Data Analytics
Data Analytics is the systematic process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. By applying statistical, computational, and machine learning techniques, data analytics enables businesses, governments, and other organizations to make more informed, data-driven decisions.
It involves a variety of techniques, such as descriptive analytics (summarizing historical data), predictive analytics (forecasting future trends), prescriptive analytics (recommending actions), and diagnostic analytics (understanding causes of past events). These methods help uncover patterns, relationships, and insights that were previously hidden in large datasets.
Data analytics can be broadly classified
into four key types:
- Descriptive Analytics:
This type of analysis focuses on summarizing historical data to understand past trends and patterns. It answers the question, “What happened?” It involves techniques like data aggregation, data mining, and basic statistical methods to provide insights into past performance. Diagnostic Analytics:
Diagnostic analytics aims to explain why something happened by identifying the causes behind certain events or trends. It goes deeper than descriptive analytics and uses data to uncover relationships or correlations.Predictive Analytics:
Predictive analytics uses historical data and statistical models to forecast future outcomes. It answers the question, “What is likely to happen?” Techniques like machine learning, regression analysis, and time series forecasting are common in predictive analytics.Prescriptive Analytics:
Prescriptive analytics provides recommendations on what actions should be taken to achieve desired outcomes. It uses optimization and simulation algorithms to suggest the best course of action.
Key Applications of Data Analytics
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