Data Smoothing
Data smoothing is a statistical technique used to reduce noise and short-term fluctuations in datasets, revealing underlying trends, patterns, or signals. It involves applying algorithms or filters to raw data points to create a smoother curve or series, making the data easier to analyze and interpret. Common applications include time-series analysis, signal processing, and data visualization.
Developers should learn data smoothing when working with noisy or volatile data, such as in financial forecasting, sensor readings, or user behavior analytics, to improve model accuracy and decision-making. It's essential for preprocessing data in machine learning pipelines, enhancing signal clarity in IoT applications, and creating cleaner visualizations in dashboards or reports.