Statistics

This course provides a basic and broad introduction to the representation, analysis, and processing of sampled data. The course introduces statistical analysis, mathematical modeling, machine learning, and visualization for experimental data. Examples are taken from real-world problems, such as analysis of internet traffic, language technology, digital sound, and image processing.

The course is intended to be a (non-exhaustive) survey of regression techniques from both a theoretical and applied perspective. Time permitting, the methods students study include: exploratory data analysis, simple linear regression; multiple linear regression; regression with categorical variables; regression with interaction terms; polynomial regression; model selection for multiple linear models; and regression diagnostics.

This course examines option pricing and hedging. It will concentrate on the theory and idea of derivatives pricing and risk management. Topics include option market; European and American options; conditional expectation and discrete-time martingale, discrete-time option pricing theory; true probabilities vs. risk-neutral probabilities; estimating volatility; the Black-Scholes formula; implied volatility; option Greeks; market-making and hedging; and exotic options.

This is a special studies course involving an internship with a corporate, public, governmental, or private organization, arranged with the Study Center Director or Liaison Officer. Specific internships vary each term and are described on a special study project form for each student. A substantial paper or series of reports is required. Units vary depending on the contact hours and method of assessment. The internship may be taken during one or more terms but the units cannot exceed a total of 12.0 for the year.

This course examines time series with applications. Fundamental concepts of time series such as trends, stationary process, ARIMA process, model building (including parameter estimation, order determination and diagnostic checking), forecasting and seasonal models, ARCH and GARCH models will be covered. The use of related statistical packages will be demonstrated. 

This course seeks to immerse students in a professional work environment. Students have the opportunity to observe and interact with co-workers, and learn how to recognize and respond to cultural differences. Students compare concepts of teamwork and interpersonal interactions in different cultures as experienced on the job. Seminar work helps students apply academic knowledge in a business setting and identify opportunities to create value within the company. Students research a specific topic related to their work placement and present their findings in a final research report.

This service-learning course combines a structured curriculum and extensive partnership with a local community-based organization to offer tangible community service. Here, student community service includes direct
engagement as well as a research-based action plan addressing a specific challenge or goal identified by a community-based organization. Students begin by exploring key community-based organizations: examining their
mission, vision and goals, and the place of the organization in the local community. Each student then works with an assigned partner organization and invests at least 90 hours partnering with the organization, working with them
and investigating ways to solve a challenge or issue the organization has identified. Student service-learning includes exploring the proximate and ultimate drivers of the organization's chosen challenge, and the organization's
infrastructure, resources, limitations and possibilities for reducing barriers to achieving the organization's self-identified goals. In concert, coursework probes the role of community-based organizations in both local and global
contexts, common challenges of community-based organizations in defining and implementing their goals, the role of service-learning in addressing these issues, and effective ways for students to help them achieve their mission,
vision, and goals. Coursework also guides the student's service-learning experience by helping students develop sound international service ethics, provide tools to investigate solutions to common development issues, aid in
data analysis and presentation, and provide best practices to illustrate findings and deliver approved joint recommendations orally and in writing. Throughout, students use service-learning as a means to expand their global awareness and understanding, explore shared aspirations for social justice, and develop skills to work with others to effect positive change.

This course examines key ideas behind algorithms from a statistical perspective, and provide an in-depth knowledge that will enable students to apply the methods with awareness of their strengths and limitations. The topics covered will include probabilistic and analytic foundations, multivariate statistical analysis and machine learning, with a particular focus on clustering, classification, model selection and high-dimensional statistical analysis. 

This course focuses on applications of basic statistical techniques. In particular, we explore model formulation, model fitting, interpretation and presentation of analysis results for simple and multiple linear regressions, and logistic regression models. Some applications to data from the field of Agriculture, Biology , Economics, Finance etc. will be explain various concepts. Applications using R statistical software is also considered.

This course provides an introduction to the statistical and econometric theory underlying surveys and counterfactual policy evaluations, which have long played a prominent role in democracies' political life. Doing so, it sharpens critical appraisal of the very many surveys and policy evaluations that are to be found in public discourse. This class uses mathematical notation and proofs: students should be motivated to engage with mathematically formalized material.