Road to Data Science as a fresher

Exploratory Data Analysis (EDA) is an essential step in the data analysis process that helps to uncover important patterns, trends, and relationships in the data. Understanding the different data types is crucial for effective EDA. In this blog post, we will discuss the different data types used in EDA, including nominal, ordinal, discrete, and continuous data, as well as the difference between quantitative and qualitative data. Nominal Data: Nominal data is categorical data that has no inherent order or hierarchy. Nominal data is often used to represent characteristics or attributes that are unique to a particular category. Examples of nominal data include: Gender (Male or Female) Marital Status (Single, Married, Divorced, or Widowed) Hair Color (Blonde, Brown, Black, or Red) Nominal data can be analyzed using frequency tables or bar charts to identify the frequency of occurrence of each category. In EDA, nominal data is considered a qualitative data type. Ordinal Data: Ordinal dat...

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