Jonathan Auerbach

An annual data challenge open to all. Contestants compete to predict the peak bloom date of cherry trees at locations around the world. Complete entries are eligible to receive prizes of $5,000 or more and will help scientists better understand the impacts of climate change.

This class develops skills in the areas of technical writing and oral communication. Students will develop a historical and ethical appreciation of the field of statistics as well as connect methods from their undergraduate coursework to solve problems.

Students will synthesize methods and ideas acquired in their undergraduate courses by working in small groups on a project and presenting their findings in a written report and an oral presentation.

How do advocates use evidence to effect change? When do data uncover the truth–and when do they distort it? This class teaches how to argue persuasively with data. Students will learn how data are collected, examined with statistical software, and communicated. All three themes: collection, examination, and communication are explored from theoretical principles, historic accounts, and hands-on coding experience, with a special focus on New York City policy. The class will culminate with each student conducting an original investigation. High school algebra is required. Calculus is helpful but not necessary. No coding experience is expected.

In this course, students will carry out a series of explorations in data science to learn about statistical thinking, principles and data analysis skills used in data science. These explorations will cover topics including: descriptive statistics, sampling and estimation, association, regression analysis, etc. Classes will be organized to have a lecture component and a hands-on exploration component each session. In the lecture session, an introductory curriculum on data science will be given. In the exploration session, students will be led through data analysis exercises using the statistical analysis language R. These exercises are designed to use open data, such as NYC open data that contain interesting information about neighborhoods of New York City. No prior programming experience is required.

A friendly introduction to statistical concepts and reasoning with emphasis on developing statistical intuition rather than on mathematical rigor. Topics include design of experiments, descriptive statistics, correlation and regression, probability, chance variability, sampling, chance models, and tests of significance.