added a BC: workspace document

content/docs/sidebar.json

@@ -91,6 +91,12 @@
         "label": "What is DVC?",
         "slug": "what-is-dvc"
       },
+      {
+        "label": "Basic Concepts",
+        "slug": "basic-concepts",
+        "source": false,
+        "children": ["workspace"]
+      },
       {
         "slug": "project-structure",
         "source": "user-guide/project-structure/index.md",

content/docs/user-guide/basic-concepts/workspace.md

@@ -2,7 +2,47 @@
 name: Workspace
 match: [workspace]
 tooltip: >-
-  Directory containing all your project files e.g. raw datasets, source code, ML
-  models, etc. Typically, it's also a Git repository. It will contain your DVC
+  The directory containing all your project files, e.g., the raw data, source
+  code, ML models. Typically, it's also a Git repository. It contains your DVC
   project.
 ---
+
+# Workspace
+
+A data science project can consist of data obtained from many distinct sources.
+These may be split into multiple files or directories or (as the project
+structure needs) have different versions for different requirements, e.g., a
+smaller / simplified version might be required in prototyping for faster
+feedback and shorter training times. A single workspace to manage all artifacts
+of a project is desirable, although versioning needs and managing dependencies
+make it increasingly difficult.
+
+DVC allows a single directory to contain all your project artifacts. The
+workspace is the directory containing the _visible_ part of your
+<abbr>project</abbr>, e.g., the raw data, source code, model files. You can have
+multiple versions of data, models, and other kinds of artifacts within the
+workspace and limit your focus to a subset of these. You can record your
+progress in a commit and analyze your data and model history. DVC provides a
+_machine learning file system_ to manipulate your data and models using its
+commands. No need to rename your models for minor changes, save cleaned up data
+in different directories or save tens of different renamed files for training
+programs. DVC can keep track of all of these in a single directory called the
+workspace.
+
+Files and directories in the workspace can be added to DVC (`dvc add`), or they
+can be downloaded from external sources (`dvc get`, `dvc import`,
+`dvc import-url`). Changes to the data, notebooks, models, and any related
+machine learning artifact can be tracked (`dvc commit`), and their content can
+be synchronized (`dvc checkout`). Tracked data can be removed (`dvc remove`)
+from the workspace.
+
+DVC supports all typical operations of a versioned data file system through its
+commands. Behind the scene these operations use <abbr>metafiles</abbr> like the
+`.dvc/` directory, `dvc.yaml` files or files with `*.dvc` extension to track the
+content and dependencies.
+
+## Further Reading
+
+- [What is DVC?](/doc/user-guide/what-is-dvc)
+- [Versioning Data and Model](/doc/use-cases/versioning-data-and-model-files)
+  from Use Cases