Data Science Life Cycle

When working on a data project it is always an advantage to follow a well-defined process. This could be similar to conducting research. In statistics, it is often referred to as the 6 steps hypothesis testing steps. There are methodologies that you can use to guide your projects such as Cross-industry standard process for data mining (CRISP-DM), Knowledge Discovery in Databases (KDD) or Sample, Explore, Modify, Model, and Assess methodology (SEMMA).

The data project can follow this iterative agile process:
Business Problem Understanding
Data Acquisition
Data Preparation
Hypothesis and Modelling
Evaluation and Interpretation
Deployment
Monitoring
Optimization