Welcome to DS-ScriptsNook 👋🕹

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📌 Repository

DS-ScriptsNook would be a one-stop destination to get acquainted with Data Science. This repository encloses with the unique collection of scripts based on Linear Algebra, Calculus, Statistics, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision and Artificial Intelligence etc. Get involved in this journey of open source.

The main aim of this project is to provide an efficient and useful resources to leap into Data Science. This would help you in acquiring all the skills you need before you get into real-time projects. Get yourself into comfort stage by stopping here.

🙌 Join Here

Anyone related to technology who are looking to contribute to open-source, are all invited to hop in. This place has task for everyone and is a beginner-friendly project.

Linear Algebra

Calculus

Statistics

Machine Learning

Deep Learning

Natural Language Processing

Computer Vision

Artificial Intelligence

You can choose up : Select a topic. Decide if you want to enhance your skills through Algorithms or Libraries or Tutorials and you’re good to start.

If you had worked on or want to initiate a unique script and want to share it with the world, you can do that through here. Go through the contributing guidelines in CONTRIBUTING👩‍💻

When issue is raised from your end (or) taken it from issues tab to add a script, elaborate as much as you could as this is all about how efficiently you had gained knowledge on concepts.

Subsequently, also go through the GitHub documentation on creating a pull request.

💡 Look Through The Garage Of Useful Scripts

📝 Project Structure

Your projects should contain this flow to maintain similarity across all other projects. Make sure to note these things, before you create a PR.

For scripts on concepts, tutorials and libraries, the project structure should look like this:

Go to the concerned folder be it tutorials or libraries etc. For example, your want to add a script about Numpy Library. Go to “Machine Learning” Folder and then to “Libraries” folder. Here in this case, we are adding up an introduction to numpy. So the folder title should be a “Introduction To Numpy”

In this folder, Create a “file_name.md” and the file name should be written as “introduction_to_numpy.md”.

Since it’s a tutorial on library and a .md file. You should follow this template to prepare this file and add up the relevant images needed to justify the elaboration of the concept.

All the images used in .md file should be in “Images” folder within “Introduction To Numpy” folder. You can take up an concept and add up in respective folders. I had provided this example to guide you on a project structure.

For scripts of algorithms on Machine Learning, Deep Learning, Computer Vision, Artificial Intelligence, the project structure should look like this:

Go to the respective ones and to the “Algorithms” folder. No , create a folder of your algorithm. (Example : If you want to add an algorithm of Decision Tree Classifier, then project name should be “Decision Tree Classifier” and file name as “decision_tree_classifier.ipynb”)

Other than algorithm file, it should also have a ‘README.md’ using this template

Images - This folder would have all images added up in README.md and the script file.

Elaborate your README briefly about how it works by showing step by step procedure.

🛠 Templates to Follow

Note : One should follow these templates while creating a new issue or pull request.

👨‍💻 Workflow:

Fork the repository
Clone your forked repository using terminal or gitbash.
Make changes to the cloned repository
Add, Commit and Push
Then in Github, in your cloned repository find the option to make a pull request

print(“Start contributing for DS-ScriptsNook”)

⚙️ Things to Note

Make sure you do not copy codes from external sources because that work will not be considered. Plagiarism is strictly not allowed.
You can only work on issues that have been assigned to you.
If you want to contribute the algorithm, it’s preferrable that you create a new issue before making a PR and link your PR to that issue.
If you have modified/added code work, make sure the code compiles before submitting.
Strictly use snake_case (underscore_separated) in your file_name and push it in correct folder.
Do not update the README.md.