Descriptive Statistics
Descriptive statistics is a branch of statistics that involves summarizing and describing the main features of a dataset using numerical and graphical methods. It focuses on measures such as central tendency (e.g., mean, median), dispersion (e.g., standard deviation, range), and distribution shape (e.g., skewness, kurtosis), without making inferences beyond the data. This concept is fundamental in data analysis for understanding patterns, trends, and basic characteristics of data.
Developers should learn descriptive statistics to effectively analyze and interpret data in fields like data science, machine learning, and business intelligence, as it helps in data exploration, quality assessment, and communication of insights. It is essential for tasks such as preprocessing data, identifying outliers, and summarizing results in reports or dashboards, making it a core skill for roles involving data-driven decision-making.