9 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.
This course focuses on the fundamentals of statistical thinking and statistical problem-solving. Topics include using current statistical software to analyze data sets, interpreting the outputs, and applying the information in the data for decision-making. Credits 3
This course explores how artificial intelligence augments business decision-making and analytics. Students will examine AI-driven data analysis, predictive modeling, and decision-support systems that improve strategic planning and business efficiency. The course will also examine the impact of AI on responsible data governance practices, algorithmic biases in shaping decision-making outcomes, and ethical considerations in promoting fairness, transparency, and accountability in AI-assisted business decisions. Students will gain hands-on experience with cognitive and AI-based analytics tools to extract insights from complex data, demonstrating AI's role in enhancing human expertise and optimizing business decisions. Credits 3
This course applies analytical programming methods and logic to business data sets to support analytics for gaining business insights. The course’s aim is to develop a foundation for using programming methods as tools for accessing data from external sources, preparing data sets for analysis, managing and transforming data sets, and working with variables, logical operators, and functions to be able to perform fundamental descriptive and inferential statistical methods. This course uses fundamental programming language concepts to import, organize, analyze, and report findings. Topics include analytical programming, control structures, operators, functions, structures, and plotting. The current programming language used in this course is the R programming language with a brief introduction to Python. Credits 3 274
for Business Analytics
This is an introductory course on data mining within a business context. This course introduces the basic concepts, principles, and methods of data mining, and focuses on applications of data mining for problem solving and business decision-making. An overview of the core ideas and steps in the data mining process is presented. The principles and techniques associated with key areas of data mining are covered, including pattern discovery, prediction, classification, cluster analysis, and time series analysis. The implementation of techniques will be through the application of an analytical programming language. Credits 3
This course is dedicated to the application of quantitative analytical tools to leverage the value of financial and accounting data for improved decision making and financial risk mitigation within the organization. In this course, an analytical approach is used to critically question financial and auditing data, to transform and manipulate data to investigate issues and questions, and to effectively interpret and summarize results for stakeholders. Issues specific to accounting and financial data sets are explored using spreadsheet-based and statistical programming techniques. Credits 3
This course is dedicated to the application of quantitative analytical tools related to the design, management, and improvement of business systems and processes. The aim of this course is to apply approaches and methods to improve effectiveness and efficiency of essential business and supply chain processes, including demand and capacity planning, facility location, inventory planning, production control, and quality improvement. Analytical methods and analysis techniques to support the design and improvement of the key business processes that add value through improved performance are covered. Key issues related to the design and management of operational processes and supply chains are analyzed using both deterministic and statistical quantitative methods such as linear programming, forecasting, simulation, and statistical process control methods. Credits 3
Source: Lincoln Memorial University's catalog, linked per course · table learning_unit · CourseShelf publish 59
