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Introduces the importance of data management, data analysis and data representation. Includes the use of common statistical tools and their applications in decision-making and research. Emphasis is on quantitative and technology based analysis of real world problems to improve decision-making in various disciplines, along with report writing and presentation skills. Prerequisite: Admission into the program and MATH 130, 138 or MATH& 141 with a C or better; or permission of the instructor.
Learn core concepts of data collection and its management. Topics include collecting data ethically from different sources, assessing data quality, learning techniques to clean, process, and store the data while maintaining privacy and security. Students will research real world examples, using common statistical software and produce reports and presentations. Prerequisite: DA 310 or permission of the instructor.
Introduce various statistical methods for analyzing more than one outcome variable and understanding the relationships between variables. Topics include a variety of multivariate models such as MANOVA, discriminant functions, canonical correlation, and cluster analysis. The focus will be on real world examples from a variety of sources and using statistical software. Prerequisite: MATH 342 with C or better. Recommended: DA 460.
Students will study the process of formulating business objectives, data selection, preparation, and partition to successfully design, build, evaluate, and implement predictive models for a variety of practical business applications. Topics include a variety of predictive models such as classification, decision trees, machine learning, supervised and unsupervised learning. Prerequisite: MATH 342 with a C or better, or permission of the instructor. Recommended: DA 460.
This course introduces a quantitatively oriented view of marketing strategy and provides tools and methods to leverage data to inform marketing strategies. Topics may include a variety of marketing analytics strategic models and metrics such as competitive analysis, segmentation, targeting and positioning. The focus will be on real world examples from a variety of sources and using statistical software. Prerequisite: MATH 342 with a C or better, or permission of the instructor. Recommended: DA 460.
This course introduces modern software and programming languages for effective data analysis, such as R and Python. Students will learn how to configure software environment, apply programming concepts, build statistical models, and write code to analyze data sets. Prerequisite: BA 240 or DA 310 or MATH 341 with a C or better, or permission of the instructor.
In this integrative learning course, students will engage in planning, designing, implementing and presenting a project demonstrating the attainment of business analytics program learning outcomes, as well as professional competencies and career readiness. Prerequisite: DA 420 with a C or better, or permission of instructor.