Mathematics & Statistics

• 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 615: Data Science in R
  Introduction to R, the computer language written by and for statisticians. Emphasis on data exploration, statistical analysis, problem solving, reproducibility, and multimedia delivery. Intended for MSSP and other graduate students. Effective Fall 2020, this course fulfills a single unit in the following BU Hub area: Critical Thinking.
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  • Critical Thinking
• 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 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 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.
• GRS MA 711: Real Analysis
  Measure theory and integration on measure spaces, specialization to integration on locally compact spaces, and the Haar integral. Lp spaces, duality, and representation theorems. Introduction to Banach and Hilbert spaces, open mapping theorem, spectral theorem for Hermitian operators, and compact and Fredholm operators.
• GRS MA 713: Functions of a Complex Variable I
  The theory of analytic functions. Integral theorems, contour integration, conformal mapping, and analytic continuation.
• GRS MA 717: Functional Analysis I
  Theory of Banach and Hilbert spaces, and Hahn-Banach and separation theorems. Dual spaces. Banach contraction mapping theorem. Reflexivity and Krein-Milman theorem. Operator theory. Brouwer-Schauder fixed-point theorems. Applications to probability, dynamical systems, and applied mathematics.
• GRS MA 721: Differential Topology I
  Differential manifolds, tangent bundles, transversality, winding numbers, and vector bundles.
• GRS MA 722: Differential Topology II
  Intersection theory, Lefschetz fixed point theory, integration on manifolds, vector fields and flows, and Frobenius' theorem.
• GRS MA 725: Differential Geometry I
  Geometry of surfaces in Euclidean space; geodesics and curvature of Riemannian manifolds; topological restrictions on curvature.

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