Data Science on Deadline
Training for data scientists focuses largely on nouns, such as algorithms, software, infrastructure, and data products. Noun-based training teaches tools and technical skills, but has little to say about time. Therefore, fundamental concepts such as scheduling, deadlines, efficiency, and return on investment. This presentation shares a verb-based framework for reasoning about time in data science workflows. From identifying the day-to-day activities that occupy most of data workers’ time, to accelerating essential activities to eliminating wasted steps, data science on deadline teaches the cognitive reflexes that separate abstract thinkers from productive doers in the world of data.
Abe was the first data scientist at Jawbone, where he builds data systems to nudge people to form good habits and live healthier. Prior to Jawbone, Abe was the lead data scientist at Massive Health. He earned his PhD in Public Policy, Political Science, and Complex Systems at the University of Michigan. All told, Abe has worked as data scientist/statistical consultant in education, health, and public policy for over a decade. In previous lives, Abe has been a pollster, journalist, refugee, and amateur historian.