Project templates best practices
Learn how to utilize project templates to jumpstart your data science initiatives.
Last updated
Was this helpful?
Learn how to utilize project templates to jumpstart your data science initiatives.
Last updated
Was this helpful?
Templates are an excellent starting point for projects. The default template is based on the CRISP-DM methodology, widely used in data science and certified by the Data Science Process Alliance. However, if your organization has its own process, you can follow best practices to get the most out of these project templates.
Since every organization is different, we suggest tailoring your template to meet your specific needs. For instance, create templates for Analytics, Operations, Machine Learning, etc.
You can modify your project template by changing phases, requirements, and documentation as necessary.
Once you have a customized project template that works for your organization, we recommend that you duplicate it for future projects.
To make your project template clear, use color coding for different elements. For instance, use one color for phase descriptions, instructions, or purposes and another for different information.
In the default template, phase details are in blue, which you can edit or delete, while black text is for phase documentation outlines.
Once you have a project template suitable for multiple projects, you can use the "Duplicate Project" feature. This creates a new project while keeping the original template's phases, requirements, and documentation, saving you from starting from scratch. To duplicate a project, go to Project Settings > Duplicate Project.
Share your custom project templates with others in your organization by creating or using a workspace that includes everyone you want to share with. Members can then duplicate the template and choose their desired workspace for the project.
In your project template, define the requirements needed for each phase. When shared, users receive the template with these requirements for completing the phase.
For instance, the data preparation phase in the default template already has the necessary requirements.
When making a project template for a specific group, you can transfer the customized template to the desired workspace. This move keeps all project information, like datasets, models, code, and iterations. It lets you pre-upload datasets and other assets into the template before sharing.
