Improve standalone notebook

Author: Mats-SX

examples/graph-analytics-serverless-standalone.ipynb

@@ -28,9 +28,9 @@
    "source": [
     "This Jupyter notebook is hosted [here](https://github.com/neo4j/graph-data-science-client/blob/main/examples/graph-analytics-serverless-standalone.ipynb) in the Neo4j Graph Data Science Client Github repository.\n",
     "\n",
-    "The notebook shows how to use the `graphdatascience` Python library to create, manage, and use a GDS Session.\n",
+    "The notebook shows how to use the `graphdatascience` Python library to create, manage, and use an Aura Graph Analytics (AGA) Session.\n",
     "\n",
-    "We consider a graph of people and fruits, which we're using as a simple example to show how to load data from Pandas `DataFrame` to a GDS Session, run algorithms, and inspect the results. \n",
+    "We consider a graph of people and fruits, which we're using as a simple example to show how to load data from Pandas `DataFrame` to an AGA Session, run algorithms, and inspect the results.\n",
     "We will cover all management operations: creation, listing, and deletion.\n",
     "\n",
     "If you are using AuraDB, follow [this example](../graph-analytics-serverless).\n",
@@ -194,12 +194,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Adding a dataset\n",
+    "## Projecting a dataset\n",
     "\n",
-    "We assume that the configured Neo4j database instance is empty.\n",
-    "We will add our dataset using standard Cypher.\n",
+    "AGA sessions always start empty, with no data.\n",
+    "So our first step will be to project data into the session.\n",
+    "In this example, we will illustrate how to do this using Pandas DataFrames.\n",
     "\n",
-    "In a more realistic scenario, this step is already done, and we would just connect to the existing database."
+    "Many systems offer ways to read data into Pandas DataFrames, enabling these systems to be used as data sources for AGA.\n",
+    "For simplicity, we will define the DataFrames used in this notebook by hand."
    ]
   },
   {
@@ -249,8 +251,11 @@
    "source": [
     "## Construct Graph from DataFrames\n",
     "\n",
-    "Now that we have imported a graph to our database, we create graphs directly from pandas `DataFrame` objects.\n",
-    "We do that by using the `gds.graph.construct()` method."
+    "With DataFrames in hand, the next step is to build a graph from them.\n",
+    "We do that by using the `gds.graph.construct()` function.\n",
+    "\n",
+    "After calling this function, we will get a Graph Object back, representing the graph that now exists within the AGA session.\n",
+    "We will use it as input to the various algorithms that we will run on the graph."
    ]
   },
   {