Data Discovery: An Overview of Exploratory Data Analysis (EDA)

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Exploratory Data Analysis (EDA) is a critical phase in the data analysis process that involves exploring and summarizing the main characteristics of a dataset. It helps analysts and data scientists understand the underlying patterns, relationships, and trends within the data. Here’s an overview of the key aspects of EDA:

I. Objectives of Exploratory Data Analysis:

1. Understand the Data:

i. Data Overview:

• Obtain basic information about the dataset: size, dimensions, and data types.
• Check the first few rows of the dataset to get a glimpse of the actual data.

ii. Variable Identification:

• Identify the types of variables in the dataset: numerical, categorical, or datetime.
• Note any potential target variables for analysis or modeling.

iii. Descriptive Statistics:

• Calculate summary statistics for numerical variables, including mean, median, minimum, maximum, and standard deviation.
• Analyze the distribution of numerical data to understand its central tendency and spread.