Data analysis is the process of examining, cleaning, transforming, and interpreting data to extract meaningful insights, identify patterns, and support decision-making. It is widely used in business, healthcare, finance, marketing, and various other industries to make data-driven decisions.
Stages of Data Analysis:
- Data Collection: Gathering raw data from multiple sources, such as databases, surveys, or APIs.
- Data Cleaning: Removing errors, inconsistencies, duplicates, and missing values to ensure accuracy.
- Data Exploration: Understanding data structure, distribution, and relationships using descriptive statistics.
- Data Transformation: Converting data into a suitable format for analysis through normalization, aggregation, or feature engineering.
- Data Modeling: Applying statistical models, machine learning algorithms, or analytical methods to identify patterns.
- Data Interpretation & Visualization: Presenting insights through graphs, dashboards, and reports for better decision-making.
Types of Data Analysis:
- Descriptive Analysis: Summarizes historical data to understand past trends (e.g., sales reports).
- Diagnostic Analysis: Investigates the reasons behind a particular outcome or trend (e.g., drop in customer retention).
- Predictive Analysis: Uses statistical models and machine learning to forecast future outcomes (e.g., demand forecasting)