|DSCI 617||Big Data Analytics||3 Credits|
This course targets data scientists and engineers. It covers programming with RDDS, Tuning and debugging Spark, Spark SQL, Spark steaming and machine learning with MLlib. It provides students the tools to quickly tackle big data analysis problems on one machine or hundreds. Project-based learning is used to help students develop effective problem-solving skills and effective collaboration skills.
|DSCI 618||Experimental Design||3 Credits|
This course covers principles of experiments and basic statistics using SAS. Topics include analysis of variance, experimental designs, analysis of covariance, mixed model, categorical data analysis, survey data analysis, sample size and power analysis, and model comparison. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills. Note: This course is for graduate students only. Cross-listed: DSCI-318
|DSCI 619||Deep Learning||3 Credits|
This course is an introduction to deep learning with an emphasis on the development and application of advanced neural networks. It covers convolutional neural networks, recurrent neural networks, generative adversarial networks, and deep reinforcement learning. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills. Note: This course is for graduate students only. Cross-listed: DSCI-419
|DSCI 624||Data Visualization||3 Credits|
This course is intended for students with introductory experience in SQL, R, and Excel. In this course, students will learn how to connect SQL Server to tools like Excel and R and how to leverage the database engine to manipulate large amounts of data for data analysis tasks. Students will learn how to create analytical plots in Excel and R and how to follow best practices for creating report-quality graphs and presentations. Students will learn how to use tools like R Notebooks so that their analysis follows the reproducible research paradigm. Finally, students will learn to create simple web applications in R Shiny to build reporting and analytics dashboards. This course will be project based. By the end of the class, the students will have a portfolio of analytical work completed inside and outside of class.
|MATH 505||Statistical Modeling I||3 Credits|
Credits: Three (3) This course focuses on model development, interpretation, understanding assumptions and evaluation of competing models. Topics include basics of statistical learning, generalized linear models, time series models, principal components analysis and decision trees. Note: This course is for graduate students only. Cross-listed: MATH-405
|MATH 506||Statistical Modeling II||3 Credits|
This course covers the learning objectives from Examination STAM (Short-Term Actuarial Models) of the Society of Actuaries. Topics include constructing empirical models; estimating the parameters of failure time and loss distribution using different methods such as maximum likelihood method of moments, Kaplan-Meier estimator, Nelson- Aalen estimator and kernel density estimators; determining the acceptability of a fitted model; comparing models using graphical procedures, Kolmogorov-Smirnov test, Chi-square goodness of fit test, likelihood ratio test, Schwarz Bayesian criterion, and Akaike Information Criterion.
|MATH 570||Probability I||3 Credits|
This is the first in a sequence of two one-semester courses on probability. Topics include basic probability concepts, conditional probability, Bayes Theorem, distribution of random variables; moments, moment generating functions, percentiles, mode, skewness, univariate transformations, discrete distributions (binomial, uniform, hypergeometric, geometric, negative binomial, Poisson), and continuous distributions (uniform, exponential).
MATH 570 and MATH 571 (along with Calculus) cover all of the learning objectives contained in Examination P (Probability) of the Society of Actuaries. Note: This course is for graduate students only. Cross-listed: MATH-370
|MATH 571||Probability II||3 Credits|
This course should be taken in sequence with MATH 570. Topics include continuous distributions and their applications; uniform distribution, exponential distribution, Gamma distribution, normal distribution and others; central limit theorem; order statistics; mixed distributions; multivariate distributions; marginal distributions; conditional distributions; joint moment generating functions; double expectation theorems.
MATH 570 and MATH 571 (along with Calculus) cover all of the learning objectives contained in Examination P (Probability) of the Society of Actuaries. Note: This course is for graduate students only. Cross-listed: MATH-371 Prerequisite:MATH-570
|MATH 572||Mathematical Statistics||3 Credits|
This course introduces students to basic concepts of inference and main methods of estimation. Topics include statistical inferences such as point and interval estimation of parameters, statistical hypotheses tests and ANOVA; inferences for single samples; inference for two samples; inferences for proportion and count data; and advance estimation methods including Moment, percentile matching, Maximum Likelihood, Bayesian. This course emphasizes the applications of the theory to statistics and estimation. This is a calculus-based one semester course. Project based learning is used to help students develop effective problem-solving skills and effective collaboration skills.
Students who receive a B- or higher in this course are eligible to receive VEE (Validation by Education Experience) credit from the Society of Actuaries in Mathematical Statistics. Note: This course is for graduate students only. Cross-listed: MATH-372 Prerequisite: MATH-571