What Is Cookiecutter Data Science?

Delving into the world of data science can be an overwhelming experience, especially when it comes to organising and structuring your projects.

This is where Cookiecutter Data Science comes into play.

It is a logical, flexible and easy-to-use framework that helps data scientists maintain an efficient and effective workflow.

But what exactly is Cookiecutter Data Science? How does it work? And why is it so beneficial for data scientists?

In this comprehensive guide, we will explore these questions and more, providing you with a thorough understanding of this essential tool in the data science toolkit.

What is Cookiecutter Data Science?

Cookiecutter Data Science is a project structure, or a sort of template, that provides a standardised and organised framework for data science projects.

It was developed by the team at DrivenData, with the aim of promoting best practices in the field of data science.

The name ‘Cookiecutter’ is derived from the tool’s ability to create projects from templates, much like how a cookie cutter creates shapes from dough.

The ‘Data Science’ part of the name refers to the specific field for which this tool is designed.

Cookiecutter Data Science is not a library or a package that you import into your projects.

Instead, it is a file and directory structure that you follow when creating your projects.

This structure helps you organise your work in a way that is easy to understand, maintain, and share with others.

The structure of Cookiecutter Data Science

It provides a pre-defined project structure that includes directories for data, notebooks, scripts, and other components of a typical data science project.

This structure is designed to be flexible and adaptable, allowing you to modify it to suit your specific needs.

The main directories in a Cookiecutter Data Science project include:

• Data: This directory is used to store all the data used in your project. It is typically divided into subdirectories for raw data, processed data, and external data.

• Models: This directory contains the models that you build during your project. It can also include any scripts or notebooks used to train these models.

• Notebooks: This directory is used to store Jupyter notebooks, which are often used for exploratory data analysis and model development.

• Reports: This directory is used to store any reports or output from your project, such as figures, tables, or other visualisations.

• Scripts: This directory contains any scripts used in your project, such as data processing scripts or model training scripts.

By following this structure, you can ensure that your projects are organised in a consistent and logical way, making it easier for you and others to navigate and understand your work.

Why use Cookiecutter Data Science?

There are several reasons why data scientists choose to use it for their projects. Let’s explore some of the key benefits of this tool.

Automation

One of the main benefits of using it is that it promotes automation.

Using a consistent structure for all your projects ensures that your work is organised in a predictable and understandable way.

This makes it easier for you to navigate your own projects and also makes it easier for others to understand your work.

Efficiency

It can also help improve efficiency. Providing a predefined structure for your projects saves you the time and effort of having to create a new structure for each project.

This allows you to focus more on the actual data science work, rather than on project organisation.

Collaboration

Another benefit is that it facilitates collaboration.

By using a standardised structure, it makes it easier for others to understand and contribute to your projects.

This can be particularly beneficial in team settings, where multiple people may be working on the same project.

Getting started with Cookiecutter in Data Science

Let’s explore how you can start using it for your own projects.

Installation

To use the Cookiecutter approach, you first need to install the Cookiecutter tool. This can be done using pip, the Python package installer. Simply run the following command in your terminal:

pip install cookiecutter

Once Cookiecutter is installed, you can create a new project using the Cookiecutter Data Science template by running the following command:

cookiecutter https://github.com/drivendata/cookiecutter-data-science

This will prompt you to enter some information about your project, such as the project name and author name.

Once you’ve entered this information, Cookiecutter will create a new directory with the specified project structure.

Using Cookiecutter

Once you’ve created a project using Cookiecutter, you can start adding your data, scripts, notebooks, and other components to the appropriate directories.

Remember to follow the structure provided by Cookiecutter Data Science to ensure that your project is organised in a consistent and logical way.

As you work on your project, you may find that you need to modify the structure to suit your specific needs.

This is perfectly fine – it is designed to be flexible and adaptable, so feel free to make any changes that will help you work more efficiently.

Conclusion

Cookiecutter Data Science is a powerful tool that can help data scientists organise their projects in a consistent, understandable, and efficient way.

Providing a standardised structure for data science projects promotes best practices and facilitates collaboration.

You can dive deeper into this topic and more in our comprehensive Data Science & AI programme, designed to prepare you for a promising future in the field of Data Science.

Alternatively, feel free to schedule a complimentary career consultation with a member of our team to discuss the programme further.