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Software utilized in fall semester courses:

Topics include, but are not limited to, navigating the Linux operating system, version control and collaboration, SQL and NoSQL databases, distributed computing, and high-level programming.Д«usiness Analytics 536 (1.5 credits) Case Studies in Business Analytics Use of standard computer packages.Д«usiness Analytics 545 (3 credits) Data Engineering for Business ApplicationsДЇundamentals of data engineering pipelines with particular focus on extracting, transforming, combining, validating and loading data for further analysis and visualization. Data preparation exploratory data analysis and visualization cluster analysis logistic regression decision trees neural networks association rules model assessment and other topics. Understanding and application of data mining methods. The course demonstrates the importance of developing solutions that address people, process, and technology.Д«usiness Analytics 542 (3 cr) Data Mining Methods for Business Applications Topics include translating data to information, formulating models, and communicating results to diverse constituencies. Presents data from real-world consulting engagements to give students practical experience solving problems. Linear programming decision models, solutions, sensitivity analysis, linear and integer optimization models, transportation and selected network flow models, along with application issues of these models, introduction to a mathematical programming language and standard optimization software.Д«usiness Analytics 535 (3 credits) Statistical Methods for Business

Boyd Center for Business & Economic ResearchД«usiness Analytics 531 (3 credits) Decision Optimization.

Anderson Center for Entrepreneurship & Innovation.

Office of Diversity & Community Relations.

Strategy, Entrepreneurship, & Organizations.