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Exploratory Data Analysis – Johns Hopkins University – Coursera

0-6 Month Online
Beginner 31-Oct-2016
Online Business Analytics
Online Self Paced 15181


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This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.


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After successfully completing this course you will be able to

  • Make visual representations of data using the base, lattice, and ggplot2 plotting systems in R,
  • Apply basic principles of data graphics to create rich analytic graphics from different types of datasets,
  • Construct exploratory summaries of data in support of a specific question, and
  • Create visualizations of multidimensional data using exploratory multivariate statistical techniques.

Duration:- 4 weeks

3-5 hours Per week

Part time/ Full time: – Part time

Fees: – INR 3259 Per Month

Important Date:

Course Start Date: 31-Oct-2016


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  • Roger D. Peng:- Johns Hopkins University
  • Jeff Leek:- Johns Hopkins University
  • Brian Caffo:- Johns Hopkins University


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  • Jagan says:

    Do we need to do EDA before we split the data into Training and Test data or is it preferable to do on Training data set? Please clarify.

    • Pulkit Sharma says:

      Hi Jagan,

      You should do the exploration before splitting the data into training and test sets as it will give you the entire insight of your data. If you explore only the train data splitting, you might loose some of the insights of your data.