The data science lifecycle involves the following general steps:
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Question/Problem Formulation:
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What do we want to know or what problems are we trying to solve?
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What are our hypotheses?
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What are our metrics of success?
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Data Acquisition and Cleaning:
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What data do we have and what data do we need?
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How will we collect more data?
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How do we organize the data for analysis?
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Exploratory Data Analysis:
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Do we already have relevant data?
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What are the biases, anomalies, or other issues with the data?
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How do we transform the data to enable effective analysis?
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Prediction and Inference:
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What does the data say about the world?
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Does it answer our questions or accurately solve the problem?
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How robust are our conclusions?
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