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Imagine a restaurant where all the employees are cooks, with no waiters, greeters or bussers. Or another restaurant in which a single employee does every job required to bring a dish to the table: planting produce, milking cows, preparing ingredients, taking and cooking orders, plating and serving the final dish. It is unlikely that either of these extremes will work well in practice. In a similar way, the interdisciplinary nature of data science means that complex projects usually require a team of people to guarantee success. Building data science skills effectively across an organization requires providing targeted learning opportunities and resources. This talk will discuss three key audiences for data science education and some ideas about how to increase data science fluency for each of these audiences."
