Exploratory Data Analysis

Exploratory Data Analysis is a systematic approach within data analytics that employs descriptive statistics and visualization techniques to examine, summarize, and uncover underlying patterns, anomalies, or structural features in datasets prior to formal modeling, distinguishing it from confirmatory or predictive analytics by its hypothesis-generating, open-ended nature.

1.4K

Publications

223.4K

Citations

3.1K

Authors

1.1K

Institutions

Publications per year

2017–2026

445

Authors

3.1K

  1. JW
    John W. Tukey

    Princeton University, United States

    13 publications · 4.6K citations

    12

    h-index

  2. BS
    Ben Shneiderman

    University of Maryland, College Park, United States

    11 publications · 1K citations

    11

    h-index

  3. DC
    Dianne Cook

    Iowa State University, United States

    11 publications · 1.4K citations

    11

    h-index

  4. DC
    David C. Hoaglin

    Harvard University, United States

    12 publications · 3.2K citations

    11

    h-index

  5. RD
    R. Dennis Cook

    University of Minnesota, United States

    9 publications · 824 citations

    9

    h-index

1–5 of 3.1K

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 32 publications · 2.6K citations

    25

    h-index

  2. Technometrics

    31 publications · 3.1K citations

    20

    h-index

  3. Journal of Computational and Graphical Statistics

    JCGS · 22 publications · 18.3K citations

    18

    h-index

  4. Journal of the American Statistical Association

    20 publications · 1.5K citations

    16

    h-index

  5. The American Statistician

    25 publications · 3.5K citations

    16

    h-index

1–5