Intro to EDA

  |   Source
Intro to EDA



  • EDA: Exploratory Data Analysis
  • ubiquitous: can explain everything from data
  • Data and Analysis is directly correlated. We have to someway analyze our data
  • By doing analysis we can make predictive model
  • Have insight and take good question about our data.
  • Defend agains bad data, wrangling, and cleaning the data.
  • Make a better visualization to present to larger part of audience.
  • Data Analyzis can benefit as follows:
    • Win competition
    • Has data reasoning
    • Improve communication
    • and extend our carreer.



  • investigate what's interesting
  • dig what's more interesting
  • The goal is have an impact so the world have an understand better.
  • how to represent mindset.
  • Aude once use conditional probability from given hometown, what is the probabilty of they're moving to another city.
  • Then she found out that most likely, we still live in our hometown
  • But in certain cases, things are different. So she investigate it with different approach.

  • contiditionalprob iven hometown what people in particular city.
  • city most likely still your homewtown.
  • certain cases, have different high prob. she curios and take different approach
  • She is fascinating to see pattern particular cities, in some countries, not necesssary expecting that.
  • She saw that the distribution of people living in same city is very flat.
  • When looking at the data, we may want to choose at the world scale, to give better intuition.
  • She back then only choose US data, but take different approach by observe world scale.

  • develop intuition about how data exists
  • By doing that we can select which features is the most significant, and what statistical tool to build our predictive model.


  • In conducting EDA, always be skeptical as we will develop new instuition.
  • Be open mind in detecting data, and have summary of the data.