Courses

Prerequisites: STAT GU4206 The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems - not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.

Prerequisites: STAT GU4204 and STAT GU4205 A fast-paced introduction to statistical methods used in quantitative finance. Financial applications and statistical methodologies are intertwined in all lectures. Topics include regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models, principal components and multivariate analysis, smoothing techniques and estimation of yield curves statistical methods for financial time series, value at risk, term structure models and fixed income research, and estimation and modeling of volatilities. Hands-on experience with financial data.

Prerequisites: At least one semester of calculus. A calculus-based introduction to probability theory. Topics covered include random variables, conditional probability, expectation, independence, Bayes rule, important distributions, joint distributions, moment generating functions, central limit theorem, laws of large numbers and Markovs inequality.

Prerequisites: STAT GR5203 and GR5204 or the equivalent. Theory and practice of regression analysis, Simple and multiple regression, including testing, estimation, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data.

Open to MA students in Statistics only Prerequisites: STAT GU4205 or the equivalent. Least squares smoothing and prediction, linear systems, Fourier analysis, and spectral estimation. Impulse response and transfer function. Fourier series, the fast Fourier transform, autocorrelation function, and spectral density. Univariate Box-Jenkins modeling and forecasting. Emphasis on applications. Examples from the physical sciences, social sciences, and business. Computing is an integral part of the course.

Prerequisites: STAT GR5206 or the equivalent. Open to MA students in Statistics only The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems - not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.

Prerequisites: STAT GR5204 or the equivalent. STAT GR5205 is recommended. Open to MA students in Statistics only A fast-paced introduction to statistical methods used in quantitative finance. Financial applications and statistical methodologies are intertwined in all lectures. Topics include regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models, principal components and multivariate analysis, smoothing techniques and estimation of yield curves statistical methods for financial time series, value at risk, term structure models and fixed income research, and estimation and modeling of volatilities. Hands-on experience with financial data.

This course is intended to provide a mechanism to MA students in Statistics who undertake on-campus project work or research. The course may be signed up with a faculty member from the Department of Statistics for academic credit. Students seeking to enroll in the course should identify an on-campus project and a congenial faculty member whose research is appealing to them, and who are able to serve as their mentor. Students should then submit an application to enroll in this course, which will be reviewed and approved by the Faculty Director of the MA in Statistics program.

Prerequisites: GR5203; GR5204 &GR5205 and at least 4 approved electives This course is an elective course for students in the M.A. in Statistics program that counts towards the degree requirements. To receive a grade and academic credits for this course, students are expected to engage in approved off-campus internships that can be counted as an elective. Statistical Fieldwork should provide students an opportunity to apply their statistical skills and gain practical knowledge on how statistics can be applied to solve real-world challenges.

The purpose of this course is to provide an overview of trends and best practices in corporate communications relating to sustainability, with a particular focus on global sustainability reporting frameworks and green marketing communications. It is designed for those who hold/will hold positions in organizations with responsibilities for communicating the sustainability goals, challenges and achievements, as well as accurately and honestly communicating the environmental aspects of an organization's products and services. Increasingly, large corporations are creating c-suite roles or dedicated departments to oversee this function. More typically, multiple functions contribute information such as: Corporate Communications, Marketing, Community Affairs, Public Policy, Environmental Health & Safety, R&D, Facilities, Operations and Legal. Benefits of reporting range from building trust with stakeholders, and uncovering risks and opportunities; to contributing to stronger long-term business strategy, and creating new products and services.

Culinary practices are intricate to how urban spaces are experienced in everyday life. This course explores the nuanced ways food practices transform global cities worldwide. It investigates how personal preferences of food shape social, cultural, and spatial boundaries. Throughout the course, students will analyze urban spaces in global cities from an intersectionality theory of capitalism lens to consider how power structures shape culinary practices in terms of race, gender, sexuality, citizenship, class, ethnicity, language, religion, caste, ability, and diet. For instance, immigrant cooking and eating practices help define ethnic enclaves. And gourmet food trucks for the middle-class can become tropes for spurring gentrification. Analyzing global North and South cities, course themes focus on the politics of street food, food trucks, restaurants, markets, farmers’ markets, food deserts, food assistance programs, urban farming and agriculture, gastronomic gentrification, and food delivery services. This course comprises a mixture of active teaching strategies, short lectures, a film, and several field trips throughout New York City. By the end of the course, students will garner a deep understanding of how food and societies influence, and are shaped by, contemporary global cities.

Culinary practices are intricate to how urban spaces are experienced in everyday life. This course explores the nuanced ways food practices transform global cities worldwide. It investigates how personal preferences of food shape social, cultural, and spatial boundaries. Throughout the course, students will analyze urban spaces in global cities from an intersectionality theory of capitalism lens to consider how power structures shape culinary practices in terms of race, gender, sexuality, citizenship, class, ethnicity, language, religion, caste, ability, and diet. For instance, immigrant cooking and eating practices help define ethnic enclaves. And gourmet food trucks for the middle-class can become tropes for spurring gentrification. Analyzing global North and South cities, course themes focus on the politics of street food, food trucks, restaurants, markets, farmers’ markets, food deserts, food assistance programs, urban farming and agriculture, gastronomic gentrification, and food delivery services. This course comprises a mixture of active teaching strategies, short lectures, a film, and several field trips throughout New York City. By the end of the course, students will garner a deep understanding of how food and societies influence, and are shaped by, contemporary global cities.