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Data Science is a field with immense breadth and depth. As our toolkit grows and we learn how to chain models and pipelines together, there's no limit to the time and complexity we can devote to over-engineering a solution. In my role as a data science instructor at Metis, and during my tenure at Microsoft before that, I've observed time and again that many data scientists seem to be much better at learning algorithms and software libraries than they are at identifying good business cases to solve, and designing workflows that will enable them to work productively from start to finish.
In this workshop I share a user-focused design approach to building an end-to-end minimum viable product for any data science project. By asking and answering the right questions at the start of any new project, we can set a course that points to clear choices for the design decisions we face along the way. I teach this in the context of building a music genre classification model that can be used to generate playlists from text descriptions.
Metis (thisismetis.com) accelerates careers in data science by providing full-time immersive bootcamps, evening part-time professional development courses, online resources, and corporate programs based in Seattle, New York, Chicago, and San Francisco.
Brought to you by Kaplan, Metis focuses primarily on Python, machine learning, data visualization, deep learning, big data processing, statistical foundations, and more. Students and alumni of the bootcamp program receive continuous support from our career advisors, empowering them to pursue a successful career in the fast-growing field of data science.
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