STAT 1127. Introductory Statistics (3-0-3) Prerequisite: Satisfactory score on math placement exam or completion of any of the following courses with a grade of "C" or better: MATH 1001, MATH 1101, MATH 1111, MATH 1113, MATH 1125, or MATH 1131. Survey of modern statistical methods applicable to behavioral, biological, health and managerial sciences, and education. Organization and analysis of data, probability distributions, sampling distributions, point estimation, confidence interval, hypothesis testing, and regression analysis. (Course fee required.)
STAT 3127. Statistical Computing (3-0-3) Prerequisite: Prerequisite: STAT 1127, or BUSA 3115, or CRJU 3107 with a minimum grade of C. The goal of this course is to provide students with an introduction to statistical programming for data management, analysis, and reporting, and familiarize students with practical issues related to the exploration of actual data sets. This course introduces the most commonly used features of one of several popular statistical packages, especially in examining, transforming, and analyzing data (linear regression, ANOVA, and dummy variable regression).
DSCI 3111. Data Mining I (3-0-3) Prerequisite: STAT 3127 with a minimum grade of C. This course identifies the importance of adequately preparing data for data modeling and predictive analytics. Topics include data retrieval, merging and organization, data cleaning and data visualization.
DSCI 3112. Data Mining II (3-0-3) Prerequisite: DSCI 3111 with a minimum grade of C. This course investigates the methods for selecting among multiple data models and for evaluating model selection. Topics include logistic regression, model evaluation techniques, cost-benefit analysis using mis-classification costs, graphical evaluation of classification models, association rules and CART models.
DSCI 3116. Ethics and Data Analytics (3-0-3) Prerequisite: DSCI 3112 with a minimum grade of C. This course investigates characteristics of ethical design of algorithms for predictive models. Topics include opacity, scale and potential damage of data mining algorithms, data accuracy, stereotyping, and proxy variables; data privacy and security.