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• ANLY 6010: Business Analytics I

  3.00 Credits

  
  An introduction to data science methods in business. Includes an introduction to programming for data manipulation and modeling. Provides an overview of descriptive, predictive, and prescriptive methods in data analytics from a practical business perspective, with a focus on direct application to current data-driven decision-making. (Fall - 1st Session, Spring - 2nd Session) [Graded (Standard Letter)]Registration Restriction(s): MAcc or MBA students only
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• ANLY 6020: Business Analytics II

  3.00 Credits

  
  A continuation of ANLY 6010. Covers the primary analytic techniques involved in data mining for business problem-solving, including advanced regression, decision trees, kNN, and others. Introduces unsupervised learning methods. Builds on the programming skills established in Business Analytics I. (Fall - 2nd Session, Summer - 1st Session) [Graded (Standard Letter)]Prerequisite(s): ANLY 6010 - Prerequisite Min. Grade: CRegistration Restriction(s): MAcc or MBA students only Prerequisite:    ANLY 6010
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• ANLY 6030: Data Management and Visualization

  3.00 Credits

  
  An introduction to the methods and tools for database management and data visualization with a particular focus on industry analytics. Database topics include the logical structure of databases as well as the methods and technology for efficient data storage, retrieval, and presentation. Visualization topics include an overview of the Tableau software for data visualization and discussion of data visualization principles. Emphasizes skills in retrieving and presenting data for business presentation. (Spring - 1st Session, Summer - 2nd Session) [Graded (Standard Letter)]Registration Restriction(s): MAcc or MBA students only
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• ANLY 6050: Programming for Data Science

  3.00 Credits

  
  Introduction to programming basics, binary computation, problem-solving methods and algorithm development. Includes procedural and data abstractions, program design, debugging, testing, and documentation. Covers data types, control structures, functions, parameter passing, library functions, arrays, inheritance and object-oriented design. Laboratory exercises in Python. (Fall - 2nd Session, Summer - 1st Session) [Graded (Standard Letter)]Registration Restriction(s): Master of Science in Business Analytics
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• ANLY 6100: Data Analytics I

  3.00 Credits

  
  An introduction to data science methods in business, finance, and economics. Includes an introduction to an appropriate programming language for data manipulation and modeling. Provides an overview of descriptive, predictive, and prescriptive methods in data analytics. (As Needed) [Graded (Standard Letter)]Prerequisite(s): BA 6000 or MGMT 6100 - Prerequisite Min. Grade: C Prerequisite:    BA 6000 O MGMT 6100
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• ANLY 6110: Data Analytics II

  3.00 Credits

  
  A continuation of ANLY 6100. Covers the primary analytic techniques involved in data mining, including logistic regression, decision trees, kNN, naive Bayes, and others. Introduces unsupervised learning methods. Builds on the programming skills established in ANLY 6100. (As Needed) [Graded (Standard Letter)]Prerequisite(s): ANLY 6100 - Prerequisite Min. Grade: C-Equivalent Course(s): ANLY 4110 Prerequisite:    ANLY 6100
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• ANLY 6200: Data Processing and Visualization

  3.00 Credits

  
  This course covers prevalent methods and tools for data processing and visualization. Students are introduced to both the Python and R programming languages for processing, analyzing, and visualizing data. In addition, the course includes an overview of the Tableau software for data visualization. Course emphasis is on mastering basic software functionality and developing intermediate to advanced skills in working with and presenting data. (Fall) [Graded (Standard Letter)]Registration Restriction(s): MSBA majors only or instructor permission
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• ANLY 6250: Data Management and Organization

  3.00 Credits

  
  An introduction to the management and organization of data, with a particular emphasis on the current database tools for industry analytics. Topics include the logical structure of databases as well as the methods and technology for efficient data storage, retrieval, and presentation. (Fall) [Graded (Standard Letter)]
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• ANLY 6300: Marketing Analytics

  3.00 Credits

  
  This course provides an overview of the most important analytics methods used in marketing decision making. Students are introduced to common marketing models such as probit, multinomial, and structural equation modeling. Well-established marketing research methods are covered, such as survey and experimental design, along with more recent marketing research tools such as sentiment mining and social-network analysis. (As Needed) [Graded (Standard Letter)]Prerequisite(s): ANLY 6100 - Prerequisite Min. Grade: CPrerequisite Can Be Concurrent? YesRegistration Restriction(s): MSBA majors only or instructor permission Prerequisite:    ANLY 6100
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• ANLY 6350: Advanced Data Management and Organization

  3.00 Credits

  
  The course focuses on research and application of advanced database systems to plan and build data centric enterprise systems.Upon completion of this course, students will learn how to utilize modern cloud computing systems architecture for optimal data management, describe and evaluate modern data architecture concepts, and apply data architecture design principles to build or modify data systems. They will also be able to evaluate and choose data technologies for an enterprise; deploy data engineering technologies and development for efficient movement of data; develop data systems for use with analytics, machine learning, and artificial intelligence; and design a data management strategy for proper storage, security, and consumption of data. (Fall - 1st Session) [Graded (Standard Letter)]Prerequisite(s): ANLY 6250 - Prerequisite Min Grade: CRegistration Restriction(s): Masters of Science in Business Analytics Prerequisite:    ANLY 6250
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