diff --git a/README.md b/README.md index e312ce9..c16ae29 100644 --- a/README.md +++ b/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). diff --git a/episodes/01_introduction.md b/episodes/01_introduction.md index e76b0ab..f97d370 100644 --- a/episodes/01_introduction.md +++ b/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. :::::::::::::::::::::::::::::::::::::::::::::::: diff --git a/episodes/02_graph_categories.md b/episodes/02_graph_categories.md index 694ad72..576f029 100644 --- a/episodes/02_graph_categories.md +++ b/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. :::::::::::::::::::::::::::::::::::::::::::::::: diff --git a/episodes/03_statistical_inference.md b/episodes/03_statistical_inference.md index 943ace1..2f310e3 100644 --- a/episodes/03_statistical_inference.md +++ b/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. :::::::::::::::::::::::::::::::::::::::::::::::: diff --git a/episodes/04_python_data_vis_for_inference_and_storytelling.md b/episodes/04_python_data_vis_for_inference_and_storytelling.md index 9c7bfda..7a1784e 100644 --- a/episodes/04_python_data_vis_for_inference_and_storytelling.md +++ b/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`: -The url below should be updated later, when the lesson is pushed to the incubator. - ``` 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) diff --git a/index.md b/index.md index af66276..fc5458f 100644 --- a/index.md +++ b/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. diff --git a/learners/reference.md b/learners/reference.md index ba26b9f..11eca1c 100644 --- a/learners/reference.md +++ b/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. diff --git a/learners/setup.md b/learners/setup.md index dc4b5fc..4c000e9 100644 --- a/learners/setup.md +++ b/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 ``` -The above link should be updated later, when the lesson is pushed to the incubator. - - ## Software Setup ::::::::::::::::::::::::::::::::::::::: discussion diff --git a/links.md b/links.md index 4c5cd2f..72a3384 100644 --- a/links.md +++ b/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/