Courses

The listing of a course description here does not guarantee a course’s being offered in a particular term. Please refer to the published schedule of classes on the MyBU Student Portal for confirmation a class is actually being taught and for specific course meeting dates and times.

• CAS MA 588: Nonparametric Statistics
  Undergraduate Prerequisites: CASMA 582 or consent of instructor. - The theory and logic in the development of nonparametric techniques including order statistics, tests based on runs, goodness of fit, rank-order (for location and scale), measures of association, analysis of variance, asymptotic relative efficiency.
• CAS MA 589: Computational Statistics
  Undergraduate Prerequisites: CASMA 575 or consent of instructor. - Topics from computational statistics that are relevant to modern statistical applications: random number generation, sampling, Monte Carlo methods, computational inference, MCMC methods, graphical models, data partitioning, and bootstrapping. Emphasis on developing solid conceptual understanding of the methods through applications.
• CAS MA 592: Introduction to Causal Inference
  Undergraduate Prerequisites: CASMA 575 or consent of instructor. - Concepts and methods for causal inference. You may have heard "association does not imply causation." But, what implies causation? In this course, we study how to estimate causal effects from data. We cover both experimental and non-experimental settings.
• CAS MA 614: Statistical Methods 2
  Prerequisites: Graduate standing in education or in the social sciences. - 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."
• CAS MA 615: Data Science in R
  Prerequisites: Graduate Standing, (CASCS 111) or equivalent, and at least one course in statistics. - 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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• CAS MA 665: Introduction to Modeling and Data Analysis in Neuroscience
  Prerequisites: (CASMA 122 or CASMA 124) or equivalent, and graduate standing, or consent of instructor - 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,
• CAS MA 666: Advanced Modeling and Data Analysis in Neuroscience
  Prerequisites: (CASMA 226 or CASMA 231) or equivalent. Graduate standing required, or consent of instructor. - 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.
• CAS MA 675: Statistics Practicum 1
  Prerequisites: Admission to the Statistical Practice MS program - 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.
• CAS MA 676: Statistics Practicum 2
  Prerequisites: Admission to the Statistical Practice MS program. - 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.
• CAS MA 677: Conceptual Foundations of Statistics
  Prerequisites: Admission to the MSSP program. - 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.
• CAS MA 678: Applied Statistical Modeling
  Prerequisites: Admission to the MSSP program. - 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.
• CAS MA 679: Applied Statistical Machine Learning
  Prerequisites: Admission to the MSSP program. - 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.
• CAS MA 681: Accelerated Introduction to Statistical Methods for Quantitative Research
  Prerequisites: (CASMA 225) AND (CASMA 242) or their equivalents. - 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.
• CAS MA 684: Applied Multiple Regression and Multivariable Methods
  Prerequisites: one year of statistics. - 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,
• CAS MA 685: Advanced Topics in Applied Statistical Analysis
  Prerequisites: (CASMA 684 or CASMA 416 or CASMA 575) or consent of instructor. Continues topics of CAS 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.
• CAS MA 711: Real Analysis
  Prerequisites: (CASMA 512) 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.
• CAS MA 713: Functions of a Complex Variable I
  Prerequisites: (CASMA 511) AND (CASMA512) or equivalent, or consent of instructor. - The theory of analytic functions. Integral theorems, contour integration, conformal mapping, and analytic continuation.
• CAS MA 717: Functional Analysis 1
  Prerequisites: (CASMA 711) or equivalent. - 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.
• CAS MA 721: Differential Topology 1
  Prerequisites: (CASMA 511) AND (CASMA512) or equivalent. - Differential manifolds, tangent bundles, transversality, winding numbers, and vector bundles.
• CAS MA 722: Differential Topology 2
  Prerequisites: (CASMA 721) - Intersection theory, Lefschetz fixed point theory, integration on manifolds, vector fields and flows, and Frobenius' theorem.