Data Science Lifecycle: From Problem to Product
Data Science Lifecycle
In real work, we don’t start with algorithms. We start with a business problem.
Lifecycle I Follow
- Problem framing: what exactly are we predicting?
- Data collection: database/APIs/files
- Cleaning + EDA: find issues, patterns
- Modeling: baseline → better models
- Evaluation: metrics + validation
- Deployment: API/app integration
Related: Roles in Data Science
