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Course Criteria
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3.00 Credits
This course teaches analysis of balanced experimental designs with fixed, random, crossed, and nested factors; factorial, nested, nested factorial, split plot, split block, and repeated measures designs; and fixed and mixed effects models, residual analysis, and post hoc mean comparisons. Prerequisite: C- or better in STAT 2000 or 3000.
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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 6500
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2.00 Credits
Students learn basic construction and refinement of statistical graphics for categorical and low-dimensional data via various R packages. They discuss good and bad graphics design principles with examples drawn from everyday sources and the scientific literature.
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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 6555 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 6560 Prerequisite: STAT 5050 & 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 6570 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 6645, MATH 5645, MATH 6645
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2.00 Credits
Students learn principal components analysis, discriminant analysis, logistic regression, nearest neighbor classification, classification trees, random forests, and are introduced to AdaBoost, gradient boosting machines, and support vector machines. They also learn agglomerative and k-means clustering, canonical correlation, and multivariate regression. Prerequisite: STAT 5100 with a C- or better
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3.00 Credits
This course takes a principled and hands-on approach to deep learning with neural networks, covering machine learning basics, backpropagation, stochastic gradient descent, regularization, and universality. Topics include CNNs, GANs, RNNs, GCNs, autoencoders, transformers, and other modern architectures and training techniques. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite Recommendation(s): The following courses or their equivalents are necessary to succeed in this course: MATH 1220; MATH 2270 or MATH 5645/STAT 5645 or MATH 6645/STAT 6645; STAT 3000 or MATH 5710; Programming experience, preferably in Python, is also necessary to succeed in this course Dual-listed as: STAT 6685 Repeatable for credit: No Grade Mode: Standard
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