arush
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| What is Data Analytics? Definition, Uses (4th Jun 25 at 11:09am UTC)
ata Analytics is the activity of reviewing, structuring, and understanding raw data to find valuable information | | Data Analytics is the activity of reviewing, structuring, and understanding raw data to find valuable information, make conclusions, and assist in decision-making. It involves statistical analysis, technology, and subject-matter expertise to transform data into decisive insights.
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Definition:
Data Analytics is the process of systematic computational processing of data. It is the process of examining, cleaning, transforming, and modeling the data in a way to identify patterns, draw conclusions, and make decisions.
Data Analytics Types:
Descriptive Analytics – What happened?
Distills past data to find trends and patterns (e.g., monthly sales reporting).
Diagnostic Analytics – Why did it happen?
Browses deeper into data to know reasons behind events (e.g., analysis of customer churn).
Predictive Analytics – What will most likely occur?
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Makes predictions based on statistical models and machine learning (e.g., demand forecasting).
Suggests action based on algorithms and simulations (e.g., route optimization).
Typical Applications of Data Analytics:
Industry\tUse Case
Business\tMarket analysis, customer segmentation, sales forecasting
Healthcare\tDiagnosis of patients, effectiveness of treatments, operational effectiveness
Finance\tDetection of fraud, risk analysis, algorithmic trading
Retail\tInventory optimization, price strategy, customer behavior
Sports\tAnalysis of player performance, injury prediction
Education\tTracking of student performance, optimization of curriculum
Government
Policy impact assessment, resource allocation, crime pattern analysis
Tools Typically Used:
Excel – Basic analysis and dashboards
SQL – Data querying
Python / R – Advanced analytics, machine learning
Tableau / Power BI – Data visualization
Do you want to find out how data analytics is applied in a particular industry or career path?
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