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Repository: Academic Time Machine 📚

Welcome to the repository containing past papers for computer science (CS), software engineering (SE), and data science (DS) courses. 🎓 This repository aims to provide a comprehensive collection of past papers to help students prepare for their exams effectively. Whether you're a current student or an alumnus looking to brush up on your knowledge, you'll find this repository useful. 📖🚀

To access and contribute to this repository, please follow the instructions below:

1. Create a pull request 🤝 Fork the repository, add materials (all the files should be in .pdf format) and create a pull request to merge your fork (Important: Kindly mention in the comments, the material you want to add).

2. Contact Collaborators 📞 Alternatively, you can reach out to any of the current collaborators listed in the repository to request updates or share new past papers. They will be happy to assist you in keeping the repository up-to-date. 😄

3. Fork the Repository 🍴 If you wish to make your own changes or updates to the repository without becoming a collaborator, you can fork the repository to create your copy. Feel free to contribute by adding new past papers, organizing the existing collection, or suggesting improvements. 💪🌟

4. Download Specific Folders 📥 If you want to download only specific course folders from a repository without downloading the whole repository, you can use the following steps:

• Go to the link of the repository (https://github.com/saleha-muzammil/Academic-Time-Machine/)
• Press . or replace .com with .dev in URL to open the repository in GitHub's internal editor
• In Explorer pane (left side or press Ctrl+Shift+E), Right click on the required file/folder and select download.
• In the Select Folder dialog box, choose the directory on your disk under which you want the selected file/folder to download.

Happy learning and best of luck with your exams! 📚✨

We would like to extend our sincere gratitude to @syedmalimustafa (who first compiled this repository) , @raza-h , @Hassaan-T075 , @saimali1124 ,@Mahd67 , @Huzaifa-fh for their valuable contributions to the Academic Time Machine repository. Their involvement and efforts have greatly enhanced the collection of past papers and improved the overall quality of the repository. 👏👏