The Data Science Life Cycle: A Disciplined Approach to Advancing Data Science as a Science

« (…) The Data Science Life Cycle introduced here can be used as a framing principle to guide decision making in a variety of educational settings, pointing the way on topics such as: whether to develop new data science courses (and which ones) or rely on existing course offerings or a mix of both; whether to design data science curricula across existing degree granting units or work within them; how to relate new degrees and programmatic initiatives to ongoing research in data science and encourage the development of a recognized research area in data science itself; and how to prioritize support for data science research across a variety of disciplinary domains. (…) »

Source > cacm.acm.org, Victoria Stodden, Communications of the ACM, July 2020, Vol. 63 No. 7, Pages 58-66, 10.1145/3360646