Data are produced by people
Recognize that data represent decisions about measurement and inclusion involving people who are and are not immediately present.
K–2 Competencies
Recognize the importance of asking questions about how data were collected.
Classroom resources
All About Our Class Activity
Students become data scientists by collecting information about their classmates and creating visual representations to learn about their classroom community.
Data Science Starter Kit - Module 1: Data Dispositions and Responsibilities
Welcome to the foundation of data science education! This module focuses on developing the right mindset for working with data—both for you and your students.🔗
Data Dispositions and Responsibilities isn’t about memorizing definitions or learning technical skills. It’s about cultivating curiosity, healthy skepticism, and ethical thinking. These are the habits of mind that make everything else in data science meaningful and responsible.
3–5 Competencies
Ask questions about how data are collected or considered.
Understand that data is generated by people who make decisions about what and how to measure.
Classroom resources
All About Our Class Activity
Students become data scientists by collecting information about their classmates and creating visual representations to learn about their classroom community.
Data Science Starter Kit - Module 1: Data Dispositions and Responsibilities
Welcome to the foundation of data science education! This module focuses on developing the right mindset for working with data—both for you and your students.🔗
Data Dispositions and Responsibilities isn’t about memorizing definitions or learning technical skills. It’s about cultivating curiosity, healthy skepticism, and ethical thinking. These are the habits of mind that make everything else in data science meaningful and responsible.
6–8 Competencies
Ask questions regarding the origins of specific automated measures (e.g., webtracking, email meta-data, user accounts).
Recognize the limits of the information the data can provide and the story it can tell.
Recognize that conclusions may need to be revised in the future as more knowledge and data become available.
Classroom resources
The Scrunchie Activity by DataClassroom
The purpose of this lesson is to introduce students to fundamental data science concepts through hands-on collection and visualization of personally meaningful data. Students will identify categorical variables in everyday objects, organize data into tidy format, and create multiple visualizations to answer questions about their own collections, building foundational skills for digital data analysis.
Backpack Investigation Activity
Students investigate the weight and contents of their backpacks using data collection, analysis, and health recommendations to make evidence-based conclusions about student wellness.
Data Science Starter Kit - Module 1: Data Dispositions and Responsibilities
Welcome to the foundation of data science education! This module focuses on developing the right mindset for working with data—both for you and your students.🔗
Data Dispositions and Responsibilities isn’t about memorizing definitions or learning technical skills. It’s about cultivating curiosity, healthy skepticism, and ethical thinking. These are the habits of mind that make everything else in data science meaningful and responsible.
9–10 Competencies
Explain how data-based decisions are revisited as new evidence or societal needs emerge (e.g., blood pressure cut-off numbers, dietary guidance, medical benchmarks).
Evaluate why data models require updates to maintain accuracy and relevance.
Classroom resources
Witches, Spiders, and Stranger Things in Data by DataClassroom
The purpose of this lesson is to help students make sense of data trends by analyzing Google search patterns for popular Halloween costumes. Students will interpret graphs to understand why certain costume searches spike around Halloween and other times, using data to predict future trends and make business recommendations.
The Taylor Swift Effect by DataClassroom
The purpose of this lesson is to help students investigate whether correlations exist between celebrity presence and athletic performance using real sports data. Students will analyze Kansas City Chiefs game statistics to determine if Taylor Swift's attendance impacts team and individual player performance, while learning about correlation vs. causation and the limitations of observational data.
School Schedule Analytics Activity
Students analyze real school data to understand enrollment patterns, resource allocation, and administrative decision-making while applying statistical analysis and data visualization techniques used in professional settings.
Data Science Starter Kit - Module 1: Data Dispositions and Responsibilities
Welcome to the foundation of data science education! This module focuses on developing the right mindset for working with data—both for you and your students.🔗
Data Dispositions and Responsibilities isn’t about memorizing definitions or learning technical skills. It’s about cultivating curiosity, healthy skepticism, and ethical thinking. These are the habits of mind that make everything else in data science meaningful and responsible.
11–12 Competencies
Explore the origins of some standardized unit measurements (e.g., horsepower, mole, scores on AP exams).
Identify the risks and tradeoffs of using traditional measurements (e.g., IQ, BMI).
Classroom resources
School Schedule Analytics Activity
Students analyze real school data to understand enrollment patterns, resource allocation, and administrative decision-making while applying statistical analysis and data visualization techniques used in professional settings.
Data Science Starter Kit - Module 1: Data Dispositions and Responsibilities
Welcome to the foundation of data science education! This module focuses on developing the right mindset for working with data—both for you and your students.🔗
Data Dispositions and Responsibilities isn’t about memorizing definitions or learning technical skills. It’s about cultivating curiosity, healthy skepticism, and ethical thinking. These are the habits of mind that make everything else in data science meaningful and responsible.
Advanced Competencies
Classroom resources
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