How Git Works - From Working Directory to Remote Repository
[1]. Working Directory:
Your project starts here. The working directory is where you actively make changes to your files.
[2]. Staging Area (Index):
After modifying files, use git add to stage changes. This prepares them for the next commit, acting as a checkpoint.
[3]. Local Repository:
Upon staging, execute git commit to record changes in the local repository. Commits create snapshots of your project at specific points.
[4]. Stash (Optional):
If needed, use git stash to temporarily save changes without committing. Useful when switching branches or performing other tasks.
[5]. Remote Repository:
The remote repository, hosted on platforms like GitHub, is a version of your project accessible to others. Use git push to send local commits and git pull to fetch remote changes.
[6]. Remote Branch Tracking:
Local branches can be set to track corresponding branches on the remote. This eases synchronization with git pull or git push.
[1]. Working Directory:
Your project starts here. The working directory is where you actively make changes to your files.
[2]. Staging Area (Index):
After modifying files, use git add to stage changes. This prepares them for the next commit, acting as a checkpoint.
[3]. Local Repository:
Upon staging, execute git commit to record changes in the local repository. Commits create snapshots of your project at specific points.
[4]. Stash (Optional):
If needed, use git stash to temporarily save changes without committing. Useful when switching branches or performing other tasks.
[5]. Remote Repository:
The remote repository, hosted on platforms like GitHub, is a version of your project accessible to others. Use git push to send local commits and git pull to fetch remote changes.
[6]. Remote Branch Tracking:
Local branches can be set to track corresponding branches on the remote. This eases synchronization with git pull or git push.
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Source Code of PORTFOLIO WEBSITE β€οΈπ
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βΎHANDWRITTEN NOTES βοΈ βΎοΈ
πΊDATA STRUCTURE SHORT NOTES
πΊDATA STRUCTURE
INTERVIEW SERIES πΉ(PART - 1)
πΊDATA STRUCTURE
INTERVIEW SERIES πΉ(PART - 2)
πΊDATA STRUCTURE
INTERVIEW SERIES πΉ(PART - 3)
πΊDBMS (DATABASE MANAGEMENT SYSTEM)NOTES
πΊC PROGRAMMING SHORT NOTES
πΊDATA STRUCTURE SHORT NOTES
πΊDATA STRUCTURE
INTERVIEW SERIES πΉ(PART - 1)
πΊDATA STRUCTURE
INTERVIEW SERIES πΉ(PART - 2)
πΊDATA STRUCTURE
INTERVIEW SERIES πΉ(PART - 3)
πΊDBMS (DATABASE MANAGEMENT SYSTEM)NOTES
πΊC PROGRAMMING SHORT NOTES
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How to enter into Data Science
πStart with the basics: Learn programming languages like Python and R to master data analysis and machine learning techniques. Familiarize yourself with tools such as TensorFlow, sci-kit-learn, and Tableau to build a strong foundation.
πChoose your target field: From healthcare to finance, marketing, and more, data scientists play a pivotal role in extracting valuable insights from data. You should choose which field you want to become a data scientist in and start learning more about it.
πBuild a portfolio: Start building small projects and add them to your portfolio. This will help you build credibility and showcase your skills.
πStart with the basics: Learn programming languages like Python and R to master data analysis and machine learning techniques. Familiarize yourself with tools such as TensorFlow, sci-kit-learn, and Tableau to build a strong foundation.
πChoose your target field: From healthcare to finance, marketing, and more, data scientists play a pivotal role in extracting valuable insights from data. You should choose which field you want to become a data scientist in and start learning more about it.
πBuild a portfolio: Start building small projects and add them to your portfolio. This will help you build credibility and showcase your skills.
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Most Asked Interview Questions with Answers π»β
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