u/Square_Arm2861

Hi everyone,

I am a beginner in Machine Learning, currently working on the Exploratory Data Analysis (EDA) step, and I have a few questions about structuring my workflow properly.

1. Handling many variables (> 20): What is the best practice when you have 20+ features? Should univariate graphical analysis still be done for every single variable before applying dimensionality reduction techniques like PCA, or can it be skipped? How do you practically handle EDA at scale?
2. Skewness & Kurtosis: When should I calculate Skewness and Kurtosis relative to plotting graphs? Are they strictly required for every numeric variable?
3. Order of execution and connections: I'm getting confused about how all these statistical concepts connect. What is the logical sequence between:
   • Univariate plots (Histograms, Boxplots)
   • Skewness & Kurtosis
   • Normality tests (e.g., Shapiro-Wilk test)
   • Hypothesis tests (t-test, ANOVA)

Any guidance, recommended pipeline, or resources to help clarify these steps would be greatly appreciated! Thanks in advance!

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