A data science project is a practical application of your skills. A typical project allows you to use skills in data collection, cleaning, analysis, visualization, programming, machine learning, and so on. It helps you take your skills to solve real-world problems.
Structure Data Science Project
Here are some of the tools and resources which help you to successfully structure your data science projects:
Cookiecutter, a command-line utility, helps you develop projects from provided templates. The platform allows you to make your unique project template or leverage an existing one. And what makes this tool robust is how you can import templates easily and utilize only the parts that work for you appropriately.
Its installation is straightforward - download the template by installing Cookiecutter to get started. Then create a specific project based on that template, and provide details of your project to get started.
2. Install Dependencies
You can manage dependencies using one of the many platforms easily available online. These tools help you isolate the primary and sub-dependencies into two different files instead of storing dependencies in (requirements.txt).
Moreover, they help you create legible dependencies files, avoid downloading new packages conflicting with the current packages, and set your project with only a few code lines.
The project template structure you generate enables you to arrange your data, source code, reports, and files for your data science workflow. With this structure, you can monitor alterations made to the project.
Here are some of the folders your project should have:
Models. A model is the final product of a machine learning channel. They need to be stored in a consistent folder arrangement to make sure that you can reproduce the precise models’ copies in the future.
Data. It is essential to segment the data to replicate similar results in the future. The data you have for building your machine learning model might not be the exact data you’ll have in the future, i.e., the data might be overwritten or missed in a worst-case scenario. So, to have reproducible/maintainable machine learning pipelines, it is crucial to keep all your raw data irreversible. Any progress you make on your raw data needs to be appropriately documented, and that is where folders come in handy. And you don’t have to name your documents as (final2_17_02_2020.csv), (final_17_02_2020.csv) anymore to keep track of the changes.
Notebooks. Various data science projects are carried out in Jupyter notebooks, allowing the readers to comprehend the project pipeline. Essentially, notebooks are filled with multiple code blocks and functions, making the creators overlook the code blocks’ functionality. Storing your code blocks, results, and functions in isolated folders lets you segment the project more and makes it easier to follow the project rationale in notebooks.
Src. An Src folder stores the functions utilized in your pipeline. You can stash these functions according to their connection in functionality, such as a software product. Also, you can effortlessly debug and test your processes, while leveraging them is as simple as importing them into notebooks.
Reports. Data science projects produce not only a model but also charts and figures as part of the data analysis workflow. These can be bar charts, parallel lines, scatter plots, etc. You should store the generated figures and graphics to access them easily when required.
Makefiles allow data scientists to structure their data science project workflow seamlessly. Moreover, the tool also helps data scientists document their pipelines and reproduce the built models. With Makefile, you can ensure reproducibility and simplified collaboration within a data science team.
5. Leverage Hydra for Configuration Files Management
Hydra, a Python library, lets you access parameters from configuration files in a Python script.
Configuration files store all of the values in a centralized location, helping you separate those values from the code and prevent hard coding. All configuration files are deposited under this template’s “config” directory.
6. Manage Models and Data With DVC
The data is stored in the subdivisions under: “data.” Every subdirectory saves the data from diverse stages. As Git isn’t ideal for version binary files, you can leverage Data Version Control (DVC) to version your models and data.
A significant benefit of using Data Version Control is that it lets you upload data monitored by the platform to remote storage. Also, you can retain your data on Google Drive, DagsHub, Amazon S3, Google Cloud Storage, Azure Blob Storage, etc.
7. Check Coding Issues Before Committing
While committing the Python code, you need to ensure that your code:
Conforms to the style guide (PEP 8)
However, it can be daunting to ensure all these criteria before committing your code. This is where the pre-commit framework comes into play, as it lets you identify straightforward issues in your code before you execute it.
8. Add API Documentation
This mandates that you have adequate time to collaborate with the relevant team members as a data scientist. Therefore, it is pivotal to create accurate project-related documentation.
Better collaboration/communication across the data science team. When all the members in the group follow the same project structure, it becomes easy to identify the amendments made by others.
Efficiency. When you use old Jupyter notebooks to reprocess some of the functions for your new data science project, you may end up iterating through 10 notebooks on average. In such cases, discovering a 20-line code can be daunting. When you structure your data science project, you submit the code in a consistent arrangement that prevents duplication and self-repeating, and you also have less trouble finding what you are looking for.
Reproducibility. It is essential to have reproducible models to keep track of versioning and make it possible to revert to previous versions quickly if one model fails. When you structure and document your tasks in a reproducible fashion, you can successfully determine if the new model is performing better than the former ones.
Data management. It is vital to separate raw data from processed and interim data. This helps ensure that all the team members working on the data science project can effortlessly replicate the existing models. The time you spend to find the respective datasets leveraged in one of the model structure stages is significantly reduced.
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