[Data Visualization] Exploratory data analysis

Posted on by Brad Lee One min read

Exploratory data analysis(EDA)

In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods.

EDA

The objectives of EDA are to:

  • Suggest hypotheses about the causes of observed phenomena

  • Assess assumptions on which statistical inference will be based

  • Support the selection of appropriate statistical tools and techniques

  • Provide a basis for further data collection through surveys or experiments

Typical graphical techniques used in EDA are:

refer to https://en.wikipedia.org/wiki/Exploratory_data_analysis

  • Box plot

  • Histogram

  • Multi-vari chart

  • Run chart

  • Pareto chart

  • Scatter plot

  • Stem-and-leaf plot

  • Parallel coordinates

  • Odds ratio

  • Targeted projection pursuit

  • Glyph-based visualization methods such as PhenoPlot and Chernoff faces

  • Dimensionality reduction:

    • Multidimensional scaling

    • Principal component analysis (PCA)

    • Multilinear PCA

    • Nonlinear dimensionality reduction (NLDR)

  • Projection methods such as grand tour, guided tour and manual tour

  • Interactive versions of these plots