Kedro VS cookiecutter-data-science

Interesting, thanks for sharing. I'll definitely take a look, although at this point I am so comfortable with Snakemake, it is a bit hard to imagine what would convince me to move to another tool. But I like the idea of composable pipelines: I am building a tool (too early to share) that would allow to lay Snakemake pipelines on top of each other using semi-automatic data annotations similar to how it is done in kedro (https://github.com/kedro-org/kedro).

# pyproject.toml [project] dependencies = [ "kedro @ git+https://github.com/kedro-org/kedro@3ea7231", "kedro-datasets[pandas.CSVDataSet,polars.CSVDataSet] @ git+https://github.com/kedro-org/kedro-plugins@3b42fae#subdirectory=kedro-datasets", ]

So there's 2 sides to pipeline management: the actual definition of the pipelines (in code) and how/when/where you run them. Some tools like prefect or airflow do both of them at once, but for the actual pipeline definition I'm a fan of https://kedro.org. You can then use most available orchestrators to run those pipelines on whatever schedule and architecture you want.

We have set up a milestone on GitHub so you can check in on our progress and contribute if you want to. To suggest features to us, report bugs, or just see what we're working on right now, visit the Kedro projects on GitHub.

you could use a project framework like kedro to force you to be more disciplined about how you structure your projects. I'd also recommend checking out this book: Edna Ridge - Guerrilla Analytics: A Practical Approach to Working with Data

Recently I started a position as Developer Advocate for Kedro, an opinionated data science framework, and one of the things we're doing is exploring what are the best open source tools we can use to create our documentation.

You can also check out Kedro, it’s like the Flask for data science projects and helps apply clean code principles to data science code.

An often overlooked framework used by NASA among others is Kedro https://github.com/kedro-org/kedro. Kedro is probably the simplest set of abstractions for building pipelines but it doesn't attempt to kill Airflow. It even has an Airflow plugin that allows it to be used as a DSL for building Airflow pipelines or plug into whichever production orchestration system is needed.

I opened an Anaconda cmd window and ran `cookiecutter https://github.com/drivendata/cookiecutter-data-science ` . I answered all prompted questions. After searching for a while I found where the project folder was created. However, how do I get this on GitHub? The only thing I can figure out is to create a brand new repo on GitHub with the exact same name, open it in GitHub desktop, click "show in explorer", and then drag and drop all files from the Cookiecutter folder into the GitHub Desktop folder. However to me this does not sound like the intended way to create a new project and put it on GitHub.

I'm not sure what "data scraping" means exactly, but for data science generally I think this is a pretty good template: https://github.com/drivendata/cookiecutter-data-science

Perhaps this cookiecutter template will help: https://github.com/drivendata/cookiecutter-data-science

I've found https://github.com/drivendata/cookiecutter-data-science as a guide, but haven't found any repos that solve a problem end to end actually use it. Are there any good repos or resources that exemplify how to solve a DS/ML case end-to-end? Including any UI (a report, stream, dash etc) needed for delivery, handling data, preprocessing, training and local development.

Project don't need to be 100% original, do the project in a such a way that other people has not done yet. There are plenty of datasets and notebooks available on Kaggle. Those are just bunch of notebooks. Take the inspiration from the notebook and build the project in modular structure and organize your project in proper folders and modules. I am using this cookiecutter for building my portfolio projects. https://github.com/drivendata/cookiecutter-data-science

Hi all,I am working on ML project and have created project using the cookiecutter-data-science scaffolding. The structure of the project somewhat looks like this,

While data science is not SE, it's fundamental to have some structure in your projects since you want the work to be somewhat reproducible. I recommend you start here https://github.com/drivendata/cookiecutter-data-science Since it's a cookie cutter it will be easier to implement at first since they can create the structure by running a short command, after some time you will tailor it to your specific company needs :) For notebooks it's kind of hard, they can't be peer reviewd that easily since cells are editable even after code has been run, keeping the old result... I recommend tools like deepnote, but I'm not sure how well they work for collaboration in notebooks because I never used them yet, I just know they are working on solving these problems. I hope these things help!

Personally I like using cookiecutter’s data science project template. It is easy to set up and has a clear structure. Here is their github: https://github.com/drivendata/cookiecutter-data-science