Courses

• GRS LX 795: Quantitative Methods in Linguistics
  Introduces students to quantitative approaches to linguistic data, including visualization, hypothesis testing, and data modeling. Students gain proficiency in R, an open-source statistical environment, and learn the logic behind statistical techniques, as well as practical skills for using them.
• GRS LY 741: 1001 Nights in the World Literary Imagination
  What is The Thousand and One Nights? How has this ever-expanding collection appealed to its diverse audiences? Focus on Nights structure and themes, notable translations and offshoots in western literature and art, and later appropriations by Arab and Muslim writers.
• GRS LY 951: Directed Study in Arabic
  Graduate-level directed study in a topic in Arabic.
• GRS LY 952: Directed Study in Arabic
  Graduate-level directed study in a topic in Arabic.
• GRS LZ 952: Directed Study in Persian (Farsi)
  Graduate-level directed study in a topic in Persian (Farsi).
• GRS MA 614: Statistical Methods 2
  Second course in statistics, embodying basic statistical methods used in educational and social science research. Reviews all basic concepts covered in a first statistics course and presents, in detail, more advanced topics such as analysis of variance, covariance, experimental design, correlation, regression, and selected nonparametric techniques. A problem-solving course; students carry out analysis of data taken from educational and other social science sources.
• GRS MA 647: Research Methods in Mathematics I
  Methods of mathematical research via prolonged study of one selected mathematical topic. Topics are usually chosen from number theory or combinatorics. Written and oral research presentations.
• GRS MA 648: Research Methods in Mathematics II
  Methods of mathematical reserach via prolonged, directed study of one selected mathematical topic, distinct from that chosen for GRS MA 647. Topics are usually chosen from geometry, number theory, or combinatorics, and may involve open problems. Written and oral research presentation.
• GRS MA 665: Introduction to Modeling and Data Analysis in Neuroscience
  An introduction to the basic techniques of quantifying neural data and developing mathematical models of neural activity. Major focus on computational methods using computer software and graphical methods for model analysis.
• GRS MA 666: Advanced Modeling and Data Analysis in Neuroscience
  Advanced techniques to characterize neural voltage data and analyze mathematical models of neural activity. Major focus on computational methods using computer software and graphical methods for model analysis.
• GRS MA 671: Chaotic Dynamical Systems
  For graduate students in disciplines outside of mathematics. Iterations of functions of one or several variables. Periodicity, stability, chaos, fractals, bifurcations. Julia sets and the Mandelbrot set. Students are required to perform several experiments on personal computers.
• GRS MA 675: Statistics Practicum 1
  First of a two-semester sequence aimed at integrating the quantitative training and other skills required for doing statistics in practice. Emphasis on statistical consulting throughout, complemented by modules on speaking, writing, statistical software and programming, and data analysis.
• GRS MA 676: Statistics Practicum 2
  Second of a two-semester sequence aimed at integrating the quantitative training and other skills required for doing statistics in practice. Emphasis on statistical consulting throughout, complemented by modules on speaking, writing, statistical software and programming, and data analysis.
• GRS MA 677: Conceptual Foundations of Statistics
  Introduction to statistical methods relevant to research in the computational sciences. Core topics include probability theory, estimation theory, hypothesis testing, linear models, GLMs, and experimental design. Emphasis on developing a firm conceptual understanding of the statistical paradigm through data analyses.
• GRS MA 678: Applied Statistical Modeling
  Application of multivariate data analytic techniques. Topics include ANOVA, multiple regression, logistic regression, generalized linear models, generalized linear mixed effect models, and Bayesian hierarchical models, experiment design, multiple comparison, and variable selection.
• GRS MA 679: Applied Statistical Machine Learning
  Continues topics of GRS MA 678 at a more advanced level. Application of supervised and unsupervised statistical machine learning techniques with extensive use of computation. Advanced topics such as analysis of network data, Bayesian nonparametric models are considered.
• GRS MA 681: Accelerated Introduction to Statistical Methods for Quantitative Research
  Introduction to statistical methods relevant to research in the computational sciences. Core topics include probability theory, estimation theory, hypothesis testing, linear models, GLMs, and experimental design. Emphasis on developing a firm conceptual understanding of the statistical paradigm through data analyses.
• GRS MA 684: Applied Multiple Regression and Multivariable Methods
  Application of multivariate data analytic techniques. Multiple regression and correlation, confounding and interaction, variable selection, categorical predictors and outcomes, logistic regression, factor analysis, MANOVA, discriminant analysis, regression with longitudinal data, repeated measures, ANOVA.
• GRS MA 685: Advanced Topics in Applied Statistical Analysis
  Continues topics of GRS MA 684 at a more advanced level. Canonical correlation, multivariate analysis of variance, multivariate regressions. Categorical dependent variables techniques; discriminant analysis, logistic regression, log-linear analysis. Factor analysis; principal-axes, rotations, factor scores. Cluster analysis. Power analysis. Extensive use of statistical software.
• GRS MA 703: Statistical Analysis of Network Data
  Methods and models for the statistical analysis of network data, including network mapping and characterization, community detection, network sampling and measurement, and the modeling and inference of network and networked-indexed processes. Balance of theory and concepts, illustrated through various applications.

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