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STANDARDS

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US-VA

Math

Virginia Math

Data Science: Data in Context

1.DS.1

The student will identify specific examples of real-world problems that can be effectively addressed using data science.

1.DS.1.a

Identify and explain characteristics that best lend themselves to a data driven approach to problem solving.
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1.DS.1.b

Formulate questions based on context.
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1.DS.1.c

Understand the type of data relevant to the context of the question at hand.
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1.DS.1.d

Define relationships between variables and constant relationships.
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1.DS.1.e

Create a hypothesis of interest in terms of measurable data.
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1.DS.1.f

Define the stages of the data cycle and how each stage is related to the other.
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1.DS.1.g

Identify and explain constraints of the data-driven approach.
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1.DS.2

The student will be able to formulate a top-down plan for data collection and analysis, with quantifiable results, based on the context of a problem.

1.DS.2.a

Design a data project plan, which is aligned with the data science cycle, that includes the following components:

1.DS.2.a.i

definition of the goal of the project as it pertains to a real-world problem;
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1.DS.2.a.ii

identification of the various parameters of the problem and stakeholders;
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1.DS.2.a.iii

a timeline for the project with deliverables;
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1.DS.2.a.iv

Key Performance Indicators (KPI) for the successful data project deliverables;
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1.DS.2.a.v

resource needs and tools for the project;
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1.DS.2.a.vi

bias considerations for the sampling process of the project; and
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1.DS.2.a.vii

limitations of the project.
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1.DS.2.b

Given the context and parameters of a problem, choose from among various sampling techniques, which may include

1.DS.2.b.i

simple random;
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1.DS.2.b.ii

systematic;
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1.DS.2.b.iii

stratified; and
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1.DS.2.b.iv

cluster; to justify the sampling methodology of the project design and implementation.
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