Master of Science in Business Analytics

This course provides an overview of analytical methods commonly used within business environments. The focus is on the applied use of analytical methods to make better business decisions and improve customer and shareholder value. The course provides fundamental concepts and tools needed to understand the emerging role of business analytics, and applies basic methods in descriptive, inferential, predictive, and prescriptive analytics to provide insights into business problems and decisions. Foundational statistical and data analysis methods used to enhance business decision-making are examined. This course also examines the use of spreadsheets for data manipulation, descriptive visualization and summarization, statistical analyses, and decision modeling.

This course explores the use of data, statistics, and technology for the purpose of driving marketing decision-making and problem-solving. Students will examine the processes involved with tracking, measuring, and analyzing data within the marketing and sales context to improve pricing, product development, segmentation, targeting, advertising, sales, customer relationship management, and client lifetime value.

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.

This course applies business intelligence technologies to the vast amount of data in business systems for more effective and efficient decision-making. Topics include data acquisition, visualization, reporting with dashboards, scorecards, graphical methodologies, and developing reports.

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.

This course examines alternative approaches to decision modeling and statistical analyses for business applications. Topics include the role of business analytics to gain value from data, data acquisition, interpretation of data, foundational elements of database management, and managing big data in an organizational setting. In this course, structured query language (SQL) is used to express decision making and analytical needs, explore datasets, sort, and process information from raw data to generate analytical results for key business insights. The course also discusses the impact of big data on organizational data-based systems and processes.

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.

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.

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. 

This course is designed to introduce business students to the use of quantitative tools and methods for structured decision making and more effective problem solving.  This course is dually focused on business problem formulation and quantitative decision analysis tools and methods. The course provides an overview of problem-solving tools and techniques and develops skills necessary to formulate, evaluate, and communicate solutions based on evidence and data.  Secondly, it presents core methods and techniques for data-driven decision analysis. Students will apply predictive, prescriptive, and experimental analytical methods and techniques to improve managerial decisions. During this course, students will learn to develop and implement a data acquisition and analysis plan appropriate to the problem(s).