When I work with data, one of the first things I do is check for outliers, values that don’t fit with the rest of the data. I usually start by using visual tools like box plots, scatter plots, or histograms to quickly spot anything unusual. Then I use statistical methods like the Z-score or IQR (Interquartile Range) to detect values that are too far from the normal range.
Once I identify the outliers, I look into the reason behind them. Sometimes they are real and meaningful, like a sudden spike in sales, and in that case, I keep them. But if they are errors caused by data entry issues or faulty sensors, I remove or correct them.
In some situations, instead of removing outliers, I might cap them (winsorization) or transform the data (like using log transformation) to reduce their impact. The main goal is to make sure the data represents reality as accurately as possible without losing valuable information.
Data scientists like me often use these same techniques to maintain data quality and reliability. By carefully handling outliers, data scientists ensure that their models make accurate and trustworthy predictions.
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