The described activities—creating a correlation matrix, calculating summary statistics, and visualizing data—are the core components of Exploratory Data Analysis (EDA). The primary goal of EDA is to understand the main characteristics of a dataset, uncover patterns, identify anomalies, and check assumptions before any modeling is performed. This stage is crucial for gaining initial insights that will guide subsequent steps like feature engineering and data pre-processing.

1. AWS Whitepaper

"The AWS Machine Learning Lens": This document outlines the machine learning lifecycle. In the section "Phase 1: Business goal and problem formulation

" it describes the data exploration and analysis step: "You perform exploratory data analysis (EDA) to understand the data and extract insights. This includes data profiling

visualization

and statistical analysis." (AWS Machine Learning Lens

Feb 2023

Page 10).

2. University Courseware

Stanford University CS229: The course materials for Machine Learning (CS229) consistently position EDA as a foundational step. Lecture notes describe this phase as the process of "looking at your data" using visualizations and summary statistics to understand its properties before attempting to train a model. (Ng

A. (2023). CS229 Machine Learning Course Materials

Stanford University

Lecture Notes 1

Section on "The Machine Learning Workflow").

3. Academic Publication

"CRISP-DM 1.0: Step-by-step data mining guide": The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a widely accepted methodology. Its second phase

"Data Understanding

" directly corresponds to EDA. This phase includes tasks such as "Compute basic statistics" and "Visualize data" to gain familiarity with the data and identify quality problems. (Chapman

P.

et al. (2000). CRISP-DM 1.0: Step-by-step data mining guide

The CRISP-DM Consortium

Section 3.2 "Data Understanding Phase").