Analyze Survey Data using Principal Component Analysis

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在此免费指导项目中,您将:

Understand the fundamentals of Principal Component Analysis (PCA) and identify opportunities to combine variables.

Conduct correlation testing with various sets of variables in Google Sheets.

Combine highly correlated variables, visualize the data, and consider next steps in Google Sheets.

在面试中展现此实践经验

2 hours
高级设置
无需下载
分屏视频
英语(English)
仅限桌面

Survey data sets are often deceptively complex because surveys collect a wide variety of data covering a wide variety of topics and experiences. To further the complexity of survey data, the respondents answering the questions come from a wide variety of backgrounds and stages in their customer journey. It is reasonable that it would be a challenge to boil down survey data into actionable insights because it can be deceptively complex. With large sets of data, Principal Component Analysis or PCA is a useful tool that reduces and transforms variables to a leaner form that allows for a speedier analysis. In this project you will gain hands-on experience with the principles of Principal Component Analysis using survey data. To do this you will work in the free-to-use spreadsheet software Google Sheets. By the end of this project, you will be able to confidently apply Principal Component Analysis concepts to transform large sets of variables into a leaner set of data that still contains the most relevant information. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

必备条件

Familiarity with spreadsheet software, factor analysis, and correlation testing. "Design a Factor Analysis Using Survey Data" is recommended.

您要培养的技能

  • Survey Methodology

  • Mining Insights

  • Business Insights

  • Data Analysis

  • Principal Component Analysis (PCA)

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Review the fundamentals of Principal Component Analysis (PCA) and combining variables.

  2. Identify use cases for PCA and refine variable selection for the project.

  3. Access Google Sheets, import survey data, and examine variables that are likely correlated.

  4. Identify variables of interest and conduct a correlation test.

  5. Compare results and review the process of correlation testing.

  6. Combine highly correlated variables, create a visualization, and consider next steps.

  7. Access the ClustVis webtool for visualizing clustering and multivariate data.

  8. Build a PCA model with Heart data and run a Principal Component Analysis

  9. Compare results and review PCA with multivariate data from multiple sources and interpret the findings in ClustVis.

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

授课教师

审阅

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常见问题

由于您的工作空间包含适合笔记本电脑或台式计算机使用的云桌面,因此指导项目不在移动设备上提供。

指导项目授课教师是特定领域的专家,他们在项目的技能、工具或领域方面经验丰富,并且热衷于分享自己的知识以影响全球数百万的学生。

您可以从指导项目中下载并保留您创建的任何文件。为此,您可以在访问云桌面时使用‘文件浏览器’功能。

您可在页面顶部点按此指导项目的经验级别,查看任何知识先决条件。对于指导项目的每个级别,您的授课教师会逐步为您提供指导。

是,您可以在浏览器的云桌面中获得完成指导项目所需的一切。

您可以直接在浏览器中于分屏环境下完成任务,以此从做中学。在屏幕的左侧,您将在工作空间中完成任务。在屏幕的右侧,您将看到有授课教师逐步指导您完成项目。

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