Next-Gen Data Scientists

Data Science Ballroom AB
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(3.45, 11 ratings)

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. This was the first course at Columbia that had the term “Data Science” in the title. I had three primary motivations:

1) Bringing industry to students: I wanted to give students an education in what it’s like to be a data scientist in industry and give them some of the skills data scientists have. This is based on my experience as a lead analyst on the Google+ Data Science team. But I didn’t want to limit them to only my way of seeing the world, so each week, guest speakers from the NYC tech community came to teach the class.

2) I wanted to think more deeply about the science of data science: Data Science has the potential to be a deep and profound research discipline impacting all aspects of our lives. Columbia University and Mayor Bloomberg announced the Institute for Data Sciences and Engineering in July, 2012. This course created an opportunity to develop the theory of Data Science and to formalize it as a legitimate science.

3) Personal Challenge: I kept hearing from data scientists in industry that you can’t teach data science in a classroom or university setting and I took that on as a challenge. I wanted to test the hypothesis that it was possible to train awesome data scientists in the classroom.

In February 2013, 2 months will have passed since the class ended. I’ll be able to reflect on how the class went, how I thought about the curriculum, how I engaged the NYC tech community to be involved in the class, who the students were, whether I had impact on them, etc.

Photo of Rachel Schutt

Rachel Schutt

Johnson Research Labs

Dr. Rachel Schutt is a Senior Research Scientist at Johnson Research Labs. Prior to that, she was a Senior Statistician at Google Research in the New York office. She is also an Adjunct Assistant Professor in Columbia’s Statistics Department, and is a founding member of the Education Committee for the Institute for Data Sciences and Engineering at Columbia. Rachel is co-authoring a book (with Cathy O’Neil) called “Doing Data Science” to be published by O’Reilly in 2013.

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 holds several pending patents. She is a frequent speaker at conferences and universities.

She earned her PhD from Columbia University in Statistics, and Masters degrees in Mathematics and Engineering from the Courant Institute (NYU) and Stanford University, respectively. Her undergraduate degree is in Honors Mathematics from the University of Michigan.

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Harsha Srivatsa
03/02/2013 10:18am PST

Excellent session Rachel. Slide 16 (brainstorming what data scientists do) by itself is worth it.

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