8 courses with the subject MSBA, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
MSBA 8100Statistical Foundations of Business Analytics4
The objective of this course is to build a solid foundation of understanding in probability and statistics for decision making under uncertainty. Topics include statistical independence, conditional probability, Bayes theorem, discrete and continuous distributions, sampling distributions, ANOVA, correlation and linear regression.
Undergraduate students must be admitted to the 4+1 Undergraduate Path to the MSBA program Credits: 4
MSBA 8110Databases4
This course focuses on the fundamentals of database design and modeling, data structures and storage technologies, and techniques for managing data. Topics include multidimensional databases (cubes), columnar databases and Cloud technologies, Structured Query Language (SQL), and processes for moving data. The course introduces “big data” architectures, including Hadoop, Map-reduce, Hive, and Spark and cloud environments such as MS Azure, AWS and Google Cloud.
This course addresses foundational and broad aspects of data use. Students will engage with the ethical dimensions of data science, including the ways we build models and algorithms to aid in our decisions. Data collection, quality, management, preparation, manipulation, sharing, ownership, and persistence all factor into an ethical framework. Data quality is addressed as an ongoing operational and governance process, as well as within the context of massive volumes of data available today. Methods of finding and addressing data issues, including missing and erroneous values are discussed.
This course provides an introduction to programming in R and Python, which are among the most popular tools for data analytics. The course covers basic programming concepts, the logic of programming; along with program design, including function modularity and managing data structures. The course provides a solid foundation for using these tools in later courses and in work careers.
Undergraduate students must be admitted to the 4+1 Undergraduate Path to the MSBA program Credits: 4
MSBA 8150Data Visualization4
This course explores the principles and techniques used in creating visual representations of quantitative data that effectively communicate the meaning in the data. It covers the core topics in data visualization: data representation, visualization toolkits (Tableau, Power BI, Python, others), dashboard design, and web-based visualizations, and visualization of complex, high dimensional data.
This course builds on the basic statistics course and moves quickly into applying these techniques towards understanding patterns in our data. R and Python are the primary tools used, and topics include an overview of regression analysis (including logistic, OLS, Poisson, and nonlinear models), supervised and unsupervised machine learning, data partitioning, and principal component analysis.
This course advances the student’s understanding of machine learning techniques, emphasizing the application of these techniques to business datasets. Students use R and Python and other tools to better understand concepts, including neural networks, clustering algorithms, naïve Bayes, and ensemble techniques such as Random Forests.
The Practicum integrates all earlier coursework, applying student skills and knowledge to a real-world situation. Students work on a project with a regional organization.
