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Course Criteria
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2.00 Credits
This course introduces students to financial time series modeling covering the topics of asset volatility, GARCH models, high-frequency data, and risk management. Prerequisites: STAT 5170 or STAT 6170 with a C- or better
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2.00 Credits
Students learn the theory and application of the linear model (LM), linear mixed model (LMM), generalized linear model (GLM) and generalized linear mixed model (GLMM) for unbalanced mixed-effect models using GLIMMIX. Prerequisite: Both of the following or instructor permission STAT 5200 with a C- or better MATH 5720 with a C- or better
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1.00 - 8.00 Credits
Educational work experience at the graduate level. Prerequisite/Restriction: Permission of instructor. Repeatable for credit.
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2.00 Credits
This course covers spatial data structures; spatial data exploration and visualization in R; spatial point patterns, spatially continuous data, and grid data; and nearest neighbor distances, K function, complete spatial randomness, variogram, kriging, and Moran's I. For 6000-level credit a major project is required. Crosslisted as: STAT 6410 Prerequisite: STAT 3000 or STAT 5100 with a C- or better STAT 5050 with a C- or better Further recommended: STAT 5550 STAT 5560/6560
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3.00 Credits
This is a survey of statistical methods and relevant theory frequently seen in biomedical applications, such as power calculations, multiple hypothesis testing, survival analysis, group sequential design, meta-analysis, and nonparametric tests. For graduate (6000-level) credit, additional work is required. Prerequisite/Restriction: C- or better in STAT 5100 or STAT 5200 or admission to the MPH program. Cross-listed as: STAT 5500 Semester(s) Traditionally Offered: Fall
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3.00 Credits
Survey of algorithms and tools for modern statistical computing. Topics include simulation design and implementation, algorithms for linear regression and subset selection, smoothing algorithms, fast fourier transform, EM algorithm, numerical methods for maximum likelihood estimation, and neural networks. Prerequisite/Restriction: C- or better in MATH 5720 and knowledge of a programming language.
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3.00 Credits
This programming intensive course covers key tools and programming principles for conducting reproducible data analyses in the R programming language. Topics include generic functions, variable scope, simulation, numerical precision, optimization, scalability, and reproducibility, all of which are presented in the context of custom R package development. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite(s): STAT 5050 and at least two credits in STAT or CS at the 5000 level or higher; or instructor permission Dual-listed as: STAT 5555 Repeatable for credit: No Grade Mode: Standard
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2.00 Credits
Students learn about statistical and scientific visualization of statistical maps and high-dimensional data; historic developments of graphics; current frontiers in visualization, including interactive, dynamic, and web-based graphics; and discuss effective use of color and motion in graphics. For graduate (6000-level) credit, a major project is required. Crosslisted as: STAT 5560 Prerequisite/Restriction:STAT 5050 and STAT 5550 with a C- or better
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2.00 Credits
This course introduces statistical methods for high dimensional biomedical data, primarily gene expression and sequence analysis, using Bioconductor tools. Topics include data visualization, differential expression (in high-dimensional count/continuous data), annotation testing, scoring alignments, HMMs, and phylogenetic trees. For graduate (6000-level) credit additional work is required. Crosslisted as: STAT 5570 Prerequisite: C- or better in STAT 5100 or 5200.
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3.00 Credits
This course provides an in-depth overview of important mathematical principles and methods that underlie state-of-the art data science, statistical, and machine learning methods with a focus on linear algebra and multivariate calculus and their data science applications. Additional coursework is required for those enrolled in the graduate-level course. Prerequisites/Restrictions: Graduate standing or: MATH 1210 STAT 3000 or MATH 5710 MATH 1220 and MATH 2270 are recommended Experience programming in Python, R, or Matlab is essential for success in the course Cross/Dual listed as: STAT 5645, MATH 5645, MATH 6645
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