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Data Science: The Numbers of Our Lives New York Times (04/11/13) Claire Cain Miller

The rise of big data over the past few years has motivated universities to offer programs to prepare students to be the data scientists of the future. For example, this fall Columbia University will offer new master’s and certificate programs that emphasize data, and the University of San Francisco will soon graduate its first class of students with a master’s degree in analytics. Data science also is being taught at New York University, Stanford University, Northwestern, George Mason University, Syracuse University, University of California, Irvine, and Indiana University. Some students intend to apply their degrees to e-commerce, in which consumer data functions as a currency, while others will enter government service; for example, analyzing tax return data to create algorithms to detect fraudulent filings. Employment opportunities for data science graduates will abound, as the United States needs an increase of up to 60 percent of such graduates, according to McKinsey Global Institute. In five years, half a million data science jobs and a shortfall of up to 190,000 qualified data scientists will exist. To address the nascent academic discipline, universities are working to define curricula that span statistics, analytics, computer science, math, and other specialized fields of study.


Blog to document and reflect on Columbia Data Science Class


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.

In the Fall of 2012, she taught Introduction to Data Science (Statistics W4242) at Columbia University.  This is the blog she wrote for that class. An interview with her on the Google Research page about Data Science and why she created the class in the first place can be found here. The course blog includes weekly reports on the lectures, as well as student work, and blog posts about Data Science.

Here is Rachel’s TEDx talk from December, 2012. The event organizers invited her to tell a personal story in order to embody one of 10 core tenets of leadership, so this isn’t a technical talk, but rather addresses how data and humanity go hand in hand. Also, after the semester wrapped up, the New York Times published this article by Steve Lohr, “Big Data is Great. But So Is Intuition“, which captures some of the themes of the course.


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