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STANDARDS

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US-VA

Math

Virginia Math

Data Science: Data and Communication

3.DS.5

The student will use storytelling as a strategy to effectively communicate with data.

3.DS.5.a

Define storytelling and explain the importance of storytelling as a strategy to communicate the idea behind and results of a data science project effectively.
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3.DS.5.b

Explain the steps involved in data storytelling and how it relates to the data cycle.
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3.DS.5.c

Effectively identify a story worth telling based on the data (looking for trends, correlations, outliers) and by asking a question or forming a hypothesis based on insight and audience.
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3.DS.5.d

Effectively select visualizations that simplify the information, highlight the most important data, and communicate key points quickly.
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3.DS.5.e

Effectively simplify the information presented to make it more concise and focus the audience's attention on the key parameters that support the student’s hypothesis.
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3.DS.5.f

Effectively form a narrative based on data available to provide context, insight, and interpretation to make the analysis more relevant to a given audience.
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3.DS.5.g

Explain how data storytelling should include complete and accurate information, and consistent visuals for effective communication.
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3.DS.6

The student will justify the design, use, and effectiveness of different forms of data visualizations.

3.DS.6.a

Conduct exploratory data analysis using visualization.
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3.DS.6.b

Formulate questions from exploration of a data set to consider how data will communicate a story.
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3.DS.6.c

Determine the effectiveness of different data visualization choices based on the data context from conventional statistical charts to unconventional/emerging data visualizations to more complex visualizations.
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3.DS.6.d

Create a visualization of a data set and summarize the representation using the context of the data.
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3.DS.6.e

Compare two or more different representations to ensure the design communicates the features and behavior of data sets.
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3.DS.6.f

Justify design choices (based on data set type, size, context, and audience) of data visualizations to highlight important features, trends, and insights.
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