Analyze Survey Data using Principal Component Analysis

4.0
estrellas
35 calificaciones
ofrecido por
Coursera Project Network
1507 ya inscrito
En este Proyecto guiado gratuito, tú:

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.

Demuestra esta experiencia práctica en una entrevista

Clock2 hours
AdvancedAvanzado
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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.

Requerimientos

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

Habilidades que desarrollarás

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

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  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.

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

Instructor

Reseñas

Principales reseñas sobre ANALYZE SURVEY DATA USING PRINCIPAL COMPONENT ANALYSIS

Ver todas las reseñas

Preguntas Frecuentes

Preguntas Frecuentes

¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.