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Professor Donoho (2024) has written a comprehensive and insightful article filled with practicality and optimism for the future of data science.He does a magnificent job of 'connecting many dots' across the many dimensions of computing, data science, and all things 'data.'I would like to highlight some aspects of the article that are particularly appealing to me, while also injecting some real-world difficulties with his thesis.If I had to summarize this article for someone without a background on modern trends in data science (and the perseverance to consume a vast array of interesting examples and emerging trends in this article), it would be this:Public, analytical competitions are a mechanism for advancing the science of analyzing data and making predictions.Furthermore, such competitions need to have common data sets from which to operate and the sharing of code for the analytical method(s).If all of this is done in the public forum, there will be an acceleration of innovation, since using common data provides a collective framework, sharing code allows for immediate modifications to test for improved predictions, and the competition is an objective way to 'score' what methods are providing the best predictive results.
Stephen J. Ruberg (Fri,) studied this question.