Differentiate between descriptive, predictive, and prescriptive analytics, and provide examples of how each is applied in real-world scenarios.

1. Descriptive Analytics:
Descriptive analytics deals with understanding historical data and providing insights into what has happened in the past. It involves summarizing and visualizing data to identify patterns, trends, and anomalies. Descriptive analytics aims to answer questions like "What happened?" or "What is the current state?"

Real-world Example:
In retail, descriptive analytics can be used to analyze historical sales data to understand which products have been the best-sellers over the past year. This analysis helps retailers identify popular products and plan inventory accordingly.

2. Predictive Analytics:
Predictive analytics uses historical data and statistical modeling techniques to make predictions about future outcomes. It involves identifying patterns and relationships in the data to forecast what is likely to happen. Predictive analytics aims to answer questions like "What is likely to happen next?"

Real-world Example:
In healthcare, predictive analytics can be applied to identify patients at a high risk of readmission. By analyzing historical patient data and identifying patterns that lead to readmission, hospitals can proactively intervene and provide targeted care to reduce readmission rates.

3. Prescriptive Analytics:
Prescriptive analytics goes beyond predictions and provides recommendations on what actions to take to achieve a particular outcome. It combines historical data, predictive models, and optimization techniques to suggest the best course of action. Prescriptive analytics aims to answer questions like "What should we do to achieve a specific goal?"

Real-world Example:
In logistics and supply chain management, prescriptive analytics can be used to optimize delivery routes for a fleet of vehicles. By considering factors like traffic conditions and delivery time windows, prescriptive analytics can recommend the most efficient routes to minimize delivery costs and time.

Summary:

* Descriptive analytics deals with historical data and provides insights into past events.
* Predictive analytics uses historical data to make predictions about future outcomes.
* Prescriptive analytics goes beyond predictions and recommends specific actions to achieve desired outcomes.

Together, these three types of analytics form a powerful framework for understanding data, making informed decisions, and driving improvements in various industries and sectors.