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STANDARDS

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US-VA

Math

Virginia Math

Data Science: Data Bias

2.DS.3

The student will recognize the importance of data literacy and develop an awareness of how the analysis of data can be used in problem solving to effect change and create innovative solutions.

2.DS.3.a

Formulate relevant/clarifying questions to identify potential data biases presented in existing analyses/visualizations.
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2.DS.3.b

Effectively read data summaries and visualizations and explain/translate into nontechnical terms in proper context.
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2.DS.3.c

Identify potential data biases in terms of data presented and discuss the potential effects of such biases in terms of how they could affect data analysis and decision making.
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2.DS.3.d

Identify privacy and consumer protection issues that might be a result of how data is presented.
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2.DS.3.e

Describe the types of data that business, industry, and government entities collect and possible ways the data is used.
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2.DS.4

The student will be able to identify data biases in the data collection process and understand the implications and privacy issues surrounding data collection and processing.

2.DS.4.a

Identify data biases in the data collection process that include, but are not limited to, confirmation, selection, outliers, overfitting / under fitting, and confounding and describe mitigation strategies for these biases.
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2.DS.4.b

Provide examples of sampling biases in terms of data collection and the potential effects.
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2.DS.4.c

Identify and describe data biases as a producer as well as a consumer/decision maker of data.
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2.DS.4.d

Describe how the data collection process should be focused, relevant, and limited to the scope of the data project plan.
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2.DS.4.e

Describe privacy considerations in the collection of data as both a consumer and producer.
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