psychological science research data presentation and analysis steps in accordance with the scientific method.
- ULO2 Ascertain commonly accepted advanced appropriate analysis techniques to answer questions in psychological science.
- ULO3 Apply modern software tools from current best practices in data science to manage and explore diverse psychological data and variables.
- ULO4 Interpret results from advanced statistical analyses.
- ULO5 Collaborate effectively and provide peer feedback to learn best data science practices.
教学方式与预期工作量
教学方式
Active learning - For full details of the teaching approach to this unit, please refer to Moodle.
预期工作量
2 hour workshop per week covering theory and software applications. The expected undirected/private study to be completed by students will be approximately 9 hours a week. This will incorporate students completing regular exercises to gain familiarity with software tools, readings, and preparation and completion of the assessments.
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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