Incoming Certificate Students

Focuses on management of IS/IT within the business environment. Topics include IT infrastructure and architecture, organizational impact of innovation, change management, human-machine interaction, and contemporary management issues involving data analytics. Class format includes lecture, group discussion, and integrative case studies.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall, Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Business Administration, Data Science, Applied Natural Resource Econ., Health Informatics, Accounting, Engineering Management

This course covers the four main paradigms of Computational Intelligence, viz., fuzzy systems, artificial neural networks, evolutionary computing, and swarm intelligence, and their integration to develop hybrid systems. Applications of Computational Intelligence include classification, regression, clustering, controls, robotics, etc.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: On Demand
• Restrictions: Permission of instructor required; Must be enrolled in one of the following Level(s): Graduate

Application, construction, and evaluation of statistical models used for prediction and classification. Topics include data pre-processing, over-fitting and model tuning, linear and nonlinear regression models and linear and nonlinear classification models.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring
• Pre-Requisite(s): MA 3740 or MA 4710 or MA 4720 or MA 4780 or (MA 4700 and MA 5701)

Introduces concepts and skills fundamental to Data Science including: getting data, data wrangling, exploratory data analysis, basic statistics, data visualization, data modeling, and learning. The course introduces data science from different perspectives: computer science, mathematics, business, engineering, and more.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-2)
• Semesters Offered: Fall, Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Data Science

Introduction to scientific and information visualization. Topics include methods for visualizing three-dimensional scalar and vector fields, visual data representations, tree and graph visualization, large-scale data analysis and visualization, and interface design and interaction techniques.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring
• Pre-Requisite(s): CS 4611 or CS 5611

This course will explore the foundational techniques of machine learning. Topics are pulled from the areas of unsupervised and supervised learning. Specific methods covered include naive Bayes, decision trees, support vector machine (SVMs), ensemble, and clustering methods.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring
• Restrictions: Permission of instructor required; May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
• Pre-Requisite(s): CS 4821

This covers fundamentals of computer security. Topics include practical cryptography, access control, security design principles, physical protections, malicious logic, program security, intrusion detection, administration, legal and ethical issues.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): CS 3411 or CS 4411

Students will learn computer programming skills in Perl for processing genomic sequences and gene expression data and become familiar with various bioinformatics resources.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall, in odd years
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

The theory of Bayesian inference. Topics include prior specifications, basics of decision theory, Markov chain, Monte Carlo, Bayes factor, linear regression, linear random effects model, hierarchical models, Bayesian hypothesis testing, Bayesian model selection.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, in even years
• Pre-Requisite(s): MA 4330 and MA 4710 and MA 4760

Statistical modeling and inference for analyzing experimental data that have been observed at different points in time. Topics include models for stationary and non stationary time series, model specification, parametric estimation, model diagnostics and forecasting, seasonal models and time series regression models.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): (MA 2710 or MA 2720 or MA 3710 or MA 3715 or MA 5701) and (MA 3720 or EE 3180 or MA 4700)

Introduces disruptive innovation concepts and provides occasions for their application to timely and relevant cases. Provides an understanding of technology management and innovation processes as they occur inside and outside of organizations.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring, Summer
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore

An overview of data analysis methods including visualization, data programming, and univariate statistics such as t-test and ANOVA.

• Credits: 3.0
• Lec-Rec-Lab: (0-2-2)
• Semesters Offered: Fall, in even years
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Set in a Linux environment, students will learn to design computational workflows, translate problems into programs, understand sources of errors, and debug, profile and parallelize the code. Successful completion of FOSS101 and earning its Digital Badge are required prior to registration

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Restrictions: Permission of instructor required; Must be enrolled in one of the following Level(s): Graduate