Exploratory Data Analysis (EDA) focuses on analyzing datasets to summarize their main characteristics, identify patterns, and detect anomalies before applying machine learning models. It enables data professionals to understand data distribution, relationships, and underlying structure to make informed decisions. This training explains key EDA techniques such as descriptive statistics, data visualization, correlation analysis, outlier detection, and feature exploration. It also covers tools like Python, Pandas, Matplotlib, and Seaborn for performing effective data analysis. You will learn how organizations use EDA to improve data quality, guide model selection, and uncover actionable business insights. The course also highlights best practices for conducting structured and efficient data exploration workflows.
Showing the single result
