From 838fc4fc92afbc8fb9b68ce87071aca8f5651b8e Mon Sep 17 00:00:00 2001
From: Breda McColgan <93921684+bredamc@users.noreply.github.com>
Date: Thu, 17 Oct 2024 14:51:29 +0100
Subject: [PATCH] ENG-11303: Updates based on redesigned Projects page (#55)

---
 .../ROOT/pages/setting-up-your-data-science-project.adoc      | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/workshop/docs/modules/ROOT/pages/setting-up-your-data-science-project.adoc b/workshop/docs/modules/ROOT/pages/setting-up-your-data-science-project.adoc
index adda737..c6a13eb 100644
--- a/workshop/docs/modules/ROOT/pages/setting-up-your-data-science-project.adoc
+++ b/workshop/docs/modules/ROOT/pages/setting-up-your-data-science-project.adoc
@@ -9,9 +9,9 @@ Before you begin, make sure that you are logged in to *{productname-long}*.
 +
 image::projects/launch-jupyter-link.png[Launch Jupyter link]
 +
-Note that it is possible to start a Jupyter notebook by clicking the *Launch Jupyter* link. However, it would be a one-off Jupyter notebook run in isolation. To implement a data science workflow, you must create a data science project (as described in the following procedure). Projects allow you and your team to organize and collaborate on resources within separated namespaces. From a project you can create multiple workbenches, each with their own IDE environment (for example, JupyterLab), and each with their own data connections and cluster storage. In addition, the workbenches can share models and data with pipelines and model servers.
+Note that it is possible to start a Jupyter notebook by clicking the *Launch standalone notebook server* link, selecting a notebook image, and clicking *Start server*. However, it would be a one-off Jupyter notebook run in isolation. To implement a data science workflow, you must create a data science project (as described in the following procedure). Projects allow you and your team to organize and collaborate on resources within separated namespaces. From a project you can create multiple workbenches, each with their own IDE environment (for example, JupyterLab), and each with their own data connections and cluster storage. In addition, the workbenches can share models and data with pipelines and model servers.
 
-. If you are using your own OpenShift cluster, click *Create data science project*. 
+. If you are using your own OpenShift cluster, click *Create project*. 
 +
 NOTE: If you are using the {org-name} Developer Sandbox, you are provided with a default data science project (for example, `myname-dev`). Select it and skip over the next step to the *Verification* section.