Concept C1.1

Measures of center

Analyze large datasets by measuring their central tendency while considering the context and distribution of the data.

K–2 Competencies

Recognize that categorical data does not have a measure of center.

K-2.C.1.1a

Describe the center of numeric data categorically using phrases like “most popular”.

K-2.C.1.1b

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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

Calculate summaries for categorical and numeric data, focusing on total and typical values.

3-5.C.1.1a

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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

Identify measures of center as statistical values that represent the central tendency of data sets.

6-8.C.1.1a

Explain what measures of center are useful for and their limitations.

6-8.C.1.1b

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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

Identify appropriate ways to summarize numerical or categorical data using frequency tables, graphical displays, and numerical summary statistics.

9-10.C.1.1a

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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

Explore the sensitivity of the mean to outliers compared to the median.

11-12.C.1.1a

Discuss instances when to use the mean or median based on the context and data distribution (e.g., skewed vs. symmetric distribution).

11-12.C.1.1b

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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.

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