|MATH 311||Discrete Mathematics||3 Credits|
This course introduces students to basic discrete mathematics concepts. Topics include logic, elementary number theory methods, number systems, sets, functions and relations, counting and probability, theories of graphs and trees, and analysis of algorithm efficiency.
|MATH 330||College Geometry||3 Credits|
This course is required for Secondary Education students specializing in math. It is also taken by math majors who are interested in geometry or who want to gain experience in writing proofs before they attempt more advanced math courses. Topics include triangles congruence, polygons, Pythagorean Theorem, formal/informal proofs, coordinate systems, conic sections, and transformations.
|MATH 405||Statistical Modeling I||3 Credits|
This course focuses on model development, interpretation, understanding assumptions and evaluation of competing models. Topics include the basics of statistical learning, linear models, and time series models. This course covers a majority of the learning objectives for the Society of Actuaries (SOA) examination SRM (Statistics for Risk Modeling).
|DSCI 307||SAS Programming||3 Credits|
This class is an introduction to the SAS programming language. Topics include reading, exporting, sorting, printing, and summarizing data; modifying and combining data sets; writing flexible code with the SAS macro facility; visualizing data; and performing descriptive and basic statistical analyses such as Chi-square tests, T-Tests, ANOVA, and regression. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills. Cross-Listed: DSCI-507
|DSCI 314||Natural Language Processing||3 Credits|
This course covers text analytics, the practice of extracting useful information hidden in unstructured text such as social media, emails and web pages using Python. Topics include working with corpora, transformations, metadata management, term document matrices, word clouds, and topic models. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
|DSCI 318||Experimental Design||3 Credits|
This course covers principles of experiments and basic statistics using SAS. 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 324||Data Visualization||3 Credits|
This course is intended for students with introductory experience in SQL, R, and Excel. In this course, students will learn how to connect SQL Server to tools like Excel, R, and Tableau, and how to leverage the database engine to manipulate large amounts of data for data analysis tasks. Students will learn how to create analytical plots in Excel and R and how to follow best practices for creating report-quality graphs and presentations. Students will learn to use R Markdown so that their analysis follows the reproducible research paradigm. Finally, students will learn to build reporting and analytics dashboards in Shiny and Tableau. This course will be project-based. By the end of the class, the students will have a portfolio of analytical work completed inside and outside of class.
|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