About the Course 2012

This course is being offered in the Department of Statistics at Columbia University in the Fall, 2012.

This course is an introduction to the interdisciplinary and emerging field of data science, which lies at the intersection of statistics, computer science, data visualization and the social sciences. The course will be organized around three central threads: (1) statistical modeling and machine learning, (2) data pipelines, programming languages and “big data” tools, and (3) real world topics and case studies. Correspondingly there will be (1) core lectures, (2) labs and (3) guest lectures from researchers and scientists who are experts in their fields. Topics and tools will include logistic regression, predictive modeling, clustering algorithms, decision trees, Hadoop, data pipelines, visualization, data journalism, R, python.

Syllabus

Lectures: Wednesdays 6:10-8:55pm
Location: 313 Fayerweather

Labs: Mondays 6:10-7:25pm and 7:40-8:55 (pick one)
Location: 313 Fayerweather

Problem Sessions: Thursdays 7:45-9:15pm
Location: 313 Fayerweather

Office Hours: Tuesdays 4-5:30pm
Location: Statistics Department, 1255 Amsterdam Ave, School of Social Work Building, 10th floor

Contact Info:
Professor: Rachel Schutt, rrs2117@columbia.edu
Lab Instructor: Jared Lander,  jpl2135@columbia.edu
Teaching Assistant: Ben Reddy, reddy@stat.columbia.edu

One comment

  1. […] Columbia, “Introduction to Data Science” was taught by Rachel Schutt, a senior statistician at Google’s research division in New York. […]

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