Skewed Data
Skewed data refers to a statistical distribution where data points are not symmetrically distributed around the mean, often exhibiting a longer tail on one side. This asymmetry can significantly impact the performance and assumptions of machine learning models and statistical analyses, as many algorithms assume normally distributed data. Understanding and handling skewed data is crucial for accurate modeling, feature engineering, and data preprocessing in data science and analytics.
Developers should learn about skewed data when working with real-world datasets, as it is common in fields like finance (e.g., income distributions), healthcare (e.g., disease incidence), and web analytics (e.g., user engagement metrics). Addressing skewness through techniques like log transformation or robust scaling can improve model accuracy, prevent bias, and ensure compliance with statistical assumptions in tools like linear regression or clustering algorithms.