Skip to Content

Course Search Results

  • 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
  • 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
  • 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
  • 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)]
  • 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: C Prerequisite Can Be Concurrent? Yes Registration Restriction(s): MSBA majors only or instructor permission Prerequisite:    ANLY 6100
  • 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: C Registration Restriction(s): Masters of Science in Business Analytics Prerequisite:    ANLY 6250
  • 3.00 Credits

    This course introduces the theory of an "Internet of Things (IoT)" and how to deal with the massive amounts of data generated by the connections of the IoT. The course includes an introduction to the open-source technologies commonly used to deal with unstructured big data problems, such as Hadoop, Spark, Pig, Hive, and Amazon Web Services. Along with familiarizing students with big data techniques and tools, the course presents real-world business applications and gives students hands-on experience with obtaining valuable information from big datasets. (Spring) [Graded (Standard Letter)] Prerequisite(s): ANLY 6100 and ANLY 6200 - Prerequisite Min. Grade: C Registration Restriction(s): MSBA majors only or instructor permission Prerequisite:    ANLY 6100 A ANLY 6200
  • 3.00 Credits

    This course provides an overview of advanced machine learning, data mining and data analytics applications. The main topics of the course can be organized as follows: Anomaly and Outlier Detection, Categorical and Regression Trees (CART), Time-series, K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Gradient Boosting (GB), Random Forests (RF), Cluster Analysis, Support Vector Machines (SVM). (Fall - 2nd Session) [Graded (Standard Letter)] Prerequisite(s): ANLY 6110 Prerequisite Min Grade: C Registration Restriction(s): Masters of Science in Business Analytics Prerequisite:    ANLY 6110
  • 3.00 Credits

    With faculty input and supervision, students carry out a complex analytics project that applies methodologies from their coursework to enhance the performance of a business. (As Needed) [Graduate Thesis/Capstone Pass/Fail] Prerequisite(s): ANLY 6110 and ANLY 6400 and ECON 6110 and instructor permission - Prerequisite Min. Grade: C Prerequisite Can Be Concurrent? Yes (ANLY 6110 and ANLY 6400 and ECON 6110) Registration Restriction(s): MSBA majors only Prerequisite:    ANLY 6110 A ANLY 6400 A ECON 6110
  • 1.00 Credits

    This course serves the MSBA student who needs additional support for a capstone project after completing ANLY 6900. (As Needed) [Graduate Thesis/Capstone Pass/Fail] Prerequisite(s): ANLY 6900 and instructor permission - Prerequisite Min. Grade: C Repeatable for Add'l Credit? Yes - Total Credits: 5 Prerequisite:    ANLY 6900