Exploratory Data Analysis Quiz
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Questions and Answers

What is the main focus of Data Analysis?

  • Entire methodology
  • Prediction of future events
  • The science behind the analysis
  • Hands-on data exploration and evaluation (correct)
  • Which type of analysis involves visualizing data using Histogram and Pie-charts?

  • Diagnostic Analytics
  • Predictive Analytics
  • Descriptive Analysis (correct)
  • Exploratory Analysis
  • What is the primary concern of Data Analytics?

  • Broader term including data analysis (correct)
  • Prediction of future events
  • Science behind the analysis
  • Entire methodology
  • Which type of analytics focuses on understanding 'why something happened'?

    <p>Diagnostic Analytics</p> Signup and view all the answers

    What does Descriptive Analysis primarily focus on?

    <p>Analyzing and summarizing data</p> Signup and view all the answers

    Study Notes

    Exploratory Data Analysis

    • Learning outcomes of exploratory data analysis include conducting correlation analysis, exploring data using Principal Component Analysis (PCA), and conducting data summary analysis.

    Analysis and Analytics

    • Analysis and analytics can be categorized into different types, including:
      • Descriptive Analysis
      • Diagnostic Analytics
      • Exploratory Analysis
      • Predictive Analytics
      • Mechanistic Analysis
      • Prescriptive Analytics

    Correlation Analysis

    • Correlation analysis involves calculating Pearson's r correlation coefficient to measure the linear relationship between two continuous variables.
    • Example of correlation analysis: calculating the correlation coefficient between two variables.

    Principal Component Analysis (PCA)

    • PCA is a dimensionality reduction technique that transforms a set of correlated variables into a set of uncorrelated variables called principal components.
    • 2-dimensional PCA: a simplified example of PCA that reduces data to two dimensions.
    • Intuition behind PCA: finding the direction of maximum variance in the data.
    • Variance Maximization: the goal of PCA is to maximize the variance of the principal components.
    • Obtaining Principal Components (PC): involves computing the eigenvectors and eigenvalues of the covariance matrix.
    • Case Study-1: an example application of PCA.

    Data Summary Analysis

    • Data summary analysis involves summarizing and describing datasets using statistical measures.
    • Case Study-2: an example application of data summary analysis.

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    Description

    Test your knowledge of exploratory data analysis, correlation analysis, and principal component analysis with this quiz. Explore different types of data analysis and analytics techniques.

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