How to organize project's data¶

Data files¶

Creating separate folders for raw, external, interim, and processed data is the best practice for organizing and managing data in a project. This approach helps to maintain a clear and organized structure for the data, and can also help to ensure that the different stages of data processing are clearly defined and documented.

Folder Structure and Naming Convention¶

The data folder structure should be organized into distinct categories, each serving a specific purpose in the data processing pipeline. Here’s the recommended structure:

data
├── raw                   # Original, immutable data dump
├── external              # Data from third-party sources
├── interim               # Intermediate data, partially processed
├── processed             # Fully processed data, ready for analysis
└── features              # Engineered features ready for model training

Explanation of Categories¶

• Raw:

• Contains the original datasets. Data in this folder is immutable and serves as a backup for all processing steps.

• External:

• For data sourced from outside the original dataset. This includes third-party data, external APIs, or any supplementary data.

• Interim:

• Holds data that is in the process of being cleaned or transformed. Useful for datasets that are not final but are needed in intermediate stages.

• Processed:

• Contains the final version of the dataset, which is ready for analysis or modeling. Data in this folder is cleaned, formatted, and pre-processed.

• Features:

• Dedicated to storing feature sets that are used in machine learning models. This includes transformed data, new features, and selected features.

Having a clear and organized structure for the data can help to ensure that it is properly managed throughout the project and that any necessary modifications or transformations can be easily traced and recorded. Additionally, it can also make it easier for other team members or stakeholders to understand and access the data as needed.

Centralized data folders: whether to use them or not¶

The answer to this question depends on the specific requirements of the project and the organization's data management policies.

In some cases, it may be appropriate for data scientists to keep a copy of the project's data on their local machines for ease of access and local processing. This can be especially useful when working with large data sets that may not be feasible to store and transfer over the network.

However, in most cases, it is a best practice to use a centralized shared data folder that is stored on a networked storage system or a cloud-based data storage service. This allows all team members to access the data, and ensures that the data is stored in a secure and organized manner. Additionally, using a centralized data folder can help to avoid duplication and ensure that everyone is working with the same version of the data.

Regardless of the approach used, it's important to establish clear guidelines and protocols for data management and access, and to ensure that data security and privacy concerns are properly addressed. This may involve implementing access controls, encryption, and backup and recovery measures.

Using VS Code on a project with a centralized shared data folder¶

Step 4 of the Getting Started guide involves creating a shared data directory for the project. To make the data accessible and visible in VS Code, symbolic links need to be created.

Here's a shell script that creates symbolic links for the specified folders:

folders=(raw external interim processed model_output)

for folder in "${folders[@]}"; do
  ln -s /path/to/original/"$folder" /path/to/link/"$folder"
done

Where the path to the original folder corresponds to the project's shared data folder:

/nyl/data/tenants/insurance/cdsamktg/mediamix/data

And the link folder corresponds to the location of the project's data folder on the cloned Git repository on server user space:

/nyl/data/home/t93kqi0/projects/Media-Mix/data/"$folder"

folders=(raw external interim processed model_output)

for folder in "${folders[@]}"; do
  ln -s /nyl/data/tenants/insurance/cdsamktg/mediamix/data/"$folder" \
  /nyl/data/home/t93kqi0/projects/Media-Mix/data/"$folder"
done

Now all shared data in each sub-directory of the data shared folder must be visible from the VS Code Explorer pane.

Metadata and Documentation¶

Discussing the importance of Metadata and Documentation in the context of your data science project is crucial. Here's a detailed breakdown:

Importance of Metadata and Documentation¶

1. Contextual Understanding: Metadata provides context to the data. It includes details like the source of the data, when and how it was collected, its format, and any changes it has undergone. This information is essential for anyone trying to understand or use the data effectively.

2. Reproducibility and Traceability: Proper documentation ensures that data processing steps can be replicated and understood by others. This is particularly important in data science, where reproducibility is a key aspect of project credibility and validation.

3. Data Quality Insights: Metadata can include information about data quality, such as accuracy, completeness, and consistency. This is valuable for assessing the reliability of the data and understanding its limitations.

4. Compliance and Auditing: In many industries, there are regulatory requirements for data management. Detailed metadata and documentation help in complying with these regulations and make audits more manageable.

Recommendations for Effective Metadata and Documentation¶

1. Standardized Format: Adopt a standardized format or template for metadata and documentation. This could be a structured file like JSON or XML for metadata, and markdown or structured text files for documentation.

2. Automated Generation: Where possible, automate the generation of metadata. For instance, when data is imported or processed, scripts can automatically capture and record key information.

3. Versioning of Data and Metadata: Just like the data, its metadata should also be version-controlled. This is important as the data evolves over time due to various processing steps.

4. Inclusion of Key Elements: Ensure that metadata includes essential elements like data source, date of acquisition, data format, any preprocessing steps applied, data schema (if applicable), and information on confidentiality or sensitivity.

5. Accessibility: Store metadata and documentation in an easily accessible location. It should be clearly linked to the corresponding data.

6. Training and Guidelines: Provide team members with training or guidelines on how to create and maintain proper documentation and metadata. This ensures consistency and compliance with established standards.

7. Regular Updates: Just as data changes, so should its documentation. It's important to update documentation whenever the data or its handling procedures change.

8. Use of Tools: Leverage tools that support metadata management and documentation. For instance, data cataloging tools can be very helpful in larger organizations for maintaining a central repository of metadata.

9. Collaboration and Reviews: Encourage regular reviews of metadata and documentation by different team members. This not only improves the quality but also ensures that the data remains understandable and usable by everyone.

10. Integration with Data Pipeline: Integrate metadata generation and documentation updates into your data processing pipelines. This ensures that changes in data are automatically reflected in the metadata and documentation.

In summary, comprehensive and up-to-date metadata and documentation are fundamental to the effective management, use, and understanding of data in any data science project. They facilitate better collaboration, ensure compliance with standards and regulations, and significantly contribute to the overall integrity and usability of the data.

Example of Metadata Creation¶

Using a JSON file to store metadata for a CSV file is an efficient way to keep track of important information about your data. Below is an example of how this can be done, along with a method to automate the process.

Example: JSON Metadata for a CSV File¶

Suppose you have a CSV file named sales_data.csv. The metadata for this file could include information such as the source of the data, the date of creation, the number of rows and columns, column names, and any preprocessing steps applied.

Here's an example of what the JSON metadata might look like:

{
  "file_name": "sales_data.csv",
  "creation_date": "2024-01-22",
  "source": "Internal Sales System",
  "number_of_rows": 1200,
  "number_of_columns": 5,
  "columns": [
    {"name": "Date", "type": "Date", "description": "Date of sale"},
    {"name": "Product_ID", "type": "String", "description": "Unique identifier for the product"},
    {"name": "Quantity", "type": "Integer", "description": "Number of products sold"},
    {"name": "Price", "type": "Float", "description": "Sale price per unit"},
    {"name": "Total_Sales", "type": "Float", "description": "Total sales amount"}
  ],
  "preprocessing": [
    {"step": "Data Cleaning", "description": "Removed null values and corrected data formats"},
    {"step": "Normalization", "description": "Normalized the Price column using min-max scaling"}
  ],
  "notes": "Data updated monthly. Last update included Q4 2023 sales data."
}

Automating the Metadata Generation¶

To automate the process of generating this metadata, you can use a script in Python. This script will:

1. Read the CSV file.
2. Extract relevant information such as the number of rows and columns, column names, etc.
3. Generate and save the metadata in a JSON file.

Here's a simple Python script to achieve this:

import json
import pandas as pd
from datetime import datetime

def generate_metadata(csv_file_path):
    # Read the CSV file
    df = pd.read_csv(csv_file_path)

    # Extracting information
    file_name = csv_file_path.split('/')[-1]
    creation_date = datetime.now().strftime("%Y-%m-%d")
    number_of_rows = df.shape[0]
    number_of_columns = df.shape[1]
    columns = [{"name": col, "type": str(df[col].dtype)} for col in df.columns]

    # Metadata dictionary
    metadata = {
        "file_name": file_name,
        "creation_date": creation_date,
        "source": "Specify the data source",
        "number_of_rows": number_of_rows,
        "number_of_columns": number_of_columns,
        "columns": columns,
        "preprocessing": [],  # Add any preprocessing steps manually or through code
        "notes": "Add any additional notes here"
    }

    # Saving metadata to a JSON file
    with open(file_name.replace('.csv', '_metadata.json'), 'w') as json_file:
        json.dump(metadata, json_file, indent=4)

# Example usage
generate_metadata('path/to/your/sales_data.csv')

Notes:¶

• The preprocessing section is left empty in the script. This is because preprocessing steps can vary widely and might need to be added manually or captured through additional scripting logic based on your specific data pipeline.
• The script assumes a basic understanding of the data's source and nature. Adjustments may be required based on the specific context of your data.
• The script uses the pandas library for data handling. Ensure this library is installed in your Python environment (pip install pandas).

This automated approach can save significant time and reduce errors in metadata creation, especially for large datasets or frequent data updates.

Data Documentation Example¶

Overview¶

This document describes the sales_data.csv dataset, which contains records of sales transactions for a retail company. The dataset is updated monthly and includes detailed information about each sale, including the date, product details, and sale amounts.

File Details¶

The file is named sales_data_documentation.md which clearly indicates its purpose and content.

• File Name: sales_data.csv
• Creation Date: 2024-01-22
• Last Updated: 2024-01-22
• Total Records: 1200
• Source: Internal Sales System

Data Dictionary¶

Preprocessing Steps¶

1. Data Cleaning
2. Null values in the Price and Quantity columns were removed.

3. Date formats were standardized to YYYY-MM-DD.

4. Normalization

5. The Price column was normalized using min-max scaling.

Usage Notes¶

• The dataset is intended for internal use only.
• Data is confidential and should not be shared outside the organization without proper authorization.
• For any discrepancies or data requests, contact the Data Management Team.

Versioning¶

• Current Version: 1.2
• Previous Versions:
• 1.1 - Included Q3 2023 sales data.
• 1.0 - Initial dataset creation.