Abstract:
This short tutorial introduces the fundamental principles and best practices of project management; it specifically targets data science projects, including those that involve supervised machine learning (ML) and natural language processing (NLP) elements. The view presented is overall consistent with traditional project management methodology as taught by the Project Management Institutes (PMI), but adapts and extends these as necessary and beneficial to the data science context, which is more experimental, data driven (and thus open-ended and uncertain) than, say, a bridge construction project. By including practical aspects like interfacing between technologists and business stakeholder in industry projects and integrating ethics and privacy considerations, we hope to provide a holistic, useful and practically applicable account.
Presenter:
Jochen L. Leidner, Thomson Reuters Research & Development and University of Sheffield, United Kingdom
