Da Outliers — Free Data Analytics Tutorial
Learn Da Outliers in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Da Outliers in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
Written & reviewed by the Syllab.in Academic Team (CBSE/NCERT subject experts) · Updated
Da Outliers in Data Analytics
An outlier is a data point that is significantly different from other observations. Outliers can be errors (a student's marks recorded as 850 instead of 85), or genuinely unusual (a prodigy who scores 100 when the class average is 60).
Why outliers matter: They can distort the mean heavily. They can indicate errors in data collection. They can reveal important insights (a sudden spike in sales could be a viral event). In fraud detection, the outlier IS the fraud.
Methods to detect outliers: Z-score method (values more than 2-3 standard deviations from mean are outliers), IQR method (values below Q1-1.5*IQR or above Q3+1.5*IQR are outliers — this is what box plots show), Visual methods (scatter plots, box plots).
What to do with outliers: Investigate (is it an error or genuine?), Remove if error, Keep if genuine, Transform (use log scale to reduce impact), Cap/floor (replace extreme values with a threshold).
Da Outliers — Syntax
# Z-score: how many standard deviations from mean # z = (x - mean) / std_dev # |z| > 2 → outlier (2 SD from mean) # |z| > 3 → extreme outlier (3 SD from mean) # IQR method: # Q1 = 25th percentile, Q3 = 75th percentile # IQR = Q3 - Q1 # Outlier if x < Q1 - 1.5*IQR or x > Q3 + 1.5*IQR
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