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Course Criteria
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3.00 Credits
Multivariate distributions, change of variable technique, chi-square distribution, estimation, confidence intervals, hypothesis testing, contingency tables, goodness of fit. Practical applications are used to aid in the development.
Prerequisite:
MATH 363
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3.00 Credits
Vector spaces and linear transformations are studied in a theoretical setting. Also, canonical forms and multilinear algebra are studied.
Prerequisite:
MATH 171, 271 with a C or better grade
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3.00 Credits
An applied statistics course in descriptive statistics, statistical inference procedures, regression analysis, analysis of variance, and analysis of deviance. Inference procedures for population means and proportions are developed. Simple linear regression, multiple linear regression, one way ANOVA, two way ANOVA, Poisson regression, and logistic regression models are used to analyze data. Emphasizes the applied aspects of these statistical models and uses computer software for data analysis.
Prerequisite:
MATH 214, 216, or 217 or permission of the instructor
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3.00 Credits
An applied statistics course that focus on multivariate statistical methods. Research procedures on the relationship among variables, significance of group differences, prediction of group membership, and structure exploration are introduced. Factorial analysis of variance, analysis of covariances, multivariate analysis of variance and covariance, path analysis, factor analysis and discriminate analysis are introduced and used to analyze data. Emphasizes the applied aspects of these statistical methods and uses computer software for data analysis.
Prerequisite:
MATH 214, 216 or 217, or permission of the instructor
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3.00 Credits
Examines the current curricula and methods of instruction used in middle and secondary level mathematics classrooms. Follows an investigative approach to middle-level and secondary mathematics instruction through hands-on activities that are standards based. Explores methods of teaching in diverse classrooms and teaching students with special needs.
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3.00 Credits
Applied statistics course in the analysis and forecasting of time series data. Linear time seriesregression models, ARIMA models, SARIMA models, GARCH models, and spectral theory are usedto examine time series data. Emphasizes the applied aspects of these models. Computer software is used for data analysis.
Prerequisite:
MATH 363 or MATH 411 or ECON 356 or permission of the instructor
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3.00 Credits
Covers acquiring, managing, and analyzing massive unstructured data through a project-driven approach. Includes theoretical analysis of clustering, visualization, link analysis, recommendation systems, mining social network graphs, dimensionality reduction with PCA and SVD, large-scale machine learning, neural nets and deep learning, distributed file systems, incremental data processing with Hadoop, NoSQL databases, cloud computing, and data security issues. Covers applications in web advertising, business, engineering, health care and social networks. Implements a computational project utilizing machine learning and artificial intelligence techniques.
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3.00 Credits
Examines and develops expertise with sequences, patterns, and functions, including linear, quadratic, logarithmic, exponential, and trigonometric functions. Appropriate technology is incorporated throughout the course. Explores curricular materials, resources, and activities relevant to teaching diverse groups at the Elementary/Middle Level.
Prerequisite:
MATH 152
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3.00 Credits
Study of set theory, real number system, functions topology of Cartesian space, sequences, convergence and uniform convergence, continuity, and uniform continuity.
Prerequisite:
MATH 272 with a grade of C or better
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3.00 Credits
Includes the study of convergence sequences in Rn, global properties of continuity, uniform continuity, differentiation of Rn, Riemann integrals, and infinite series.
Prerequisite:
MATH 421
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