in the middle of editing

Author: Goli-SF

config.yaml

@@ -73,9 +73,11 @@ episodes:
 
 # Information for Learners
 learners: 
+- setup.md
 
 # Information for Instructors
 instructors: 
+- instructor-notes.md
 
 # Learner Profiles
 profiles: 

episodes/01_introduction.md

@@ -1,11 +1,9 @@
 ---
 title: "Introduction"
 teaching: 10
-exercises: 2
+exercises: 0
 ---
 
-<span style="color: red;">Update teaching time and number of exercises.</span>
-
 :::::::::::::::::::::::::::::::::::::: questions 
 
 - How can the humanities benefit from data visualization?
@@ -17,45 +15,54 @@ exercises: 2
 
 ::::::::::::::::::::::::::::::::::::: objectives
 
-- Introduce the use cases of data visualization for the humanities
-- Introduce the concept of statistical inference to humanities researchers
-- Teach humanities researchers to visualize data with python to infer information from it
-- Teach humanities researchers to use data visualization and statistical inference for data storytelling
+- Introduce the use cases of data visualization for the humanities.
+- Introduce the concept of statistical inference to humanities researchers.
+- Teach humanities researchers to visualize data with python to infer information from it.
+- Teach humanities researchers to use data visualization and statistical inference for data storytelling.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
-### Who can benefit from this lesson?
-
-<u>The main goal of this lesson is to demonstrate the importance of data visualization and how it can unlock a variety of learning and research pathways—ranging from exploratory data analysis and statistical inference to understanding machine learning processes and data storytelling.</u>
+:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: instructor
+To warm up, conduct a brief brainstorming session to elicit potential answers to the 
+questions above. Write the answers on the board and engage learners in a discussion about their background knowledge 
+in each area.
 
-If you're looking for ways to approximately predict specific values based on a given dataset for data storytelling, or if you've ever wondered how machine learning models that predict values (rather than categories) work, this lesson is for you. It will introduce you to the concept of statistical inference—a mathematical calculation used in predictive machine learning algorithms—through various data visualization techniques. These visualization methods will also enhance your data storytelling skills, not only in describing existing data but also in predicting values based on the available data. 
+Depending on the backgrounds and interest areas of workshop attendees, you can focus on one or the other point
+from the text below. 
+::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
 
-Data visualization is central to this lesson, serving as both the means and the goal. You’ll not only learn to write Python code and engage in hands-on data visualization, but also discover how to explore, understand, and predict dataset values through visualization techniques.
+### Who can benefit from this lesson?
 
-### How is this lesson structured?
+The main goal of this lesson is to demonstrate the importance of data visualization and how it can unlock a 
+variety of learning and research pathways—ranging from exploratory data analysis and statistical inference to 
+understanding machine learning processes and data storytelling.
 
-* The lesson begins with a brief overview of various graph types and their applications.
-* Next, you'll explore statistical inference and linear regression, which will help you understand correlations and make predictions based on datasets. These concepts also provide foundational insights into how machine learning models work. 
-* Finally, you'll learn how to use visualization techniques to identify patterns within a dataset and extract statistical insights, bringing together the concepts from the previous sections and engaging in hands-on data visualization in Python.  
+If you're looking for ways to approximately predict specific values based on a given dataset for data storytelling, 
+or if you've ever wondered how machine learning models that predict values (rather than categories) work, 
+this lesson is for you. It will introduce you to the concept of statistical inference—a mathematical 
+calculation used in predictive machine learning algorithms—through various data visualization techniques. 
+These visualization methods will also enhance your data storytelling skills, not only in describing existing 
+data but also in predicting values based on the available data. 
 
-### What background knowledge do you need for this lesson?
+Data visualization is central to this lesson, serving as both the means and the goal. You’ll not only learn 
+to write Python code and engage in hands-on data visualization, but also discover how to explore, understand, 
+and predict dataset values through visualization techniques.
 
-1. Basic acquaintance with Python
-2. Basic mathematical background
-3. Curiosity to learn more about Python programming, statistics and data storytelling
 
-:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: instructor
+### How is this lesson structured?
 
-Use the text above as a warm-up activity. Conduct a brief brainstorming session to elicit potential answers to the 
-questions above. Write the answers on the board and engage learners in a discussion about their background knowledge 
-in each area.
+* The lesson begins with a brief overview of various graph types and their applications.
+* Next, you'll explore statistical inference and linear regression, which will help you understand correlations 
+and make predictions based on datasets. These concepts also provide foundational insights into how machine 
+learning models work. 
+* Finally, you'll learn how to use visualization techniques to identify patterns within a dataset and extract 
+statistical insights, bringing together the concepts from the previous sections and engaging in hands-on 
+data visualization in Python.  
 
-::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
 
 ::::::::::::::::::::::::::::::::::::: keypoints 
-
-- An overview of the lesson structure and objectives
-
+- Getting to know each other.
+- An overview of the lesson structure and objectives.
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
 

episodes/02_graph_categories.md

@@ -1,10 +1,9 @@
 ---
 title: 'Graph Categories'
-teaching: 10
-exercises: 2
+teaching: 15
+exercises: 0
 ---
 
-<span style="color: red;">Update teaching time and number of exercises.</span>
 
 :::::::::::::::::::::::::::::::::::::: questions 
 
@@ -20,6 +19,14 @@ exercises: 2
 
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
+:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: instructor
+This episode is more meant for self study. You don't need to go into extensive detail about the content of this 
+episode. Instead, focus on reviewing the graphs with the learners and ask if they are already familiar with 
+them and their use cases. The most important graphs to highlight—those that will also be featured in the 
+visualization section of this lesson—are scatter plots, bubble charts, and correlograms. Place greater emphasis 
+on these and prepare the learners to create them in the visualization section. 
+::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
 ::::::::::::::::::::::::::::::::::::: challenge 
 
 ## Question
@@ -34,23 +41,14 @@ Data visualization has multiple purposes. It can help you understand trends and
 It can also help you introduce a dataset to others in scientific texts or in data storytelling.
 
 :::::::::::::::::::::::::::::::::
+
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
 Most graphs used for data visualization fall into one of the following four general categories, based on their function. 
 In this lesson, we won’t cover how to create all of these graphs in Python, but will focus on a few that are 
 useful for statistical inference and data storytelling with our specific dataset. However, 
 it's helpful to know the names of these graphs and understand the contexts in which they can be applied.
 
-:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: instructor
-
-This episode is more meant for self study. You don't need to go into extensive detail about the content of this episode. Instead, focus on reviewing the graphs 
-with the learners and ask if they are already familiar with them and their use cases. The most important graphs to 
-highlight—those that will also be featured in the visualization section of this lesson—are scatter plots, 
-bubble charts, and correlograms. Place greater emphasis on these and prepare the learners to create them in the 
-visualization section. 
-
-::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
-
 ## 2.1. Explore Relationships between two or more Features
 
 **Scatter Plot:** "A scatter plot (aka scatter chart, scatter graph) uses dots to represent values for two different 

learners/setup.md

@@ -2,32 +2,49 @@
 title: Setup
 ---
 
-This lesson teaches you how to visualize data using Python. 
+In this lesson, you'll explore the different types of graphs and 
+their use cases. You'll then dive into the concept of statistical 
+inference. Next, you'll get hands-on with Python coding to analyze 
+the happiness and income dataset provided below. Finally, you'll 
+use the graphs you've created to make informed estimates about 
+countries not included in the dataset.
+
+:::::::::::::::: callout
+### What background knowledge do you need for this lesson?
+
+1. Basic acquaintance with Python
+2. Basic mathematical background
+3. Curiosity to learn more about Python programming, statistics and data storytelling
+::::::::::::::::::
 
 ## Dataset
 
+The dataset we are working with in this lesson originates from [Kaggle](https://www.kaggle.com/datasets/levyedgar44/income-and-happiness-correction). 
 If you wish to save the the dataset on your computer, go ahead and download the 
 [Income and Happiness Correlation dataset](data/income_happiness_correlation.csv) and save it to your working directory. 
-Otherwise, you can directly load it into your code using the following link:
+Otherwise, you can directly load it into your code later using the following link:
 
 ```
 https://raw.githubusercontent.com/Goli-SF/stat_inf_data_vis/tree/main/episodes/data/income_happiness_correlation.csv
 ```
 
-<span style="color: red;">The above link should be updated later.</span> 
+<span style="color: red;">The above link should be updated later, when the lesson is pushed to the incubator.</span> 
 
-The dataset originates from [Kaggle](https://www.kaggle.com/datasets/levyedgar44/income-and-happiness-correction).
 
 ## Software Setup
 
 ::::::::::::::::::::::::::::::::::::::: discussion
 
 ### Python and Jupyter Notebook/Google Colab
 
-To do the exercises in this lesson, you need an IDE (Integrated Development Environment). We recommend you use Jupyter Notebook or an equivalent 
-(such as [Google Colab](https://colab.google/)). 
+To do the exercises in this lesson, you need an IDE (Integrated Development Environment). We recommend you use 
+Jupyter Notebook or cloud-based equivalent such as [Google Colab](https://colab.google/). 
+
+If you're using Google Colab, you don't need any installation. Just create a Google account - if you don't have one
+already -, create a new Colab notebook and start coding. 
 
-To install Jupyter Notebook and Python on your computer together, we recommend using [Anaconda](https://www.anaconda.com/download/success)
+Otherwise, to install Jupyter Notebook and Python on your computer together, we recommend using 
+[Anaconda](https://www.anaconda.com/download/success).
 To do so, click on your operating system from the list below and follow the instructions. 
 
 :::::::::::::::::::::::::::::::::::::::::::::::::::
@@ -68,12 +85,12 @@ This [video tutorial](https://www.youtube.com/watch?v=TcSAln46u9U) can help you
 2. Download the Anaconda Installer with Python 3 for Linux.
 (The installation requires using the shell. If you aren't comfortable doing the installation yourself stop here and request help at the workshop.)
 3. Open a terminal window and navigate to the directory where the executable is downloaded (e.g., `cd ~/Downloads`).
-4. Type
-```bash Anaconda3-```
-and then press `Tab` to autocomplete the full file name. The name of file you just downloaded should appear.
-5. Press <kbd>Enter</kbd> (or <kbd>Return</kbd> depending on your keyboard). You will follow the text-only prompts. To move through the text, 
-press <kbd>Spacebar</kbd>. Type `yes` and press enter to approve the license. Press <kbd>Enter</kbd> (or <kbd>Return</kbd>) to approve the default location 
-for the files. Type `yes` and press <kbd>Enter</kbd> (or <kbd>Return</kbd>) to prepend Anaconda to your `PATH` (this makes the Anaconda distribution the default Python).
+4. Type `bash Anaconda3-` and then press <kbd>Tab</kbd> to autocomplete the full file name. The name of file you just 
+downloaded should appear.
+5. Press <kbd>Enter</kbd> (or <kbd>Return</kbd> depending on your keyboard). You will follow the text-only prompts. 
+To move through the text, press <kbd>Spacebar</kbd>. Type `yes` and press <kbd>Enter</kbd> (or <kbd>Return</kbd>) 
+to approve the license. Press <kbd>Enter</kbd> (or <kbd>Return</kbd>) to approve the default location for the files. 
+Type `yes` and press <kbd>Enter</kbd> (or <kbd>Return</kbd>) to prepend Anaconda to your `PATH` (this makes the Anaconda distribution the default Python).
 6. Close the terminal window.
 
 ::::::::::::::::::::::::