Understanding sources of variability
Recognize measurement errors and natural variability in data. Assess data quality, identify outliers, and refine models using statistical and contextual analysis.
K–2 Competencies
Classroom resources
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
Identify and explain simple measurement error. e.g., different students' get varying results when measuring the same object
Identify potential sources of natural variability in a given measure based on knowledge of the data context. e.g., plants can be different heights, plants grow taller over time, plants grow differently in different areas in the garden
Classroom resources
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
Consider both context and the characteristics of a dataset to determine whether a given data point is reasonable. e.g., meaningful outliner, erroneous outlier
Relate sources of variability to domain-specific phenomena as described in the relevant domain standards. e.g., Next Gen Science Standards, Mathematics Common Core State Standards, C3 Framework
Classroom resources
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
Consider variability as a key component of informal inference by questioning whether observed differences are meaningful or not. e.g., phone battery lasts 6 hours one day and 4 the next—is this a real difference in battery life, or just normal variation from daily use
Identify categorical options for measuring "best" fit from data points to provided estimates. e.g., line or curve for a scatterplot, mean for a distribution
Consider both context and the characteristics/source of a dataset to determine how "messy" a dataset may be due to measurement error. e.g., faulty sensors, inaccurate or inappropriate measurements
Use errors to improve the AI and/or machine learning model.
Classroom resources
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
Estimate and describe errors between predictions and actual outcomes. e.g., residuals, misclassification rates
Analyze error patterns to assess model performance. e.g., residual plot, confusion matrix
Use insights from error analysis to improve the model. e.g., in linear regression, add a variable or use a curve; in classification, balance the groups or adjust the cutoff
Classroom resources
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
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