Data science has tremendous potential to extend our capabilities and empower organizations to accelerate their digital transformation by infusing apps and experiences with AI. Danielle Dean covers the basics of managing data science projects, including the data science lifecycle, and offers an overview of an internal approach at Microsoft, the Team Data Science Process (TDSP). TDSP is an agile, iterative data science methodology to deliver predictive analytics solutions and intelligent applications efficiently. TDSP helps improve team collaboration and learning and is a distillation of the best practices and structures from Microsoft and others in the industry that facilitate the successful implementation of data science initiatives. Join in to learn more about the typical priorities of data science teams and the keys to success on engaging and creating value with data science.
Danielle Dean is a principal data scientist lead at Microsoft in the Algorithms and Data Science Group within the Artificial Intelligence and Research Division, where she leads a team of data scientists and engineers building predictive analytics and machine learning solutions with external companies utilizing Microsoft’s Cloud AI Platform. Previously, she was a data scientist at Nokia, where she produced business value and insights from big data through data mining and statistical modeling on data-driven projects that impacted a range of businesses, products, and initiatives. Danielle holds a PhD in quantitative psychology from the University of North Carolina at Chapel Hill, where she studied the application of multilevel event history models to understand the timing and processes leading to events between dyads within social networks.
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