What is Data Analytics? Definition, Types, Methods, Examples & Tool

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Data analytics is the process of examining and interpreting large sets of data to uncover insights, trends, and patterns. It involves collecting, cleaning, transforming, and modeling data to identify useful information for decision-making.

Types of Data Analytics:

Descriptive Analytics: Examines past data to understand what happened and why it happened.
Diagnostic Analytics: Analyzes past data to identify the causes of an event or problem.
Predictive Analytics: Uses statistical models and machine learning algorithms to forecast future outcomes.
Prescriptive Analytics: Recommends actions to achieve a desired outcome based on predictive analytics.

Methods of Data Analytics:

Statistical Analysis: Uses statistical methods to analyze data and make inferences.
Machine Learning: Uses algorithms to identify patterns and make predictions based on data.
Data Mining: Uses statistical and machine learning techniques to identify patterns in data.

Examples of Data Analytics:

Marketing: Analyzing customer data to identify patterns and trends to improve marketing strategies.
Healthcare: Analyzing patient data to identify health risks and develop treatment plans.
Finance: Analyzing financial data to identify trends and risks and make investment decisions.

Tools for Data Analytics:

Microsoft Excel: A spreadsheet software that can be used for basic data analysis.
R: A programming language and software environment for statistical computing and graphics.
Python: A general-purpose programming language commonly used for data analysis.
Tableau: A data visualization tool that allows users to create interactive dashboards and visualizations.

Location: Hyderabad, Telangana, India

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