The document discusses principles and techniques for exploratory data analysis including: 1) Showing comparisons, causality, and systematic structure through data visualization principles. 2) Creating one dimensional and two dimensional plots like scatter plots to understand data properties and find patterns. 3) Using base plotting systems, lattice systems, and ggplot2 systems which offer different levels of customization for creating plots. 4) Addressing issues like scaling, cost, and clustering when analyzing exploratory data.

1. Exploratory Data Analysis
By : Vishwas Narayan

2. Principles of Analytic Graphics
Principle 1 : Show comparison
Principle 2 : Show causality,mechanism,explanation.systematic structure
Principle 3 : Show multivariate data
Principle 4 : Integration Of the evidence
Principle 5 : Describe and document the evidences with the appropriate
labels,scales,sources etc
Principle 6 : Content is king

3. Graphs
● To understand the data property
● To find patterns
● To suggest modeling strategy
● To “debug” analysis

4. Characteristics of the exploratory data
● They are made quickly
● A large number can be made
● The goal is for the personal understanding
● Axes/legends are generally cleaned up(later)
● Color and the size are the primary information

5. One Dimensional Summaries
mean,median,mode,max,min

6. Two Dimensional Plots
Multiple or the overlaid plots
Scatter plots
Smooth scatter plots

7. > 2 dimensional plots
Co-plots
Spinning plots
Use of the color,size

8. Base plotting system
test ,lines,points,axis
These will be very convenient
Difficult to translate
Plot is just a series of the command

9. Lattice system
The plots are created with the specific function
Spacing and the margins are automatic only parameters(a lot in number) is to be
given here.
Panel plot are the different levels of the third variable.

10. The ggplot2 system
Splits the difference between base and the lattice in the number of ways
Automatically deals with the spacing,text,titles but also allows you to annotate
adding to a plot
Superficial similarity to lattice but generally easier/more intuitive to use

11. Scaling
Vertical Scaling
Horizontal Scaling

12. Today cost is the key

16. Shap Framework

17. Jmeter tool

18. Train Test Scree Plots

21. What is Data Wrangling?
What you just did now is Data Wrangling.

22. Lattice plotting system

23. Lattice data system behavior

25. Hierarchical Clustering

26. K Mean clustering

27. Dimension Reduction