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Copy file name to clipboardExpand all lines: _posts/2025-04-07-Data-Annotation-Fundamentals-Course.MD
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1. Starting with the [basics of Git](https://git-scm.com/book/en/v2/Git-Basics-Getting-a-Git-Repository) and understanding [what exactly a distributed version control systems is and how it works](https://about.gitlab.com/topics/version-control/benefits-distributed-version-control-system/) and how [data version control work using Git's data model](https://dvc.org/doc/user-guide) ... the first order of business is to read through this entire 60 module syllabus, so that you understand the WHY of WHY Git's data model and Git's approach to distributed **version control** will matter as much as it does to your future study of the topic of data annotation and knowledge engineering, ie ***everything****is going to be built on the foundation of Git's data model and DVCS*.
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**It is important, especially throughout Module 1 [but it will be true for other Modules] to look ahead at the other 59 modules in this course.***Skim, don't READ, just pay attention to the general roadmap.* Do not expect to understand everything now, but As you grasp the lay of the land for how data annotation works, you will see that of course, Git matters. There's no substitute for a really solid grasp of Git's underlying model ensures you can make intentional, **traceable** changes to your knowledge base. This foundation allows future automation tools to interpret your repository's history reliably and predictably.
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> **Understand why everything's built on Git. Throughout Module 1 [but this will be true for other Modules] it is ESSENTIAL to look ahead at the other 59 modules in this course to understand the pivotal nature Git will play.**: *Skim, don't READ, just pay attention to the general roadmap.* Do not expect to understand everything now, but as you grasp the lay of the land for how data annotation works, you will see that of course, Git is the indispensable tool. Thus, there is no substitute for a really solid grasp of Git's underlying model ensures you can make intentional, **traceable** changes to your knowledge base. This foundation allow you to more effectively deploy [automation to set context better for AI systems by interpreting the histories of your knowledgestores](https://enterprise-knowledge.com/what-software-engineering-can-teach-knowledge-engineers-about-version-control/), which reside in either Git repositories or some version-controlled datastore that interoperates with Git reliably and predictably for assuring both data quality and context. Otherwise *GIGO*, ie [in intelligent systems, context and data quality are kings!](https://x.com/i/grok/share/P23hgP1gcPEBOIQXcULyLc6aZ)
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2. Setting up Git: installation, configuration, and repository initialization
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