Dirta Science is a theme for the Python3
cookiecutter package that implements
CRISP-DM, using GitLab for CI and project phase tracking.
It allows you to create a standardised directory structure for easy maintainability of data science projects, with make commands for implementating unit testing and virtual enviroment management.
Start using it by installing cookiecutter and executing:
on your terminal of choice.
End-to-end data science projects will usually follow these steps:
- Understanding business and technical requirements
- Data collection
- Data cleaning
- Data modelling
Typically, these tasks begin sequentially but can easily be retread when things change, e.g. business requirements, or challenges arise, e.g. limitations of deployment enviroment. These kind of changes affect the “discovery process” because they impact:
- Data captured
- Domain knowledge
- Analytical method
- User biases
Without tracking these changes, it becomes difficult to differentiate between what discovered knowledge is valuable or not, which can cause project creep.
Additionally, if not predefined, it can be a dice roll as to how a project may be architected. Could it be a directory full of Python scripts or maybe some Jupyter notebooks? Will I see a final final draft?
This is not the fault of the team, but just the outcome of multiple people working on a project with little/no structure and limited memory. Imagine coming back to an unstructured project in 6 months time and trying to remember what you did…
A quick search and you’ll find many frameworks (KDD, SEMMA, etc.). Having looked at KDD and CRISP-DM, CRISP-DM provided some lower level project start questions than KDD and, therefore, a better ability to track impact of changes. With
Dirta Science the CRISP-DM template documents are made readily available. I’ve found it’s not a painless solution, but it’s certainly a step in the right direction to checklist things that can be easily missed.
cookiecutter is a great way to easily set up a standardised project structure, and building a custom theme was a simple process. Initially based from Cookiecutter Data Science
, I’ve come to the current architecture, integrating GitLab’s CI tool, and adding in unit testing and
virtualenv within the theme. Again, it’s not a perfect solution and deviating from this format has sometimes been necessary, e.g. projects using MATLAB’s app designer.
See the repository here .
Shoutout to Cookiecutter Data Science, without whom I wouldn’t have gone down this ‘mild’ rabbit hole to improve my process.
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. and Wirth, R. (2000). CRISP-DM 1.0 Step-by-step Data Mining Guide. Technical Report. The CRISP-DM Consortium.
Frawley, W., Piatetsky-Shapiro, G. and Matheus, C. (1992). Knowledge Discovery in Databases: An Overview. AI Magazine, (Vol 13, No 3), pp.57-69.