improvements

docs/tutorials/complete_applications/scenario_management_overview/index.md

@@ -9,10 +9,6 @@ create dataflows (pipelines), manage and record their executions, and access the
 It is designed to be used by end-users in a multi-user context and gets especially useful 
 for Machine Learning or Mathematical optimization.
 
-# Objective of the Application
-
-TODO add a description of the application we are building
-
 # Before we begin
 
 Only Taipy has to be installed. **Taipy** package requires Python 3.8 or newer;
@@ -38,12 +34,10 @@ can be done easily and quickly with drag and drop.
 
 So, without further delay, let's begin to code!
 
-# Steps
+# Concepts
 
 1. [Configuration](step_01/step_01.md)
 
-2. [Basic functions](step_02/step_02.md)
-
 3. [Data Node types](step_03/step_03.md)
 
 4. [Cycles](step_04/step_04.md)

docs/tutorials/complete_applications/scenario_management_overview/step_01/step_01.md

@@ -24,10 +24,10 @@ understand the *Data node* and *Task* concepts.
   situations. Taipy provide the framework to execute various scenarios under different 
   situations (i.e. various data/parameters values set by end-users).
 
-- [**Configuration**](../../../../manuals/core/config/index.md) is a structure to define 
-  scenarios. It represents our Direct Acyclic Graph(s); it models the data sources and 
-  parameters, as well as tasks. Once defined, a configuration acts like a superclass; it is used 
-  to instantiate different scenario instances.
+[**Configuration**](../../../../manuals/core/config/index.md) is a structure to define scenarios.
+It represents our Direct Acyclic Graph(s); it models the data sources and parameters, as well as 
+tasks. Once defined, a configuration acts like a superclass; it is used to instantiate different 
+scenario instances.
 
 # Configuring a Scenario
 
@@ -59,7 +59,7 @@ Three Data Nodes are being configured (**historical_temperature**, **date_to_for
 
     === "Python configuration"
 
-        **Alternative 2:** Configuration using Python Code
+        **Alternative 1:** Configuration using Python Code
 
         Here is the code to configure a simple scenario.
 
@@ -81,7 +81,7 @@ Three Data Nodes are being configured (**historical_temperature**, **date_to_for
 
     === "Using Taipy Studio"
 
-        **Alternative 1:** Configuration using Taipy Studio
+        **Alternative 2:** Configuration using Taipy Studio
 
         By watching the animation below, you can see how this configuration gets created using 
         Taipy Studio. In fact, Taipy Studio is an editor of a TOML file specific to Taipy. It 
@@ -103,6 +103,8 @@ Three Data Nodes are being configured (**historical_temperature**, **date_to_for
 
 The configuration is done! Let's use it to instantiate scenarios and submit them.
 
+# Instantiate Scenario
+
 First, run the Core service in your code (`tp.Core().run()`). Then, you can play with Taipy: 
 
 - create scenarios,
@@ -140,7 +142,7 @@ Results:
 Value at the end of task 23.45
 ```    
 
-In this code, you can see how to create qnd submit scenarios, retrieve data nodes from, 
+In this code, you can see how to create and submit scenarios, retrieve data nodes, 
 read and write data. Many other functions are described in the manuals, in particular in the 
 [taipy](../../../../manuals/reference/pkg_taipy.md), 
 [scenario](https://docs.taipy.io/en/develop/manuals/core/entities/scenario-cycle-mgt/) and 

docs/tutorials/complete_applications/scenario_management_overview/step_03/step_03.md

@@ -1,5 +1,5 @@
 > You can download the code for
-<a href="./../src/step_03.py" download>Step 3</a> 
+<a href="./../src/step_03.py" download>Step 2</a> 
 or all the steps <a href="./../src/src.zip" download>here</a>. 
 
 # Data Node types

docs/tutorials/complete_applications/scenario_management_overview/step_06/step_06.md

@@ -6,9 +6,11 @@ or all the steps <a href="./../src/src.zip" download>here</a>.
 
 *Estimated Time for Completion: 15 minutes; Difficulty Level: Intermediate*
 
-Skipping tasks is a crucial capability of Taipy. If you run a function twice with the same inputs, you'll get the same output. Executing this type of function is inefficient and consumes unnecessary time and resources.
+Skipping tasks is a crucial capability of Taipy. If you run a function twice with the same inputs,
+you'll get the same output. Executing this type of function is inefficient and consumes unnecessary time and resources.
 
-Taipy offers the option to set the _skippable_ attribute for each task. If this attribute is set to True, Taipy Core’s scheduler automatically detects if changes have occurred on any of the input Data Nodes of a task. 
+Taipy offers the option to set the _skippable_ attribute for each task. If this attribute is set to
+True, Taipy Core’s scheduler automatically detects if changes have occurred on any of the input Data Nodes of a task. 
 If there are no changes, it automatically skips the execution of that task. By default, _skippable_ is set to False. 
 
 

docs/tutorials/complete_applications/scenario_management_overview/step_07/step_07.md

@@ -6,7 +6,8 @@ or all the steps <a href="./../src/src.zip" download>here</a>.
 
 *Estimated Time for Completion: 15 minutes; Difficulty Level: Advanced*
 
-Taipy has [different ways](../../../../manuals/core/config/job-config.md) to execute the code. Changing the execution mode can be useful for running multiple tasks in parallel.
+Taipy has [different ways](../../../../manuals/core/config/job-config.md) to execute the code.
+Changing the execution mode can be useful for running multiple tasks in parallel.
 
 - _standalone_ mode: asynchronous. Jobs can be run in parallel depending on the graph of execution (if _max_nb_of_workers_ > 1).