Defining relationships
Organize, visualize, and analyze data to identify patterns, trends, and associations. Use statistical measures and graphs to interpret relationships and make predictions.
K–2 Competencies
Organize objects by size, color, shape, etc.
Use language like “goes with” “belongs to”, or “matches” to group items together.
Classroom resources
Data Science Day: Rainbow Data & Lucky Graphs
Students explore data collection and graphing through St. Patrick's Day themed activities involving rainbow colors, patterns, gold coin counting, and probability, learning that data can help us understand colors, patterns, and chance.
Data Science Starter Kit Module 3: Making Sense of Data - Analysis and Modeling
Welcome to the exciting part of data science—making sense of the information you’ve collected! This module focuses on how to analyze data to find patterns, understand what the numbers really mean, and start drawing conclusions.🔗
Analysis and Modeling isn’t about complex mathematics or advanced statistics. It’s about developing the thinking skills to look at data and ask, “What story is this telling me? What patterns do I notice? What questions does this raise?” Whether students are working with simple tally marks or sophisticated datasets, the fundamental thinking is the same.
3–5 Competencies
Create time-series graphs to determine change in variable over time
Use data collected through surveys or experiments (e.g., heights of fellow classmates) and use spreadsheets to visualize trends and relationships
Use no-code or low-code data science tools. e.g., CODAP, Desmos, Google sheets
Classroom resources
Data Science Day: Rainbow Data & Lucky Graphs
Students explore data collection and graphing through St. Patrick's Day themed activities involving rainbow colors, patterns, gold coin counting, and probability, learning that data can help us understand colors, patterns, and chance.
Data Science Starter Kit Module 3: Making Sense of Data - Analysis and Modeling
Welcome to the exciting part of data science—making sense of the information you’ve collected! This module focuses on how to analyze data to find patterns, understand what the numbers really mean, and start drawing conclusions.🔗
Analysis and Modeling isn’t about complex mathematics or advanced statistics. It’s about developing the thinking skills to look at data and ask, “What story is this telling me? What patterns do I notice? What questions does this raise?” Whether students are working with simple tally marks or sophisticated datasets, the fundamental thinking is the same.
6–8 Competencies
Employ complex graphs (e.g., bar graphs, line graphs) and basic statistical concepts (e.g., mean, median, mode) to describe patterns and identify trends, similarities, and differences within data.
Create scatterplots and add line of best fit.
Classroom resources
Data Science Day: Spring Break Travel Analytics
Students analyze spring break travel trends, popular destinations, budgeting for trips, and climate data while learning about survey design, comparative statistics, and practical applications of data analysis for travel planning - inclusive of students traveling, staying home, or visiting family.
Middle School Drought Analysis
The purpose of this lesson is to develop students' data literacy skills through analyzing real drought data from their state or region. Students will explore climate patterns, create visualizations, draw evidence-based conclusions, and communicate findings effectively while learning to evaluate data sources, identify patterns, and understand the ethical implications of data analysis.
Data Science Starter Kit Module 3: Making Sense of Data - Analysis and Modeling
Welcome to the exciting part of data science—making sense of the information you’ve collected! This module focuses on how to analyze data to find patterns, understand what the numbers really mean, and start drawing conclusions.🔗
Analysis and Modeling isn’t about complex mathematics or advanced statistics. It’s about developing the thinking skills to look at data and ask, “What story is this telling me? What patterns do I notice? What questions does this raise?” Whether students are working with simple tally marks or sophisticated datasets, the fundamental thinking is the same.
9–10 Competencies
Describe associations between two categorical variables using measures such as difference in proportions and relative risk.
Analyze data to uncover correlations, trends, and groupings that inform decision-making processes across diverse fields.
Classroom resources
Data Science Day: College Admissions Analytics
Students analyze comprehensive college admissions data including acceptance rates, financial aid trends, return on investment calculations, major selection outcomes, and demographic patterns to make evidence-based decisions about higher education using professional analytical methods.
Data Science Starter Kit Module 3: Making Sense of Data - Analysis and Modeling
Welcome to the exciting part of data science—making sense of the information you’ve collected! This module focuses on how to analyze data to find patterns, understand what the numbers really mean, and start drawing conclusions.🔗
Analysis and Modeling isn’t about complex mathematics or advanced statistics. It’s about developing the thinking skills to look at data and ask, “What story is this telling me? What patterns do I notice? What questions does this raise?” Whether students are working with simple tally marks or sophisticated datasets, the fundamental thinking is the same.
11–12 Competencies
Conduct linear regression analysis to find the best-fit.
Construct prediction intervals and confidence intervals to determine plausible values of a predicted observation or a population characteristic.
Classroom resources
Data Science Day: College Admissions Analytics
Students analyze comprehensive college admissions data including acceptance rates, financial aid trends, return on investment calculations, major selection outcomes, and demographic patterns to make evidence-based decisions about higher education using professional analytical methods.
Data Science Starter Kit Module 3: Making Sense of Data - Analysis and Modeling
Welcome to the exciting part of data science—making sense of the information you’ve collected! This module focuses on how to analyze data to find patterns, understand what the numbers really mean, and start drawing conclusions.🔗
Analysis and Modeling isn’t about complex mathematics or advanced statistics. It’s about developing the thinking skills to look at data and ask, “What story is this telling me? What patterns do I notice? What questions does this raise?” Whether students are working with simple tally marks or sophisticated datasets, the fundamental thinking is the same.
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.