Courses

A performance workshop combining the study of technique and composition in preparation for a public dance presentation. May be repeated

, LST. 3. Pr. THEA 3850. Intensive study of advanced dance techniques in theory and practice. Course often serves as a training and preparation for public performance. Theatre majors who do not earn a grade of "C" or higher must repeat this course. Course may be repeated for a maximum of 12 credit hours.

An examination of statistical and research techniques appropriate to emergency management applied research, including descriptive statistics, probability theory, sampling and statistical inference, hypothesis testing, regression and correlation analysis, analysis of variance, modeling and techniques for data reduction (e.g. factor analysis).

Techniques address the approach to data analyses required to examine the problem student’s selected research topic will be practiced.

A methods-focused course covering the exploration, transformation, visualization, and analysis of data in the psychological sciences using widely or freely available software (e.g. Excel, Jamovi, and JASP). The course builds upon the skills taught in PSY 220 (Research Design & Analysis I) and PSY 320 (Research Design & Analysis II).

This course introduces computation-intensive statistical approaches and modern machine learning methods. Topics include: 1. R-computing and programming (intermediate to advanced) 2. Rmarkdown 3. Data scraping and text mining 4. Random number simulations 5. Bootstrapping 6. Optimization 7. Regression 8. Analysis of Variance 9. Classification, Clustering 10. Network data analysis

Students in this course will continue their exposure to data analytics by studying advanced statistical techniques and methods, managing and manipulating large data sets in order to produce actionable information, and communicating this information to interested parties.

This course provides an in-depth exploration of analytic technologies essential for business analytics professionals. Students will gain advanced skills in Excel, SQL, and Power BI, equipping them with the tools necessary to manage, analyze, and visualize data effectively. Emphasis will be placed on practical applications and real-world business scenarios.

Data mining; linear discriminant analysis (LDA), principal component analysis (PCA) and other methods; classification, clustering, and mining, information extraction; dealing with uncertainty, Bayesian inference; neural models, regression and feature selection.

This course covers advanced approaches used for writing crawlers and spiders, text analytics, sentiment analysis, social media analytics, network analytics, and deep learning for solving business and organizational problems. The course provides conceptual and hand-on understanding of such state-of-art analytics approaches using various python libraries. Knowledge of python programming is necessary to do well in the course.