Descriptive Statistics

Statistics enable analysts to better understand and interpret data, make predictions, and evaluate the performance of models. Descriptive statistics are used to summarize and describe the main features of a dataset by providing a quantitative summary of the data. In data science, features are individual measurable properties or characteristics of what is being observed. Features are variables, attributes, or dimensions that describe some property or information about the data to help with building a model and analyzing the data for meaningful insights.

Descriptive statistics make large datasets more manageable and interpretable by transforming raw data into actionable insights. Key types of descriptive statistics include measures of central tendency (mean, median, mode), dispersion (range, variance, standard deviation), distribution (skewness), and position (percentiles, z-scores). Descriptive statistics empower data exploration into dataset characteristics, pattern detection, and trend analysis as well as detecting outliers and data quality issues. Descriptive statistics can be used to create data visualizations and meaningful summaries for stakeholders.

Analyzing datasets using descriptive statistics allows you to simplify complex data and use the information in actionable ways. Businesses use descriptive statistics to make informed decisions, set performance benchmarks, monitor KPIs, and identify struggling areas of the business. Descriptive statistics also provide the foundation for every aspect of machine learning from initial data preparation to model deployment and monitoring.

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