Practical Example: How to Structure a Data Science Project Using Well-Organized Folders and Files#

Structuring a data science project in a well-organized manner is crucial for its success. The process of data science involves several steps from collecting, cleaning, analyzing, and modeling data to finally presenting the insights derived from it. Thus, having a clear and efficient folder structure to store all these files can greatly simplify the process and make it easier for team members to collaborate effectively.

In this chapter, we will discuss practical examples of how to structure a data science project using well-organized folders and files. We will go through each step in detail and provide examples of the types of files that should be included in each folder.

One common structure for organizing a data science project is to have a main folder that contains subfolders for each major step of the process, such as data collection, data cleaning, data analysis, and data modeling. Within each of these subfolders, there can be further subfolders that contain specific files related to the particular step. For instance, the data collection subfolder can contain subfolders for raw data, processed data, and data documentation. Similarly, the data analysis subfolder can contain subfolders for exploratory data analysis, visualization, and statistical analysis.

It is also essential to have a separate folder for documentation, which should include a detailed description of each step in the data science process, the data sources used, and the methods applied. This documentation can help ensure reproducibility and facilitate collaboration among team members.

Moreover, it is crucial to maintain a consistent naming convention for all files to avoid confusion and make it easier to search and locate files. This can be achieved by using a clear and concise naming convention that includes relevant information, such as the date, project name, and step in the data science process.

Finally, it is essential to use version control tools such as Git to keep track of changes made to the files and collaborate effectively with team members. By using Git, team members can easily share their work, track changes made to files, and revert to previous versions if necessary.

In summary, structuring a data science project using well-organized folders and files can greatly improve the efficiency of the workflow and make it easier for team members to collaborate effectively. By following a consistent folder structure, using clear naming conventions, and implementing version control tools, data science projects can be completed more efficiently and with greater accuracy.

project-name/
\-- README.md
\-- requirements.txt
\-- environment.yaml
\-- .gitignore
\
\-- config
\
\-- data/
\   \-- d10_raw
\   \-- d20_interim
\   \-- d30_processed
\   \-- d40_models
\   \-- d50_model_output
\   \-- d60_reporting
\
\-- docs
\
\-- images
\
\-- notebooks
\
\-- references
\
\-- results
\
\-- source
    \-- __init__.py
    \
    \-- s00_utils
    \   \-- YYYYMMDD-ima-remove_values.py
    \   \-- YYYYMMDD-ima-remove_samples.py
    \   \-- YYYYMMDD-ima-rename_samples.py
    \
    \-- s10_data
    \   \-- YYYYMMDD-ima-load_data.py
    \
    \-- s20_intermediate
    \   \-- YYYYMMDD-ima-create_intermediate_data.py
    \
    \-- s30_processing
    \   \-- YYYYMMDD-ima-create_master_table.py
    \   \-- YYYYMMDD-ima-create_descriptive_table.py
    \
    \-- s40_modelling
    \   \-- YYYYMMDD-ima-importance_features.py
    \   \-- YYYYMMDD-ima-train_lr_model.py
    \   \-- YYYYMMDD-ima-train_svm_model.py
    \   \-- YYYYMMDD-ima-train_rf_model.py
    \
    \-- s50_model_evaluation
    \   \-- YYYYMMDD-ima-calculate_performance_metrics.py
    \
    \-- s60_reporting
    \   \-- YYYYMMDD-ima-create_summary.py
    \   \-- YYYYMMDD-ima-create_report.py
    \
    \-- s70_visualisation
        \-- YYYYMMDD-ima-count_plot_for_categorical_features.py
        \-- YYYYMMDD-ima-distribution_plot_for_continuous_features.py
        \-- YYYYMMDD-ima-relational_plots.py
        \-- YYYYMMDD-ima-outliers_analysis_plots.py
        \-- YYYYMMDD-ima-visualise_model_results.py

In this example, we have a main folder called project-name which contains several subfolders:

  • data: This folder is used to store all the data files. It is further divided into six subfolders:

    • `raw: This folder is used to store the raw data files, which are the original files obtained from various sources without any processing or cleaning.
    • interim: In this folder, you can save intermediate data that has undergone some cleaning and preprocessing but is not yet ready for final analysis. The data here may include temporary or partial transformations necessary before the final data preparation for analysis.
    • processed: The processed folder contains cleaned and fully prepared data files for analysis. These data files are used directly to create models and perform statistical analysis.
    • models: This folder is dedicated to storing the trained machine learning or statistical models developed during the project. These models can be used for making predictions or further analysis.
    • model_output: Here, you can store the results and outputs generated by the trained models. This may include predictions, performance metrics, and any other relevant model output.
    • reporting: The reporting folder is used to store various reports, charts, visualizations, or documents created during the project to communicate findings and results. This can include final reports, presentations, or explanatory documents.

  • notebooks: This folder contains all the Jupyter notebooks used in the project. It is further divided into four subfolders:

    • exploratory: This folder contains the Jupyter notebooks used for exploratory data analysis.
    • preprocessing: This folder contains the Jupyter notebooks used for data preprocessing and cleaning.
    • modeling: This folder contains the Jupyter notebooks used for model training and testing.
    • evaluation: This folder contains the Jupyter notebooks used for evaluating model performance.

  • source: This folder contains all the source code used in the project. It is further divided into four subfolders:

    • data: This folder contains the code for loading and processing data.
    • models: This folder contains the code for building and training models.
    • visualization: This folder contains the code for creating visualizations.
    • utils: This folder contains any utility functions used in the project.

  • reports: This folder contains all the reports generated as part of the project. It is further divided into four subfolders:

    • figures: This folder contains all the figures used in the reports.
    • tables: This folder contains all the tables used in the reports.
    • paper: This folder contains the final report of the project, which can be in the form of a scientific paper or technical report.
    • presentation: This folder contains the presentation slides used to present the project to stakeholders.

  • README.md: This file contains a brief description of the project and the folder structure.

  • environment.yaml: This file that specifies the conda/pip environment used for the project.
  • requirements.txt: File with other requeriments necessary for the project.
  • LICENSE: File that specifies the license of the project.
  • .gitignore: File that specifies the files and folders to be ignored by Git.

By organizing the project files in this way, it becomes much easier to navigate and find specific files. It also makes it easier for collaborators to understand the structure of the project and contribute to it.