Ultimately, data drives investment. Behind nearly every great enterprise is an analytical mind. This department is home to our renowned Applied Statistics, Management Information Systems, and Operations Management programs. So whether it's the ability to detect statistical aberrations in an information system or designing a system in which aberrations are not possible, the primary objective of the Department of Information Systems, Statistics, and Management Science is to offer high-quality programs designed to prepare students for careers in both the public and private market sector.
Faculty
Head
- Dula, Jose
Professors
- Chakraborti, Subha
- Cochran, James
- Dula, Jose
- Gray, Brian
- Hale, David
- Hale, Joanne
- Keskin, Burcu
- Lodree, Emmett
- McManus, Denise
- Melnykov, Volodymyr
- Melouk, Sharif
- Mittenthal, John
- Perry, Marcus
- Raja, Uzma
- Thatcher, Jason
Associate professors
- Barrett, Bruce
- Carter, Michelle
- Johnston, Allen
- Wang, Qin
- Yavuz, Mesut
Assistant professors
- Bott, Gregory
- Chen, Yuanyuan
- Dayarian, Iman
- Freeman, Nick
- Hudnall, Matthew
- Jena, Rishi
- Kim, Youngsoo
- Lee, Danhyang
- Melnykov, Yana
- Parton, Jason
- Saifee, Danish
- Sengul Orgut, Irem
- Spurrier, Gary
- Zhu, Xuwen
Instructors
- Casselman, Brad
- McMillan, Jennifer
Courses
Management Information Systems
This course will highlight one or more core programming languages (e.g., Java, Python) used within modern, data-driven organizations for the purpose of data collection, manipulation, and analysis. The first portion of the course will focus on essential programming knowledge and practices. The second portion of the course will emphasize the development of programmatic solutions, which will acquire data (e.g., web content, social media data, geospatial data, sensor-based data) through the integration of APIs and/or web services as well as ethical scraping techniques and then store the data in a modern backend database.
This course will cover the essentials of database design and management in modern, data-driven organizations. The first portion of the course will focus on relational database design as well as SQL for the storage and access of structured data. The focus of the second portion of the course will highlight modern database structures/systems (e.g., Apache Hadoop, graph databases) as well as their query languages for storing, accessing, and analyzing more unstructured data or data having relationships not easily queried by traditional databases. Additional topics may include data cleansing, query optimization, and extract-transform-load (ETL) processes.
Operations Management Courses
This course provides Operations Management concepts and applications in data-driven decision making. Emphasis is on data clean-up, data analysis, problem formulation, and interpretation of results using spreadsheet-based modeling and solution procedures including optimization and simulation approaches.
Building on the foundations of spreadsheet modeling analysis, this course provides a deeper understanding of optimization and simulation. Course topics include discrete optimization, duality and sensitivity, large scale optimization, multi-objective optimization, dynamic programming, and Monte Carlo and process simulations with an emphasis on practical applications. In addition to spreadsheets, the students will learn specialty optimization and simulation software, including heuristic methods and algorithms. Extensive use of software.
Statistics Courses
A broad introduction to statistical and probabilistic methods useful for managerial decision making. Topics include graphical displays, numerical summaries, basic probability models, confidence intervals, hypothesis testing, and regression analysis.
Introduction to the management of data using SAS. The collection and management of data from business or scientific research projects are emphasized.