no more duration

Author: corydonbaylor

modules/snowflake-analytics/pages/neo4j-fraud.adoc

@@ -36,7 +36,9 @@ Analytics] for Snowflake
 with appropriate access to databases and schemas.
 * Neo4j Graph Analytics application installed from the Snowflake
 marketplace. Access the marketplace via the menu bar on the left hand
-side of your screen, as seen below: image:marketplace.png[]
+side of your screen, as seen below: 
+
+image:marketplace.png[]
 
 ==== What You Will Build
 
@@ -53,7 +55,7 @@ networks
 
 Dataset overview : This dataset is modelled to design and analyze a peer
 to peer transaction network to identify fraudulent activity using graph
-analytics. image:datamodel.png[]
+analytics. 
 
 Let’s name our database `P2P++_++DEMO`. Using the CSVs found
 https://drive.google.com/drive/u/1/folders/1BnAnRSEfuwDvc4eQH8IRvy3tUkwOeaNf[here],

modules/snowflake-analytics/pages/neo4j-patient-journey.adoc

@@ -39,8 +39,6 @@ image:marketplace.png[]
 
 === Load The Data
 
-Duration: 5
-
 Dataset overview : This dataset is modelled to design and analyze
 patients and different procedures that they undergo using graph
 analytics.
@@ -58,8 +56,6 @@ in your data.
 
 === Set Up
 
-Duration: 5
-
 ==== Import The Notebook
 
 * We’ve provided a notebook to walk you through each SQL and Python
@@ -130,8 +126,6 @@ USE ROLE gds_user_role;
 
 === Clean Our Data
 
-Duration: 5
-
 We need our data to be in a particular format in order to work with
 Graph Analytics. In general it should be like so:
 
@@ -325,8 +319,6 @@ image::graph.png[]
 
 === Find Similar Patients
 
-Duration: 10
-
 Now we are finally at the step where we create a projection, run our
 algorithms, and write back to snowflake. We will run louvain to
 determine communities within our data. Louvain identifies communities by
@@ -481,8 +473,6 @@ select * from neo4j_patient_db.public.patient_community
 
 === Conclusions And Resources
 
-Duration: 2
-
 In this quickstart, you learned how to bring the power of graph insights
 into Snowflake using Neo4j Graph Analytics.