Another must-know term for any data analyst, the outlier (whether multivariate or univariate), refers to a distant value that deviates from a sample’s pattern.

In data analytics, an outlier refers to a data point or observation that significantly differs from other observations in a dataset. Outliers can occur due to various reasons such as measurement or recording errors, natural variation, or rare events. Identifying outliers is essential in data analysis as they can skew statistical analyses and machine learning models, leading to inaccurate results or predictions.

To handle outliers, data analysts typically employ techniques such as:

Visual inspection: Plotting the data using graphs like box plots, histograms, or scatter plots can help visually identify outliers.
Statistical methods: Calculating summary statistics such as mean, median, standard deviation, and quartiles can help detect outliers based on their deviation from the central tendency of the data.
Machine learning algorithms: Some algorithms are robust to outliers, while others are sensitive. Understanding the behavior of algorithms regarding outliers can help in selecting appropriate models or preprocessing techniques.
Data preprocessing: Techniques such as winsorization (replacing extreme values with less extreme ones), trimming (removing extreme values), or transformation (e.g., logarithmic transformation) can mitigate the influence of outliers on statistical analyses.
Domain knowledge: Understanding the context of the data and the underlying process generating it can help distinguish between genuine outliers and valid data points.

In an interview setting, it’s essential to demonstrate an understanding of what outliers are, why they are important, and how they can be managed or treated in data analysis workflows. Additionally, providing examples or discussing specific techniques for outlier detection and handling can further showcase your expertise in data analytics.