Parallel visual-type construction
Align the type of data (numeric, categorical, string, other) to a visualization type designed for that use-case.
K–2 Competencies
Explore hands-on activities with students, helping them understand the difference between data types. e.g., bar graphs for numerical data such as votes of favorite fruit, picture graph for categorical data such as favorite colors
Classroom resources
Data Science Starter Kit Module 5: Telling the Story - Visualization and Communication
Welcome to the culminating skill of data science—communicating your findings effectively so others can understand and act on them! This module focuses on how to create clear visualizations and compelling narratives that make data accessible and meaningful to different audiences.🔗
Visualization and Communication isn’t about creating fancy graphics or impressive presentations. It’s about developing the empathy and clarity to think, “How can I help others understand what this data means and why it matters to them?” The best data science in the world is useless if it can’t be understood and applied by the people who need it.
3–5 Competencies
Visualize multiple types of data (e.g., numeric, categorical, string data) during in-class data collection exercises.
Classroom resources
Data Science Starter Kit Module 5: Telling the Story - Visualization and Communication
Welcome to the culminating skill of data science—communicating your findings effectively so others can understand and act on them! This module focuses on how to create clear visualizations and compelling narratives that make data accessible and meaningful to different audiences.🔗
Visualization and Communication isn’t about creating fancy graphics or impressive presentations. It’s about developing the empathy and clarity to think, “How can I help others understand what this data means and why it matters to them?” The best data science in the world is useless if it can’t be understood and applied by the people who need it.
6–8 Competencies
Describe and discuss the typical visualization characteristics of numeric, categorical, and string data while identifying and outlining the differences between them.
Classroom resources
Data Science Starter Kit Module 5: Telling the Story - Visualization and Communication
Welcome to the culminating skill of data science—communicating your findings effectively so others can understand and act on them! This module focuses on how to create clear visualizations and compelling narratives that make data accessible and meaningful to different audiences.🔗
Visualization and Communication isn’t about creating fancy graphics or impressive presentations. It’s about developing the empathy and clarity to think, “How can I help others understand what this data means and why it matters to them?” The best data science in the world is useless if it can’t be understood and applied by the people who need it.
9–10 Competencies
Demonstrate the wrong type of data (e.g., numeric, categorical, string) entered into a misaligned visualization package (e.g., scatterplot of categorical data) and explain why the visualization fails to work or clearly represent the data.
Classroom resources
Data Science Starter Kit Module 5: Telling the Story - Visualization and Communication
Welcome to the culminating skill of data science—communicating your findings effectively so others can understand and act on them! This module focuses on how to create clear visualizations and compelling narratives that make data accessible and meaningful to different audiences.🔗
Visualization and Communication isn’t about creating fancy graphics or impressive presentations. It’s about developing the empathy and clarity to think, “How can I help others understand what this data means and why it matters to them?” The best data science in the world is useless if it can’t be understood and applied by the people who need it.
11–12 Competencies
Produce a data visualization parallel to the type of data (e.g., numeric, categorical, string, image, unstructured).
Defend your visualization choice to others and explain the data type and visualization type including suitability for continuous or discrete variables.
Classroom resources
Too Old to Mango? by DataClassroom
The purpose of this lesson is to help students master professional-level data visualization and communication skills by analyzing agricultural research data on mango tree productivity across different ages. Students will create publication-quality visualizations, conduct statistical analysis, and communicate findings to diverse stakeholders in ways that can influence agricultural policy, economic decisions, and global food security strategies.
Data Science Starter Kit Module 5: Telling the Story - Visualization and Communication
Welcome to the culminating skill of data science—communicating your findings effectively so others can understand and act on them! This module focuses on how to create clear visualizations and compelling narratives that make data accessible and meaningful to different audiences.🔗
Visualization and Communication isn’t about creating fancy graphics or impressive presentations. It’s about developing the empathy and clarity to think, “How can I help others understand what this data means and why it matters to them?” The best data science in the world is useless if it can’t be understood and applied by the people who need it.
Advanced Competencies
Classroom resources
Support other teachers by sharing a resource
Do you have a lesson plan, video, or tip that could help others teaching this topic?
Developed by our coalition
.png){kind=logo}
Coalition organizers
Share feedback on the Learning Progressions
Your feedback helps us improve these progressions for teachers around the world. Thank you!
Share feedback on the Learning Progressions
Your feedback helps us improve these progressions for teachers around the world. Thank you!
Share a classroom resource
Suggesting a resource helps students around the world learn essential data science skills.