What are some practical applications of EDA in data science?
Exploratory Data Analysis (EDA) is crucial for understanding data patterns. It helps in identifying trends, anomalies, and relationships.
Can you give me an example of EDA in a real project?
Sure! In a retail project, EDA can reveal customer purchasing patterns, helping to optimize inventory and marketing strategies.
What tools are commonly used for EDA?
Common tools for EDA include Python libraries like Pandas, Matplotlib, and Seaborn, as well as R packages like ggplot2.
How does EDA differ from data visualization?
EDA is a broader process that includes data visualization as a technique. It focuses on summarizing the main characteristics of the data.
What are the key steps in performing EDA?
Key steps include data collection, data cleaning, data visualization, and statistical analysis to uncover insights.
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