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.