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.

The class will be held Wednesday evenings in 503 Hamilton, 6:10-8:55pm (with a break)