apply corrections suggested by colleagues

README.md

@@ -25,7 +25,7 @@ in Research, Data, and Methods. HERMES is funded by the
 through grants from the European Union.
 
 # Verion
-V 2025.01.31
+V2025.01.31
 
 # License
 This lesson has a [CC-BY license](LICENSE.md).

episodes/01_introduction.md

@@ -14,11 +14,12 @@ exercises: 0
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
 ::::::::::::::::::::::::::::::::::::: objectives
+After completing this lesson, learners will be able to ...
 
-- 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.
+- Understand the use cases of data visualization for the humanities.
+- Understand the concept of statistical inference to humanities researchers.
+- Visualize data with python to infer information from it.
+- Use data visualization and statistical inference for data storytelling.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::
 

episodes/02_graph_categories.md

@@ -14,8 +14,8 @@ exercises: 0
 
 ::::::::::::::::::::::::::::::::::::: objectives
 
-- Discuss the benefits of data visualization in humanities research.
-- Explore the most effective graph types for data visualization in the humanities.
+- Learn about the benefits of data visualization in humanities research.
+- Learn some of the most effective graph types for data visualization in the humanities.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::
 

episodes/03_statistical_inference.md

@@ -14,9 +14,9 @@ exercises: 0
 
 ::::::::::::::::::::::::::::::::::::: objectives
 
-- Explain the mathematical concept of statistical inference to humanities students and researchers.
-- Explain the difference between descriptive and inferential statistics, correlation and causation. 
-- Explain the meaning of regression. 
+- Understand the mathematical concept of statistical inference.
+- Understand the difference between descriptive and inferential statistics, correlation and causation. 
+- Understand the meaning of regression. 
 
 ::::::::::::::::::::::::::::::::::::::::::::::::
 

episodes/04_python_data_vis_for_inference_and_storytelling.md

@@ -16,11 +16,11 @@ exercises: 15
 
 ::::::::::::::::::::::::::::::::::::: objectives
 
-- Creating scatter plots, bubble charts and correlograms in Python, using the Seaborn library.
-- Implementing data visualization for exploratory analysis of a concrete dataset and telling a story 
+- Create scatter plots, bubble charts and correlograms in Python, using the Seaborn library.
+- Implement data visualization for exploratory analysis of a concrete dataset and tell a story 
 based on the trends that it reveals. 
-- Using data visualization to infer information from a concrete dataset. 
-- Reflecting on the use cases of data visualization in humanities research. 
+- Use data visualization to infer information from a concrete dataset. 
+- Reflect on the use cases of data visualization in humanities research. 
 
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
@@ -59,14 +59,11 @@ Let’s answer these questions for our dataset by writing some code.
 The dataset we're working with is stored in a CSV (comma-separated values) file on GitHub. Let's load it into 
 our notebook and store it in a pandas DataFrame named `happy_df`: 
 
-<span style="color: red;">The url below should be updated later, when the lesson is pushed to the incubator.</span> 
-
 ```
 import pandas as pd
 
 # path to the dataset: 
-url= "https://raw.githubusercontent.com/HERMES-DKZ/data_challenges_data_carpentries/main/\
-data_carpentries/statistical_inferece_data_visualization/data_statistical_inference_data_visualization/income_happiness_correlation.csv"
+url= "https://raw.githubusercontent.com/HERMES-DKZ/stat_inf_data_vis/main/episodes/data/income_happiness_correlation.csv"
 
 # loading the dataset and storing it in a pandas DataFrame:
 happy_df= pd.read_csv(url)

index.md

@@ -2,8 +2,9 @@
 site: sandpaper::sandpaper_site
 ---
 
-This is a new lesson built with [The Carpentries Workbench][workbench]. 
-
-
-[workbench]: https://carpentries.github.io/sandpaper-docs
-
+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.

learners/reference.md

@@ -4,5 +4,30 @@ title: 'Reference'
 
 ## Glossary
 
-This is a placeholder file. Please add content here. 
+**Graph**
+A graph is a visual representation of data. It's like a picture that shows how different pieces of information are 
+related to each other. You can think of it like a map: just as a map helps you see where places are in relation to 
+each other, a graph shows how different data points connect and how they are related to each other. Graphs are the 
+products of data visualization. They can help you understand the data better by seeing trends and relations in it, 
+introduce it to others and draw conclusions from it. 
+
+**Data Visualization**
+Data visualization is the broader practice of using graphs, charts, maps, and other visual tools to represent data. 
+It’s all about turning raw data (numbers, facts, figures) into images that can communicate insights quickly and clearly. 
+For example, instead of reading through pages of numbers, a well-designed chart can tell you the story behind those 
+numbers, making it easier to understand patterns, trends, and relationships.
+
+**Data Storytelling*:** 
+Data storytelling is the art of combining data with a narrative. It's about presenting data not just as isolated 
+facts, but in a way that tells a compelling story. It’s like writing a story, but instead of using words, you use data. 
+The goal is to make the data more engaging and understandable for an audience by providing context, explaining trends, 
+and helping people see the bigger picture. Good data storytelling helps people grasp what the data means and why it 
+matters.
+
+**Statistics** 
+Statistics is the science of collecting, analyzing, and interpreting data. It involves methods for understanding and 
+making sense of data, including calculating averages, percentages, trends, and variations. While data visualization 
+and storytelling help communicate findings, statistics provides the tools to understand and measure the data itself. 
+For example, if you want to know how typical or unusual a certain data point is, you would use statistical techniques 
+to analyze it.
 

learners/setup.md

@@ -2,18 +2,13 @@
 title: Setup
 ---
 
-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
+1. Basic acquaintance with Python: you should know how to import Python packages and load data into your code.
+You also need basic familiarity with Python syntax. 
+2. Basic mathematical background: you need a basic understanding of statistics and probabilities. 
 3. Curiosity to learn more about Python programming, statistics and data storytelling
 ::::::::::::::::::
 
@@ -25,12 +20,9 @@ If you wish to save the the dataset on your computer, go ahead and download the
 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
+https://raw.githubusercontent.com/HERMES-DKZ/stat_inf_data_vis/main/episodes/data/income_happiness_correlation.csv
 ```
 
-<span style="color: red;">The above link should be updated later, when the lesson is pushed to the incubator.</span> 
-
-
 ## Software Setup
 
 ::::::::::::::::::::::::::::::::::::::: discussion

links.md

@@ -3,8 +3,4 @@ Place links that you need to refer to multiple times across pages here. Delete
 any links that you are not going to use. 
  -->
 
-[pandoc]: https://pandoc.org/MANUAL.html
-[r-markdown]: https://rmarkdown.rstudio.com/
-[rstudio]: https://www.rstudio.com/
-[carpentries-workbench]: https://carpentries.github.io/sandpaper-docs/