Data analytics involves a complete process of collecting, cleaning, analyzing, and interpreting data to help make better decisions. Here’s a clear breakdown:
🔍 What Data Analytics Involves
1. Data Collection
Gathering data from various sources such as:
- Databases
- Websites
- Sensors
- Surveys
- Transaction systems
2. Data Cleaning
Making the data accurate and usable by:
- Removing duplicates
- Handling missing values
- Correcting errors
- Standardizing formats
This is one of the most important steps—dirty data leads to wrong insights.
3. Data Exploration & Analysis
Using statistics and tools to:
- Understand patterns
- Identify trends
- Detect anomalies
- Summarize data
Tools used: Python, R, SQL, Excel, Power BI, Tableau
4. Data Modeling
Applying mathematical or machine learning models to:
- Predict outcomes
- Classify data
- Find relationships
Examples:
- Linear regression
- Decision trees
- Clustering
- Time-series forecasting
5. Data Visualization
Creating charts and dashboards to make insights easy to understand.
Tools:
- Tableau
- Power BI
- Python libraries (Matplotlib, Seaborn, Plotly)
6. Interpretation & Decision-Making
Explaining insights and helping businesses:
- Improve performance
- Reduce risk
- Identify opportunities
- Optimize processes
In short:
Data analytics = Data → Insights → Decisions
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