Merge pull request #77 from ds-modules/cg_refactor

Refactoring of the CG

_toc.yml

@@ -23,27 +23,27 @@ parts:
 - caption: Getting started with DataHub tools
   chapters:
   - file: technology/pedagogy-and-technology/introduction-to-jupyter
-    title: Introduction to Jupyter
+    title: Jupyter Notebooks and JupyterHub
   - file: technology/r-datahub
-    title: R DataHub
+    title: R Hub
   - file: technology/shiny
     title: Shiny  
   - file: technology/quarto
     title: Quarto
   - file: workflow/use-retrolab
-    title: Launch Jupyter notebooks using RetroLab interface
+    title: RetroLab
   - file: workflow/use-realtimecollaboration
-    title: Use Real Time Collaboration (RTC)
+    title: Real Time Collaboration (RTC)
   - file: workflow/use-realtimefilesharing
-    title: Use Real Time File Sharing
+    title: Real Time File Sharing
   - file: workflow/launch-vscode.md
-    title: Launch VSCode Editor
+    title: VSCode
   - file: technology/pedagogy-and-technology/options-launch-rkernel
-    title: Options to Launch R Kernel
+    title: Options to Launch R
 - caption: Workflow Basics
   chapters:
   - file: workflow/creating-notebooks
-    title: Creating Assignments
+    title: Create Assignments
   - file: technology/pedagogy-and-technology/notebook-zero
     title: Notebook Zero
   - file: workflow/accessibility
@@ -57,37 +57,37 @@ parts:
   - file: workflow/download_notebook_as_pdf.md
     title: Download Jupyter Notebook as PDF
   - file: workflow/download-archive.md
-    title: Download Files/Folders as archive from Datahub
+    title: Download Datahub home directory contents as archive
   - file: workflow/performance_issue.md
     title: Best Practices to Avoid Performance Issues
   - file: workflow/securely-push-github
     title: Securely Push Changes to Github
-  - file: faq/share
-    title: Sharing information about the Hub
   - file: technology/using-ai-llm
-    title: Use Generative AI in Datahub
+    title: Generative AI Usecase in DataHub
   - file: technology/jupyter/large-datasets
-    title: Working with Large Datasets
+    title: Work with Large Datasets
   - file: workflow/bestpractices
     title: Instructional Design Best Practices for Creating Jupyter Notebooks
+  - file: faq/share
+    title: Sharing information about the Hub
 - caption: Datahub Frequently Asked Questions (FAQ)
   chapters:  
   - file: faq/onboarding
-    title: Onboarding new users into the Hub
+    title: Onboard new users
   - file: technology/jupyter/install-packages
-    title: Using Packages in Jupyter Hub
+    title: Package Installations
   - file: faq/admin
-    title: Explore elevated privileges in DataHub
+    title: Elevated privileges
   - file: faq/changerequirements
-    title: Changing existing requirements in the Hub
+    title: Change existing requirements in the Hub
   - file: faq/troubleshoot
-    title: Troubleshooting issues in the JupyterHub service
+    title: Troubleshoot issues in DataHub
   - file: technology/jupyter/troubleshoot-nbgitpuller
-    title: Troubleshooting issues in the nbgitpuller service
+    title: Troubleshoot issues in Link Generator
   - file: technology/jupyter/python-errors
-    title: Troubleshooting issues in the Python Code
+    title: Troubleshoot issues in the Python Code
   - file: faq/features
-    title: Requesting new features in the Hub
+    title: Request new features in the Hub
 - caption: Resources for Students
   chapters:
   - file: technology/resources

intro.md

@@ -2,17 +2,23 @@
 
 ## What is this guide and who is it for?
 
-The information in the guide is primarily intended for instructors who either currently are or will be teaching a course using the campus Jupyterhub.
+The information in the guide is primarily intended for 
+
+a) Instructors who either currently are or will be teaching a course using the DataHub (Campus JupyterHub) and
+b) Students who are enrolled in courses using DataHub and want to learn about the tools/resources available for them.
 
 ## How should I use this guide?
 
 The information is divided up into six sections:
-1. **Getting Started With the Campus JupyterHub**: Guide to set up campus Jupyterhub and troubleshoot relevant issues that arise during this set up.
-2. **Workflow Basics**: Helpful information around creating, distributing, and grading assignments 
-3. **Getting Started With the Modules and Connectors**: An overview of the different Data Science Education course types
-4. **Creating a Connector**: A guide to the pre-course set-up for a Connector course
-5. **Making a Module**: A guide to the pre-course set-up for a Module
-6. **Reference**: Useful people, terms, and contact information to know.
+1. **Getting Started With the DataHub tools**: Guide to familiarize you with the DataHub platform and its essential tools.
+2. **Workflow Basics**: This section covers information around creating, distributing, and grading assignments.
+3. **Datahub Frequently Asked Questions (FAQ)**: Comprehensive FAQ section that addresses common questions and issues encountered by DataHub users.
+4. **Resources for Students**: Curated resources tailored for students enrolled in DataScience courses.
+5. **Getting Started With the Modules and Connectors**: An overview of the different Data Science Education course types.
+6. **Creating a Connector**: A guide to the pre-course set-up for a Connector course.
+7. **Making a Module**: A guide to the pre-course set-up for a Module.
+8. **Workshop Resources**: This section contains resources for facilitating outreach workshops.
+9. **Reference**: Useful people, terms, and contact information to know.
 
 The first time you read through the material, you might go through the topics in order. After the first read, you could refer back to specific sections when seeking answers to questions.