The data science lifecycle

The data science lifecycle is the process of applying data science methods and techniques to solve business or other problems.

It usually includes the following stages:

1. Business Understanding: Define the business problem and goals.

2. Data Understanding: Collect and explore data to better understand it and identify potential problems.

3. Data preparation: Cleans and transforms data to prepare it for analysis.

4. Modeling: Use statistical and machine learning techniques to build models to make predictions about data or to discover patterns in data.

5. Evaluate: Evaluate the performance of the models and select the best model.

6. Deployment: Deploy the model in production and monitor its performance over time.

The data science lifecycle is an iterative process, and you may find that, over time, you need to go back to earlier stages.