• MATH 4637: Stat Analysis/App Big Data

  3.00 Credits

  University of Memphis

  
  Introduction to statistical modeling and anlysis with some applications to big data. Topics include: linear models - regression, analysis of variance, multiple comparisons, shrinkage methods, and model checking; dimension reduction methods; discriminant analysis and clustering; and Bayesian modeling and computation. Some knowledge of programming is useful but not required. COREQUISITE: MATH 4614 or 4636.
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• MATH 4640: Intro Probability Models

  3.00 Credits

  University of Memphis

  
  Introduction to Markov chains; Limity Probabilities, Branching processes, applications to modeling population growth and spread of infectious diseases; Counting and Poisson processes; Continuous-Time Markov Chaisn, Birth and Death Processes, applications to queuing models. PREREQUISITE: MATH 4635 with a minimum grade of C-.
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• MATH 4643: Intro Regression/Time Ser Anyl

  3.00 Credits

  University of Memphis

  
  Hypothesis testing and confidence intervals for linear regression models, examination of residuals, calculation of elasticities and partial correlations, heteroscedasticity, serial correlation, multicolinearity, non-linearity, deterministic and stochastic time series models, stationary time series and autocorrelation functions, diagnostic checks, forecasting using ARIMA models. PREREQUISITE: MATH 4636 with a minimum grade of C-.
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• MATH 4685: Statistical Learning I

  3.00 Credits

  University of Memphis

  
  First course in a two course sequence designed to introduce students to a set of tools used in the modeling and understanding of big data. The two part course introduces statistical methods in the context of developments in computer science. Emphasis is on application to real data sets. Topics include linear regression, shrinkage methods, classification algorithms, principle component analysis, support vector algorithms and clustering methods. PREREQUISITE: MATH 4635 with a minimum grade of C-.
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• MATH 4686: Statistical Learning II

  3.00 Credits

  University of Memphis

  
  Second course in a two course sequence designed to introduce students to a set of tools used in the modeling and understanding of big data. The two part course introduces statistical methods in the context of developments in computer science. Emphasis is on application to real data sets. Topics include cross-validation, Bayesian inference and computation, and non-parametric methods. PREREQUISITE: MATH 4685 with a minimum grade of C-.
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• MATH 4692: Exp.in Data Science

  3.00 Credits

  University of Memphis

  
  A capstone course designed to fuse the core concepts and skills from computer science and the mathematical sciences learned throughout the data science program. Computational, analytic, interpretive and communication skills will be used to design and execute a full data science project. The course will be co-taught by computer science and mathematical sciences. PREREQUISITE: permission of the instructors.
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• MATH 4721: Numerical Analysis

  3.00 Credits

  University of Memphis

  
  Derivation and application of computer-oriented, numerical methods for functional approximation, differentiation, quadrature, and solution of ordinary differential equations. PREREQUISITE: MATH 2120 with a minimum grade of C- and MATH 3242 with a minimum grade of C- and knowledge of some structured programming language.
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• MATH 990: Foundations Of Geometry

  3.00 Credits

  University of Memphis

  
  No course description available.
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• MATH UALD: Mathematics

  3.00 - 99.00 Credits

  University of Memphis

  
  No course description available.
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• MATH UAUD: Mathematics

  3.00 - 99.00 Credits

  University of Memphis

  
  No course description available.
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