What is the importance of EDA in data science?
Exploratory Data Analysis (EDA) is crucial in data science as it helps in understanding the data's structure, patterns, and anomalies.
Can you explain some common techniques used in EDA?
Sure! Common techniques include summary statistics, data visualization, and correlation analysis to uncover insights.
How does EDA influence the modeling process?
EDA informs the modeling process by identifying relevant features, potential biases, and guiding the choice of algorithms.
What tools are commonly used for EDA?
Popular tools for EDA include Python libraries like Pandas, Matplotlib, and Seaborn, as well as R's ggplot2.
Is EDA a one-time process or should it be repeated?
EDA should be an ongoing process, especially as new data comes in or when the data context changes.
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