Course Schedule
DATA 600 Data Analysis & Visualization | 3 Credits | Sept. 3-Oct. 18, 2024
This course focuses on both the technical and visual aspects of inspecting and presenting data. Technical topics will include importing data from various sources, establishing relationships between data tables, transforming data (including pivoting and unpivoting), filtering, sorting, and aggregation. Visuals will be designed to focus attention on what the data is saying, with a special focus on visuals that respond dynamically to user manipulations (e.g., filtering). Emphasis will be placed on the design/refinement cycle for visualizations, with peer review playing a prominent role.
MATH 635 Operations Management & Research | 3 Credits | Oct. 28-Dec. 13, 2024
An introduction to the theory and practice of quantitative modeling and optimization, with applications to computer simulation and business resource management. Possible topics include linear and nonlinear programming, network analysis, game theory, deterministic and probabilistic models.
DATA 665 Advanced Operations in Management & Research | 3 Credits | Jan. 13-Feb. 28, 2025
Students will learn specialized applications of operations research to problems arising from business. These will include data envelope analysis, transportation and transshipment problems, goal programming, network models (including PERTCPM), and capital budgeting. Other topics such as inventory models, facility location problems, etc. will be covered as time and student interest permit. Special attention will be paid to the development and analysis of models for realistic medium- to large-scale problems.
BUSN 610 Supply Chain & Risk Management | 3 Credits | March 10-April 25, 2025
In this course, students will gain a solid understanding of supply chain and risk management principles, including effective ways to identify, mitigate, and measure the impact of potential supply chain disruptions, leading to informed and effective supply chain decisions. Topics in the course include supply chain design, strategies, integration, visualization, analytics, endogenous and exogenous risk, and mitigation methods.
Optional Elective: DATA 640 Agriculture Data Analytics | 3 Credits | May 5-June 13, 2025
The students will study different patterns of agricultural organizations' decision-making and ways that data analysis can be effectively used for each type. The course provides an understanding of the basics of several important analytic methods for agriculture business. Students will learn various machine learning models using Python in this course which will help them in making better decisions. The purpose of this course is to provide various types of machine-learning algorithmic solutions for the problems related to Agriculture-based data. After the completion of this, students will be able to solve any business-oriented problem for Agriculture data using Machine Learning algorithms.