ArjanCodes
15 minutes
Structure Data Science Projects in Python Using Cookie Cutter and TyPi Pipelines
Data science projects benefit from deliberate software-engineering structure because analysis code is changed frequently, so organization, reuse, and reproducibility lower the cost of change. The core practices are a standardized project structure (project templating), reuse of established libraries, pipeline/workflow orchestration, logging and experiment tracking, intermediate data representations, extraction of reusable code into shared packages, separation of configuration from code, and unit testing. This belongs to data science engineering and software design, applying principles such as separation of concerns, modularity, and reproducibility to exploratory analytical work.