What is it? Usually done with a Data Analyst.After processing, data is analyzed to extract meaningful insights and derive value from the data.
Types of analysis:
Exploration and understanding:
- EDA: Involves exploring data sets to find patterns, anomalies, or relationships without having a specific hypothesis in mind. It is often used in the initial stages of data analysis to generate insights.
- Descriptive: Focuses on summarizing historical data to understand what has happened in the past. It often involves the use of Statistics measures and Data Visualisation tools to present data trends and patterns.
- Diagnostic: Seeks to understand why something happened. It involves examining data to identify causes and correlations, often using techniques like data mining and statistical analysis.
Forward looking:
- Predictive: Uses historical data and statistical algorithms to forecast future outcomes. It helps in identifying trends and making predictions about future events based on past data.
- Prescriptive: Goes a step further by recommending actions based on the predictions made. It uses optimization and simulation algorithms to suggest the best course of action for a given situation.
- Inferential: Makes inferences and predictions about a population based on a sample of data. It often involves Hypothesis testing and Confidence Interval.