Analyze Survey Data using Principal Component Analysis

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Coursera Project Network
在此免費指導項目中,您將:

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.

在面試中展現此實踐經驗

Clock2 hours
Advanced高級設置
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

必備條件

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

您要培養的技能

  • Survey Methodology
  • Mining Insights
  • Business Insights
  • Data Analysis
  • Principal Component Analysis (PCA)

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  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.

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

授課教師

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