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<divclass="lead">Benjamin Xie & Greg L. Nelson</div>
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<p>
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We define data science as an <b>iterative process of augmenting human thinking with computational tools to use data to make decisions in/about the world.</b>
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</p>
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<p>
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Let's decompose that definition:
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<ul>
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<li>
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<i>"iterative process"</i>: While we see data science as a process (explained below), it is very much an iterative one.
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We will often find ourselves jumping back to a previous step in the process or jumping "out of order" as the situation demands.
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</li>
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<li>
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<i>"augmenting human thinking with computational tools"</i>: Human thinking and reasoning is at the core of data science.
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We want to teach you first and foremost how to think like a data scientist. Computational tools supplement human thinking,
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but we reiterate that human thinking is at the core of data science.
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</li>
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<li>
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<i>"make decisions in/about the world"</i>: The purpose of data science is inform decisions.
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Because these decisions are very dependent on the contexts they are made in, the contexts data scientists work are very critical.
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</li>
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</ul>
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</p>
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<p>
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We define the <b>data science process</b> as 5 steps:
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<ol>
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<li>Identify decision context and data science question(s)</li>
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<li>Collect and clean data</li>
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<li>Model data</li>
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<ol>
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<li>Generate explanations and models</li>
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<li>Evaluate and interpret explanations and models</li>
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</ol>
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<li>Make/Inform decisions</li>
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<li>Archive work</li>
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</ol>
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We reiterate that this process is iterative and we may jump backwards or out of order to different steps.
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We also reiterate that this entire process exists within specific contexts, so data scientists much be critical of their work at each step.
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This means (among other things) considering bias in the data, model, and interpretations and ethical and privacy concerns.
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