Course requirements can be found at the link below:
(X = 0, 1, or 2)
Offered occasionally
DSA 652, ACM 604, ACM 610, ACM 611, ACM 62X, ACM 650, ACM 652, ACM 653
ACM 690 can be taken in fall, spring, or summer
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Prerequisites: Instructor permission or admission to the Professional Applied and Computational Mathematics (PACM) Program.
Processes of mathematical modeling, use of dimensional analysis, scaling, and elementary perturbation methods; constrained and unconstrained optimization, sensitivity analysis, derivation and analysis of system of discrete dynamical models.
Prerequisites: Instructor permission or admission to the Professional Applied and Computational Mathematics (PACM) Program.
Mathematical modeling and applications of differential equations, simulation of dynamical systems, and partial differential equations.
Prerequisites: Instructor permission or admission to the Professional Applied and Computational Mathematics (PACM) Program.
Applications of series and integral transforms, and the study and simulations of probability models.
Prerequisites: Instructor permission.
Topics include continuous distributions, sampling distributions, point and interval estimation, and tests of hypotheses. Offered occasionally.
Prerequisite: Admission to program or instructor permission.
Problem solving and applications of continuous mathematics, including real analysis, single and multivariable calculus, differential equations, optimization, and Fourier analysis. Emphasis on team building and group management through problem-solving activities.
Prerequisite: Admission to program or instructor permission.
Problem solving and applications of discrete mathematics, including combinatorics, graph theory, logic, linear algebra, number theory, and set theory. Emphasis on team building and group management through problem-solving activities.
Prerequisite: Admission to program or instructor permission.
Introduction to algorithm design to implement mathematical models, procedural, and functional programming, programming paradigms, higher-level languages; statistical and visualization software, typesetting software for science and mathematics.
Prerequisites: Instructor permission.
Applications of spreadsheet and database software programming to solve real life problems in computational mathematics. Analysis of data to produce reports and presentations for diverse audiences.
Prerequisite: Instructor permission.
Survey of statistical and data programming software and applications to real life problems in computational mathematics. Analysis of data to produce reports and presentations for diverse audiences with a focus on understanding the syntax and use of statistical programming languages.
Prerequisite: Admission to the program or instructor permission.
Mathematical analysis and solution of real-world problems that optimize linear objective functions subject to systems of linear inequalities; the two-phase revised simplex method; applications in diverse areas such as business management, industry, economics, finance, and game theory.
Prerequisite: Admission to program or instructor permission.
Exploratory data analysis, polynomial interpolation, curve fitting, least squares, cubic splines, minimax polynomial, Taylor and Chebyshev series, applications to fitting experimental data.
Prerequisite: Admission to program or instructor permission.
Difference equations, systems of differential equations, Euler and Runge-Kutta methods, error analyses, logistic models; applications to ecology, finance, conflicts, natural and social sciences.
Prerequisite: Admission to program or instructor permission.
Numerical algorithms for linear algebra problems, matrix operations, matrix decompositions, solving systems of linear equations, selected problems from applied settings.
Prerequisite: Admission to program or instructor permission.
Numerical algorithms for eigenvalue problems, matrix factorization, matrices, vectors, eigenvalues, eigenvectors, eigenspaces, eigenvalue algorithms, selected problems from applied settings.
Prerequisite: Admission to program or instructor permission.
Numerical methods and algorithms for finding roots of non-linear equations, numerical integrals, Fourier series and Laplace transform; selected problems from applied settings.
Prerequisite: Admission to program or instructor permission.
Simple linear regression and correlation, multiple linear regression, multicollinearity, multiple and partial correlations, confounding and interaction, sequential methods of model selection.
Prerequisite: Admission to program or instructor permission.
Design of experiments (one, two and three factors), multiple comparisons, randomized complete block designs, Latin square design.
Prerequisite: Admission to program or instructor permission.
Introduction to nonparametric tests such as sign-test, signed rank test, rank sum test, two-way analysis of variance by ranks, tests of randomness, rank correlation coefficient.
Prerequisite: Admission to program or instructor permission.
Symmetric random walks, ballot theorem, returns to origin and arcsine laws, gambler's ruin, Brownian motion, conditional distributions, hitting times and maxima.
Prerequisite: Admission to program or instructor permission.
Transition matrices, classification of states, limiting probabilities, applications.
Prerequisite: Admission to program or instructor permission.
Exponential distribution, Poisson, Yule, pure birth, birth and death processes, applications.
Prerequisites: Graduate standing.
Practical introduction to mortgage lending and the practice of measuring and managing consumer credit risk. Introduction to Markov chain theory and transition roll rate modeling through extensive case study of the collapse of the U.S. mortgage industry in 2007 - 2008 and the origins of the Great Recession. Risk reporting and segmenting; probability of default; loss given default; house price dynamics; loss forecasting with consideration of micro and macro-factors. Use of statistical software package SAS to analyze loan-level datasets. Suggested preparation: previous coursework or experience in calculus, linear algebra, linear regression, and introduction to programming.
Prerequisites: Instructor permission or admission to the Professional Applied and Computational Mathematics Master program.
In-depth study of probability, differential equations and numerical analysis and their connections to finance and economics; put-call parity equation; risk-neutral probability; binomial tree analysis.
Prerequisites: Instructor permission or admission to the Professional Applied and Computational Mathematics Master program.
Additional study of probability, differential equations and numerical analysis and their connections to finance and economics; Black-Scholes equation, risk-neutral probability, Brownian motion, hedging, continuous and discrete stochastic models.
Prerequisite: ACM 640 or instructor permission.
Comparison of linear and logistic regression, multiple logistic regression, regression diagnostics, indicator variables, multicollinearity, confounding and interaction, model selection, maximum likelihood techniques, polychotomous logistic regression.
Prerequisite: ACM 640 or instructor permission.
Survival and hazard functions, life tables, Kaplan-Meier survival analysis, Cox regression proportional hazards model and Cox regression with time-dependent variables; comparison with logistic regression approaches.
Prerequisite: ACM 640 or instructor permission.
Time and frequency domain techniques including autocorrelation, spectral analysis, autoregressive moving average and integrated moving average models, Box-Jenkins methodology, fitting, forecasting and seasonal adjustments.
PSM 601 PROJECT MANAGEMENT FOR MATH AND SCIENCE PROFESSIONALS
Prerequisites: Graduate standing.
Current practices in project management as applied to math and science projects. Hands-on experience with the skills, tools, and techniques required in different phases of a project's life cycle, including project selection, project planning, project staffing and organization, task scheduling, project scope management, budgeting and progress reporting, risk management, quality management, project communications, and use of appropriate project management software tools. Techniques for communicating and motivating teams throughout the project life cycle. Emphasis on team building and practicing project management techniques through the use of science-based cases.
PSM 602 COMMUNICATION STRATEGIES FOR MATH AND SCIENCE PROFESSIONALS
Prerequisites: Graduate-level standing.
Intend to develop strategic thinking about communication of quantitative information and improve writing, presentation, and interpersonal communication skills for mathematicians and scientists in a variety of settings (i.e. industrial, managerial, academic, research). Includes a review of “best practices” or guidelines that have been derived from both research and experience. Students will put those guidelines into practice, using a workshop format that will rely heavily on discussion and in-class exercises.
ACM 690 MASTER'S PROJECT
Prerequisite: Written approval of faculty adviser and department chair.
Research or investigation of a particular problem, planned and carried out under the guidance of a qualified member of the graduate faculty, submitted in acceptable form according to directions given by the Mathematics Department.
Prerequisite: Instructor permission.
Practical hands-on introduction to Data Science and Data Analytics tools and acquiring, storing, manipulating, and exploring data - both big and small. Examples from bioinformatics (e.g., genomics), health care informatics, urban and regional planning, astronomy and data journalism. Extensive writing of formal reports. Offered late summer session.
Prerequisites: MAT 126, MAT 311, CIS 512, or Instructor permission
Applied introduction to building predictive, machine-learning models for real-world problems; learning Python computing environment, basic data analysis, management; data visualization and reporting using machine learning methods, including k-nearest neighbor, linear models, naïve Bayesian models, decision trees, random forests, and neural networks. Sample data sets from across industry professions.
Prerequisite: Graduate status.
Introduction to a “big picture” understanding of data flow for strategic, data-driven decision making, including data storage, data organization, data gathering and preparation, exploratory data analysis, and meaningful visualizations and communication. Includes hands-on practice. Offered occasionally, beginning spring 2020.
Prerequisites: Instructor permission
Elements, methods and tools of an organization’s data strategy and its governance. Components of a data strategy for each phase in the data lifecycle, tools for executing the strategy, and aligning the data strategy with the emerging needs of the organization. Policies, procedures, standards, and training for establishing authority over the ownership and use of data assets and its security.
Prerequisites: Undergraduate courses in MAT 126, MAT 311 and CIS 151 or instructor permission.
Introduction to key concepts and applications of time series analysis for bank risk management data-driven decision-making. Analysis, decomposition, segmentation, model selection and estimation, statistical and hypothesis testing, and forecasting and sensitivity testing. Use of actual datasets for applied analysis; revenue forecasting future scenarios; interactive classroom instruction in SAS programming environment. Offered occasionally.
Prerequisites: Graduate standing.
Introduction to Data Science and Analytics; modern analytical techniques; application to academia, industry and business needs. Fundamental concepts and terms; methods, tools, and techniques; identification of “big data” problems; data sources; analytical approaches; algorithm implementations; interpretation and reporting of results. Offered annually in the Fall semester.
Prerequisites: CIS 512 or DSA 512 or equivalent.
Introduction to Machine Learning Techniques for Data Science; mathematical methods; algorithms; application to academia, industry and business problems. Fundamental concepts and terms; methods, tools, and techniques. Supervised and unsupervised learning; identification of learning problems; data sources; analytical approaches; algorithm implementation; interpretation and reporting. Offered annually in the Fall semester.
Prerequisites: Instructor permission, programming experience required
Introduction to Python programming focusing on the development of Python scripts and custom tools for processing and analysis of geospatial data. Automating geoprocessing workflows, creating custom analysis tool, and customizing user interfaces.
Prerequisite: Instructor permission.
Exploration of publicly available data and user-generated data; application of communication theory and research in data analytics; attention to tools and methods in communication data analytics; focus on ethical responsibilities of data scientists; intensive practice in using statistical software and Python.
Prerequisite: Instructor permission.
This course will cover the fundamentals of effective data-driven storytelling. Students will learn how to analyze data, detect stories within datasets and communicate findings in oral, written, and interactive visual delivery modes for various audiences.
Prerequisite: Graduate status.
Concepts and analytical techniques of comprehensive systems for operations management; quantitative methods in practical situations; modeling, computer interactive analysis, and nonsteady state situations; data streams; sthing; forecasting; cyclic components; feedback.
Professional Applied and Computational Mathematics
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