BZAN/STAT 645: Advanced Topics in Data Mining (graduate level). Selected topics in data mining. Read and critique current literature. Solve research problems motivated by real applications. (RE) Prerequisite(s): BZAN 552 or permission of instructor.
BZAN 557: Text Mining (graduate level). This course will introduce computational methods, rooted from machine learning, natural language processing, and statistics, to find patterns in large text corpora, unlocking the power of large amounts of text data. This course is designed to be a general introductory level course for all graduate students who are interested in text mining.
BZAN 552 / STAT 576: Multivariate and Data Mining Techniques (graduate level). Multivariate normal distribution, data visualization, handling missing data, dimension reduction techniques, supervised learning, clustering, outlier detection, including a team-based project and common data mining software. (RE) Prerequisite(s): 572 and 574. Prior knowledge may satisfy prerequisite with consent of instructor.
BZAN 542 / STAT 574: Data Mining Methods for Business Applications (graduate level). Understanding and application of data mining methods. Data preparation; exploratory data analysis and visualization; cluster analysis; logistic regression; decision trees; neural networks; association rules; model assessment; and other topics. Applications to real world data. Use of standard computer packages. (RE) Prerequisite(s): 532 or 538 or 571 or consent of instructor.
BAS 474: Data Mining and Business Analytics (undergraduate level). Understanding and application of data mining methods. Data preparation, exploratory data analysis and visualization, cluster analysis, logistic regression, decision trees, neural networks, association rules, model assessment, and other topics. Applications to real world data. Use of standard computer packages. (RE) Prerequisite(s): 320 with grade of C or better or Economics 381 with grade of C or better or consent of instructor.
STAT 201: Introduction to Statistics (undergraduate level). This is an introductory course appropriate for a general audience. Topics include: Data collection and descriptive statistics. Concepts of probability and probability distributions. Binomial and normal distributions. Estimation of means, confidence intervals, and hypothesis tests for single mean and proportion. Simple regression and correlation. Contingency tables. Process improvement and statistical process control. Use of statistical computing software.
29:623:340 Business Research Methods (undergraduate level). This course is intended to provide the students fundamental ideas, concepts and reasoning of basic statistics for business research. We explore methods and strategies for producing data that can give clear answer to specific questions, analyzing data using graphs and numerical summaries, and describing chance, variation and risk, which can help us make inferences and draw conclusions.