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Scientific computation has grown in scope over the years, but today's needs are far beyond what people have experienced in traditional scientific fields such as physics, chemistry, and biology. Although the term "computing" might also mean information processing as opposed to numerical computation, it's increasingly difficult to draw a clear boundary between the two. As more disciplines require or desire computational skills, the crowds we teach will include not only future engineers and natural scientists but also social scientists, business analysts, or even casual learners. People in this learning community will contribute to computational science not by theoretically exploring the properties of numerical algorithms, but by applying or modifying existing numerical methods or methodologies to successfully solve problems in their respective fields. How we teach numerical computation to the crowds must be taken far beyond the discussions on the relevance and advantages of computational software or programming languages. Whether repackaging what we teach would prepare students better - numerically speaking - remains to be seen. However, we must find ways to integrate modern scientific computation research results into our curricula.