What is the difference between EDA and data cleaning?
An example of an AI chatbot conversation about EDA and data cleaning differences.
What is the difference between EDA and data cleaning?
Exploratory Data Analysis (EDA) focuses on analyzing data sets to summarize their main characteristics, often using visual methods. Data cleaning, on the other hand, involves correcting or removing inaccurate records from a dataset.
Can you give me examples of techniques used in EDA?
Sure! Common techniques in EDA include data visualization (like histograms and scatter plots), summary statistics, and correlation analysis.
How important is data cleaning before performing EDA?
Data cleaning is crucial before EDA because it ensures the accuracy and quality of the data, leading to more reliable insights.
What tools can I use for EDA and data cleaning?
Popular tools for EDA include Python libraries like Pandas and Matplotlib, while data cleaning can be done using tools like OpenRefine or even Excel.
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