How to Organize Your Data Science Project | by Angelica Lo Duca | Jun, 2023


Environment Setup, Data Science

Strategies for efficiently planning and organizing your data science projects through manual installation, Cookiecutter, or a cloud service.

Photo by Alvaro Reyes on Unsplash

A successful data science project requires careful planning and organization throughout its phases. Whether you prefer manual organization or an external tool, you can use various strategies to streamline your workflow.

This blog post will explore three main strategies to organize your data science project:

  • Manual organization
  • Using an external tool for management
  • Using a cloud service

Manual organization involves structuring your data science project using directories and files without relying on any external tools. This approach gives you complete control over the organization and allows you to tailor it to your project needs.

Follow the best practices described below for manually organizing your data science project:

  1. Create a project directory for your data science project. This will serve as the root directory for all your project files.
project_dir/

2. Separate data and code: Divide your project into two main directories: data-related files and code-related files.

project_dir/
├── data/
├── code/

3. Organize data files: Within the data directory, create subdirectories to store different data types, such as raw data, processed data, and intermediate results.

project_dir/
├── data/
│ ├── raw/
│ ├── processed/
│ └── intermediate/
├── code/

4. Split code into modules based on functionality. Each module should have its directory and contain related scripts or notebooks.

project_dir/
├── data/
├── code/
│ ├── preprocessing/
│ ├── modeling/
│ └── evaluation/

5. Use version control: Initialize a Git repository within your project directory to track changes and collaborate with others effectively.

project_dir/
├── .git/
├── data/
├── code/

6. Include a README file to describe your project.

project_dir/
├── .git/
├── data/
├── code/
└── README.md

7. Utilize virtual environments to isolate dependencies and ensure reproducibility.

project_dir/
├── .git/
├── data/
├── code/
├── README.md
└── env/

Now that you have learned how to organize your data science project manually, let’s move to the next step, using an external tool for management.

Manual installation may be time-consuming and error-prone. Additionally, the lack of a documented process makes reproducing the exact software environment difficult, hindering collaboration and the ability to reproduce results accurately. You can use an external data science project management tool to overcome the previous issues.

Many tools exist for project management. In this article, we will focus on Cookiecutter. Cookiecutter enables you to define project structures based on predefined templates. It provides a command-line interface to generate project directories, files, and initial code snippets.

  1. Start by installing Cookiecutter:
pip install cookiecutter

2. Choose a data science project template: You can browse the available templates on GitHub or other community-driven repositories. For example, you can use the template defined by the official Cookiecutter repository to organize a data science project template:

3. Run the following command to install the template:

cookiecutter -c v1 https://github.com/drivendata/cookiecutter-data-science

The template requires Git to be installed. Cookiecutter will prompt you to provide values for project-specific parameters defined in the template, such as project name, author, and project description. Enter the required information to customize the project. The following code shows an example of the prompt:

> cookiecutter https://github.com/drivendata/cookiecutter-data-science
project_name [project_name]: my-test
repo_name [my-test]: my-test-repo
author_name [Your name (or your organization/company/team)]: angelica
description [A short description of the project.]: a test project
Select open_source_license:
1 - MIT
2 - BSD-3-Clause
3 - No license file
Choose from 1, 2, 3 [1]: 1
s3_bucket [[OPTIONAL] your-bucket-for-syncing-data (do not include 's3://')]:
aws_profile [default]:
Select python_interpreter:
1 - python3
2 - python
Choose from 1, 2 [1]: 1

The following figure shows the generated directories and files:

Image by Author

Now you can start working on your files.

In Cookiecutter, you can define your custom templates by following the procedure described in the Cookiecutter official repository.

So far, we’ve seen two techniques for organizing data science projects: one manual technique and one based on Cookiecutter. Actually, there is also a third technique that almost completely solves the problem of organizing files and folders on your computer. It’s about using a cloud service.

There are many services of this type, which, in technical terms, are called model tracking platforms or experimentation platforms. Examples of these services are Comet, Neptune, and MLflow (which you can install on your computer). These services aim to manage all experiments, code, data, and even results in the cloud.

Model tracking platforms also provide dashboards in which you can compare the results of the experiments directly through tables or graphs. The following figure shows an example dashboard in Comet.

An example of a dashboard in Comet

You can browse other examples of dashboards at this link.
Using a model tracking platform is quite simple. The following figure shows an example of the architecture of a model tracking platform.

Image by Author

You start with your local models, which can be stored in a single file. Then you save them on the model tracking platform, which, in addition to containing a dashboard, also contains a registry for accessing the produced assets. You can export the results to a report or integrate them into a deployment flow.

Using a model tracking platform is a good solution. However, remember that the service could require you to spend money to use it.

Congratulations! You have just learned how to organize your data science project! You can use one of the following techniques:

  • Manual organization, which is time-consuming and error-prone
  • External tool, such as Cookiecutter, which helps to create the initial structure of your project
  • Cloud service, which organizes all the code for you, but it could require you to pay.

Choose the technique that best suits your needs and requirements to ensure a well-organized and successful data science project!



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