Modified some sentences and the tooltip for clarity

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

@@ -2,39 +2,39 @@
 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 consists of data obtained from many different sources.
-This data may be split into multiple files or directories or (as the project
-structure needs) have different versions for different requirements. e.g. A
+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 complex.
+make it increasingly difficult.
 
 DVC allows a single directory to contain all your project artifacts. The
-workspace is the directory containing _visible_ part of your
-<abbr>project</abbr> e.g. 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, then record your progress
-in a commit and move along your data and model history. DVC provides a _machine
-learning file system_ to manipulate your data and models using its commands. No
-need to keep different artifact versions in different directories, under
-different names, without any semantic relation among them. Instead DVC is able
-to link your data and models and can show the evolution, progress and
-interrelationships.
+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
+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.
+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