Data Science is an emerging field in industry, yet not well-defined as an academic discipline (or even in industry for that matter). I proposed the “Introduction to Data Science” course at Columbia in March, 2012, based on my experience as a statistician at Google, and on a series of informal interviews and conversations I had with data scientists in industry, and professors. The course was offered in the fall of 2012, and was the first course at Columbia that had the term “Data Science” in the title. Throughout the semester we examined the central question of "What is Data Science?" from two perspectives: (1) Data Science is what Data Scientists do and (2) Data Science as the potential to be a deep academic discipline. I'll discuss the process I went through to create and design a Data Science course, how I engaged the data science community, how the course could be expanded to a master’s degree curriculum, the impact the course had on the students, and the shortcomings of the course.
Educating the Next Generation of Data Scientists
Rachel Schutt is a senior research scientist at Johnson Research Labs, an adjunct assistant professor of statistics at Columbia University, and a founding member of the Education Committee of Columbia’s Institute for Data Sciences and Engineering. Schutt is also co-authoring a book (with Cathy O’Neil) called “Doing Data Science”.
Her interests include statistical modeling, exploratory data analysis, machine learning algorithms, and social networks, as well as the ethical dimensions of data science, and using data science to do good.
She earned her Ph.D. from Columbia University in statistics and master’s degrees in mathematics and engineering from NYU and Stanford University.