EDA. Exploratory Data Analysis

Exploratory Data Analysis (EDA) is an approach to data analysis that involves examining, summarizing, and visualizing data sets to understand their main characteristics and patterns. EDA is an initial phase in the data analysis process in which analysts seek to gain insight and understand the nature of the data before applying more advanced techniques or modeling.

Key aspects of the EDA include:

• Data Summary: Applying descriptive statistics concepts such as mean, median, standard deviation and variance to summarize numerical data. For categorical data, frequency counts and percentages help to understand the distribution.
• Data VisualizationGraphical representations such as histograms, box plots, scatter plots and bar charts are used to visually explore relationships, distributions and trends within the data. Visualizations facilitate the identification of outliers, patterns and possible correlations.
• Missing Data ManagementIdentifying and addressing missing values within the data set is a crucial part of EDA. Strategies may involve imputation (replacing missing values with estimated values) or deciding to exclude incomplete data.
• Identifying Patterns and RelationshipsEDA helps to understand correlations between variables, detect anomalies or outliers, and recognize trends within the data.
• Dimensionality ReductionEDA may involve reducing the number of variables using techniques such as principal component analysis (PCA) or feature selection to focus on the most significant variables.

EDA is an iterative process that guides data scientists and analysts to understand the nature of the data, identify potential problems or peculiarities, and make informed decisions about additional analysis or modeling techniques to apply.