Exploratory Data Analysis
Exploratory Data Analysis (EDA) is a statistical approach and methodology used to analyze and summarize datasets, primarily to understand their main characteristics, patterns, and anomalies before formal modeling. It involves techniques like data visualization, summary statistics, and data cleaning to uncover insights, detect outliers, and test assumptions. EDA is a critical first step in data science and analytics workflows, helping to inform subsequent modeling decisions and hypothesis generation.
Developers should learn and use EDA when working with data-driven projects, such as in data science, machine learning, or business analytics, to gain initial insights and ensure data quality before building models. It is essential for identifying data issues, understanding distributions, and exploring relationships between variables, which can prevent errors and improve model performance. Use cases include analyzing customer behavior data, preparing datasets for predictive modeling, or conducting preliminary research in scientific studies.