What is the difference between EDA and data analysis?
An example of an AI chatbot conversation about EDA vs data analysis.
What is the difference between EDA and data analysis?
Exploratory Data Analysis (EDA) focuses on summarizing the main characteristics of data, often using visual methods. Data analysis, on the other hand, encompasses a broader range of techniques to draw conclusions and make decisions based on data.
Can you give me some examples of EDA techniques?
Sure! Common EDA techniques include histograms, box plots, scatter plots, and correlation matrices. These help visualize data distributions and relationships.
When should I use EDA instead of traditional data analysis?
Use EDA when you're exploring a new dataset to understand its structure and patterns. Traditional data analysis is better for hypothesis testing and making predictions.
Are there any tools specifically for EDA?
Yes, popular tools for EDA include Python libraries like Pandas and Matplotlib, as well as software like Tableau and R.
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