Data engineering is a crucial subset of the data science toolbox because access to data is required for high quality analysis and story telling. A data scientist must understand the tasks and time required for data engineering and be prepared to roll up her sleeves, which may include hammering out low-level scripts or developing company-wide software to create a fully functional data science environment. The data can set up successes or failures at an organization. A data scientist’s work is only as good as her access to data. This posts provides questions to evaluate time requirements for engineering the ideal environment, data access, and resources?

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