Mathematics
Graduate Faculty
Chris Ahrendt, Ph.D.
Julian Antolin Camarena, Ph.D.
Mohammad Aziz, Ph.D.
Allison Beemer, Ph.D. (Program Co-Director)
Abra Brisbin, Ph.D. (Chair)
Abhinav Chand, Ph.D.
Christopher Davis, Ph.D.
Herschel Day, F.S.A.
Colleen Duffy, Ph.D.
Marc Goulet, Ph.D.
Jennifer Harrison, Ph.D.
Ryan Harrison, Ph.D.
Christopher Hlas, Ph.D.
Marie-Claire Koissi-Kouassi, Ph.D. (Program Co-Director)
Jessica Kraker, Ph.D.
Chloe Lewis, Ph.D.
aBa Mbirika, Ph.D.
Carolyn Otto, Ph.D.
Kristopher Presler, F.S.A.
Katrina Rothrock, Ph.D.
Sam Scholze, Ph.D.
Feroz Siddique, Ph.D.
Wufeng Tian, Ph.D.
Melissa Troudt, Ph.D.
Vicki Whitledge, Ph.D.
All 500- and 600-level graduate courses include requirements or assignments which differentiate them from their companionate 300- and 400-level undergraduate offerings. Students who have taken a course at the 300- or 400-level may not include that course at the 500- or 600-level in a graduate program, except in the case of special topics courses when the topic is not the same as that taken at the undergraduate level.
Data Science (DS)
DS 700 Foundations of Data Science (3 crs)
Prerequisite: Limited to Data Science master's degree students.
Introduction to data science and its importance in business decision making.
Attributes: Data Science MS OL Flat Rate Tuition, Special Course Fee Required
Grading Basis: A-F Grades Only
Lecture/Discussion Hours: 3
Lab/Studio Hours: 0
DS 701 Exploratory Data Analysis (3 crs)
Prerequisite: Limited to Data Science master's degree students.
•Data Science MS OL Flat Rate Tuition
This course introduces data science and highlights its importance in decision making. Students will learn how to analyze data using the R programming language. During the course, students will learn how to import data into R, tidy it, conduct exploratory data analysis, develop visualizations, and draw statistical inferences. The course aims to teach data wrangling, visualization and exploration with R.
Attributes: Data Science MS OL Flat Rate Tuition, Special Course Fee Required
Grading Basis: A-F Grades Only
Lecture/Discussion Hours: 3
Lab/Studio Hours: 0
DS 705 Statistical Methods (3 crs)
Prerequisite: DS 700 or DS 701. Limited to Data Science master's degree students.
Statistical methods and inference procedures presented with an emphasis on applications, computer implementation, and interpretation of results.
Attributes: Data Science MS OL Flat Rate Tuition, Special Course Fee Required
Grading Basis: A-F Grades Only
Lecture/Discussion Hours: 3
Lab/Studio Hours: 0
DS 710 Programming for Data Science (3 crs)
Prerequisite: Limited to Data Science master's degree students.
Introduction to programming languages and packages used in data science.
Attributes: Data Science MS OL Flat Rate Tuition, Special Course Fee Required
Grading Basis: A-F Grades Only
Lecture/Discussion Hours: 3
Lab/Studio Hours: 0
DS 715 Data Warehousing (3 crs)
Prerequisite: Limited to Data Science master's degree students.
Introduction to the concepts and techniques to work with and reason about subject-oriented, integrated, time-variant, and nonvolatile collections of data in support of management’s decision-making process.
Attributes: Data Science MS OL Flat Rate Tuition, Special Course Fee Required
Grading Basis: A-F Grades Only
Lecture/Discussion Hours: 3
Lab/Studio Hours: 0
DS 716 Data Management for Data Science (3 crs)
Prerequisite: Limited to Data Science master's degree students.
•Data Science MS OL Flat Rate Tuition
This course explores the various approaches for data management used in data science. We present how data is collected, transformed, stored, and delivered for use in data science projects.
Attributes: Data Science MS OL Flat Rate Tuition, Special Course Fee Required
Grading Basis: A-F Grades Only
Lecture/Discussion Hours: 3
Lab/Studio Hours: 0
DS 730 Big Data: High Performance Computing (3 crs)
Prerequisite: DS 710. Limited to Data Science master's degree students.
Overview of how to process large datasets efficiently, including introduction of non-relational databases.
Attributes: Data Science MS OL Flat Rate Tuition, Special Course Fee Required
Grading Basis: A-F Grades Only
Lecture/Discussion Hours: 3
Lab/Studio Hours: 0
DS 735 Communicating about Data (3 crs)
Prerequisite: Limited to Data Science master's degree students.
Prepares students to master technical, informational, and persuasive communication to meet organizational goals.
Attributes: Data Science MS OL Flat Rate Tuition, Special Course Fee Required
Grading Basis: A-F Grades Only
Lecture/Discussion Hours: 3
Lab/Studio Hours: 0
DS 740 Data Mining & Machine Learning (3 crs)
Prerequisite: DS 705. Limited to Data Science master's degree students.
Data mining methods and procedures for diagnostic and predictive analytics.
Attributes: Data Science MS OL Flat Rate Tuition, Special Course Fee Required
Grading Basis: A-F Grades Only
Lecture/Discussion Hours: 3
Lab/Studio Hours: 0
DS 745 Visualization and Unstructured Data Analysis (3 crs)
Prerequisite: Limited to Data Science master's degree students.
Covers various aspects of data analytics including visualization and analysis of unstructured data such as social networks.
Attributes: Data Science MS OL Flat Rate Tuition, Special Course Fee Required
Grading Basis: A-F Grades Only
Lecture/Discussion Hours: 3
Lab/Studio Hours: 0
DS 750 Data Storytelling (3 crs)
Prerequisite: DS 700 or DS 701. Limited to Data Science master's degree students.
•Recommended Additional Prerequisites: DS 705 OR DS 740 suggested but not required. Data Science MS OL Flat Rate Tuition
Data storytelling involves using data to tell a compelling narrative that helps audiences understand, engage with, and act on the information. This course combines data analysis with communication techniques to present data in an informative and engaging way. This course is specifically designed as a graduate-level requirement for the MSDS degree, focusing on teaching students how to effectively communicate insights through data storytelling techniques. Participants will learn to craft engaging stories that resonate with various audiences and drive decision-making.
Attributes: Data Science MS OL Flat Rate Tuition, Special Course Fee Required
Grading Basis: A-F Grades Only
Lecture/Discussion Hours: 3
Lab/Studio Hours: 0
DS 760 Ethics of Data Science (3 crs)
Prerequisite: Limited to Data Science master's degree students.
Ethical issues related to data science, including privacy, intellectual property, security, and the moral integrity of inferences based on data.
Attributes: Data Science MS OL Flat Rate Tuition, Special Course Fee Required
Grading Basis: A-F Grades Only
Lecture/Discussion Hours: 3
Lab/Studio Hours: 0
DS 770 Ethical Decision-Making Using Data (3 crs)
Prerequisite: Limited to Data Science master's degree students.
•Recommended Prerequisite: DS 740, suggested but not required. Data Science MS OL Flat Rate Tuition
This course examines how data science relates to developing strategies for organizations. The emphasis is on using an organization’s data assets to inform better decisions. The course investigates the use of data science findings to develop solutions to competitive organizational challenges. Special attention is given to critically examining decisions to ensure that they are ethical and avoid unfair bias. Professional codes of conduct as well as local and international regulations are also considered.
Attributes: Data Science MS OL Flat Rate Tuition, Special Course Fee Required
Grading Basis: A-F Grades Only
DS 775 Prescriptive Analytics (3 crs)
Prerequisite: Limited to Data Science master's degree students.
Procedures and techniques for using data to inform decision making. Topics include optimization, decision analysis, game theory, and simulation.
Attributes: Special Course Fee Required
Grading Basis: A-F Grades Only
Lecture/Discussion Hours: 3
Lab/Studio Hours: 0
DS 776 Deep Learning (3 crs)
Prerequisite: DS 710 and DS 740. Limited to Data Science master's degree students.
•Data Science MS OL Flat Rate Tuition
Introduction to the theory and applications of deep learning. The course begins with the study of neural networks and how to train them. Various deep learning architectures are introduced including convolutional neural networks, recurrent neural networks, and transformers. Applications may include image classification, object detection, and natural language processing. Algorithms will be implemented in Python using a high-level framework such as Pytorch or TensorFlow.
Grading Basis: A-F Grades Only
DS 780 Data Science and Strategic Decision Making (3 crs)
Prerequisite: Limited to Data Science master's degree students.
The interaction between data science and strategic decision making. Leveraging data resources for competitive advantage in the marketplace.
Attributes: Data Science MS OL Flat Rate Tuition, Special Course Fee Required
Grading Basis: A-F Grades Only
Lecture/Discussion Hours: 3
Lab/Studio Hours: 0
DS 785 Data Science Capstone (3 crs)
Prerequisite: DS 715 or DS 716; DS 730; DS 740; DS 750 or completion of 27 credits. Limited to Data Science master's degree students.
• Full-time equivalent.
Capstone course; students will develop and execute a data science project using real-world data and communicate results to a non-technical audience.
Attributes: Data Science MS OL Flat Rate Tuition, Special Course Fee Required
Grading Basis: A-F Grades Only
Lecture/Discussion Hours: 3
Lab/Studio Hours: 0
Mathematics (MATH)
MATH 691 Special Topics (1-4 crs)
• Dual-listed with MATH 491.
A variable content course designed to allow a breadth of study through investigation of mathematical topics not covered in other courses. Special interests of instructors will be utilized to provide topics.
Repeat: Course may be repeated for a maximum of 9 credits
Grading Basis: No S/U Grade Option
MATH 694 Mathematics Seminar (1 cr)
• Dual-listed with MATH 494.
An intensive study of selected topics in mathematics. The exact topics to be studied will vary according to the interests of the professor and the seminar participants.
Repeat: Course may be repeated for a maximum of 3 credits
Grading Basis: No S/U Grade Option
Lecture/Discussion Hours: 1
Lab/Studio Hours: 0
MATH 797 Independent Study (1-3 crs)
Consent: Department Consent Required
Individual project under the direction of a faculty member.
Repeat: Course may be repeated
Grading Basis: No S/U Grade Option
MATH 798 Graduation Only (1 cr)
Repeat: Course may be repeated
Grading Basis: PR Only Grade Basis