Data science lies at the intersection of a number of disciplines, including mathematics, computer science, statistics, and communications. The overwhelming breadth of its body of knowledge makes it difficult for businesses and practitioners to effectively communicate results and challenges, manage the lifecycle of data-driven projects, build a technology radar that allows for opportunity discovery, manage the uncertainty of data science projects, or select the correct set of human resources and technologies.
Despite being a largely experimental discipline, data science is built on a set of solid foundations, such as learning theory, that have traditionally been restricted to the academic setting due to their theoretical nature. The growth of data science as a strategic discipline makes its correct management paramount to the survival of new and traditional businesses that want to compete in a foreseeable data-driven economy. David Martinez Rego shares a set of sound, solid principles (that should be familiar to any data science manager) that will help increase your effectiveness as a data science manager, anticipate limitations of algorithms and overcome them, and assist in understanding each current and future technique or algorithm into a broader context.
David Martinez Rego is a data scientist at DataSpartan specializing in designing tailored software systems and algorithms. He has been part of laboratories in a number of academic institutions, including the University of A Coruña, the University of Florida, and University College London. Currently, he is based in London, where he divides his time doing research, lecturing, and consulting for different industries and startups.
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