MATH 370 | 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). This course is calculus—based. |
MATH 371 | Probability II | 3 Credits |
This is the second in a sequence of two one-semester courses on probability. Topic includes probability function and probability density function of one continuous random variable such as exponential distribution, normal distribution, Gamma distribution, beta distribution, and log normal distribution; mixed distributions; joint probability functions and joint probability density functions; conditional probability and marginal probability distributions; central limit theorem; joint moment generating and transformations; covariance and correlation coefficients. This course is calculus based. |
MATH 372 | Mathematical Statistics | 3 Credits |
This is the second in a sequence of two one-semester courses on probability. Topic includes probability function and probability density function of one continuous random variable such as exponential distribution, normal distribution, Gamma distribution, beta distribution, and log normal distribution; mixed distributions; joint probability functions and joint probability density functions; conditional probability and marginal probability distributions; central limit theorem; joint moment generating and transformations; covariance and correlation coefficients. This course is calculus based. |
DSCI 318 | Experimental Design | 3 Credits |
This course covers principles of experiments and basic statistics using R. 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. |
DSCI 408 | Machine Learning | 3 Credits |
This is an introductory course in machine learning intended primarily for students majoring or minoring in Mathematics, Data Science or Actuarial Science. This course may also be useful for those using predictive modeling techniques in business, economics or research applications. The main focus of this course is to understand the basic operations and applications of what we currently call machine learning. This course will cover material from several sources. A few main topics that will be covered include: how machine learning differs from traditional programming techniques, data manipulation and analysis, some basic coding skills and an introduction to some of the tools available for data scientists. Specific application techniques will include the following (as time permits): data acquisition, classification, regression, overfitting, supervised and unsupervised training, normalization, distance metrics, k-means clustering, error calculation, optimization training, tree-based algorithms (including random forests), frequent item sets and recommender systems, sentiment analysis, neural networks, genetic algorithms, visualizations, and deep learning (including an introduction to convolutional neural networks and generative adversarial networks). Cross-Listed: DSCI-508 |
DSCI 412 | Predictive Modeling | 3 Credits |
This course introduces students to fundamental statistical learning techniques that can be applied to real-world business problems. Topics include generalized linear models, tree-based models, clustering methods, and principal components analysis. It trains students to understand key steps and considerations in building predictive models, selecting a best model, and effectively communicating the model results. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills. Cross-listed: DSCI-512 |
DSCI 417 | 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. |