Online M.S. in Business Analytics Courses

This course reviews statistical concepts and methods with emphasis on data analytics and visualizations. Topics to be covered include descriptive statistics, plots and graphs for discrete and continuous data, statistical inference, regression, and selected non-parametrics including chi-square. In addition, power pivot and other Excel analytical tools will be covered. Students will obtain a solid introduction to R as a functional programming language and will be able to use Excel and R to effectively compute statistical and graphical procedures. 3 credits.

Prerequisite: BANL 6100. This course focuses on the art of communicating ideas imbedded in data through visual means to include spatial representations. Students are introduced to industry-standard graphic and data design techniques used to create understandable visualizations in order to communicate effectively with a particular audience. Techniques in organizing and articulating data are developed using real world examples. The course materials, assignments and project will all be prepared using the R programming language.

Prerequisite: BANL 6100. This course will introduce students to foundations of relational database design and management with a focus on business domains and business analytics applications. Topics include database design principles (to include connecting and updating), entity-relationship diagrams, constructing queries in SQL, and analyzing databases – critical skills for data analysts. The course materials, assignments, and project will all be prepared using the R programming language.

This course introduces key concepts, skills, and methods in Business Analytics for data-driven decision making in organizations. Using Microsoft Power BI, the course trains the student in dashboarding, and Power BI’s quantitative, and computational capabilities across various business domains. The mix of topics includes data access, data wrangling, visualization, and machine learning via R programming language in Power BI.

Prerequisite or co-requisite: BANL 6100. The focus of this course is on statistical and data analytical methods for the preparation of business forecasts. A variety of empirical techniques are covered: smoothing methods, moving averages, regression analysis, classical time-series decomposition methods, and ARIMA (Box-Jenkins) models. Emphasis is placed upon building forecasting models and evaluating their reliability. The focus is on time-series data. R is the preferred statistical package.

Prerequisite or co-requisite: BANL 6100. This course is designed to provide business students with the skills to conduct data mining and statistical analysis for dealing with common managerial-making tasks, such as prediction, classification, and clustering. Data mining is a rapidly growing field that is concerned with developing techniques to assist analysts to make intelligent use of large data sets. In this course, the emphasis is on understanding the application of a wide range of modern techniques to specific managerial situations, rather than on mastering the mathematical and computational foundations of the techniques. Upon successful completion of the course, students should possess valuable analytical skills that will give them a competitive edge in many industry sectors, in a wide range of managerial and analytical positions.

Prerequisite: BANL 6100. The course consists of applied training in foundational topics for supervised learning such as Linear Regression, Nearest Neighbors, and Neural Networks. It first builds a sound understanding of data preparation, exploration, and reduction methods. It covers both prediction as well as classification processes. The emphasis is on understanding the application of a wide range of modern machine learning techniques to specific decision-making situations across business domains, rather than on mastering the mathematical and computational foundations of the techniques. The R programming language will be used for instruction.

Prerequisite: BANL 6100. The course consists of applied training in foundational topics for unsupervised learning such as Association Rules, Cluster Analysis, and Text Mining. It first builds a sound understanding of data preparation, exploration, and reduction methods. It covers both prediction as well as classification processes. The emphasis is on understanding the application of a wide range of modern machine learning techniques to specific decision-making situations across business domains. The R programming language will be used for instruction.

This course provides the basic theoretical foundation of both macroeconomics and international economics. The course will help the students to make decisions in today’s global economy. Topics may include comparative advantage, gains from trade, measuring national output, inflation, unemployment, productivity, growth, the role of economic policy and institutions in the performance of firms and nations, currency exchange rates, capital markets, open economy, trade liberalization, and economic integration.

An introduction to the political, economic, technological, and cultural settings of international business. Examines the problems, policies, and operational procedures of the multinational corporation, including adjustment to foreign cultures and governments. Review of development, organization, and structure of the international firm.

This course will familiarize you with some of the organizational and management issues surrounding the emergence of information and knowledge as key factors in developing and maintaining a competitive advantage for firms. The course is organized around two ideas: knowledge as a manageable asset and why people in organizations sometimes don’t use what they know. A basic assumption of the class is that organizations are complex adaptive systems operating in highly competitive, information- and knowledge-rich environments. This course will also use perspectives from Positive Organizational Scholarship as a possible framework for understanding how to get people in an organization to use what they know.

This course integrates marketing strategy with the enhancement of value in firms, products, services, and industry. The course focuses on competitive and customer metrics and analysis, development of marketing strategy, and implementation of strategic marketing plans in business organizations. The course incorporates current developments in marketing to acquaint students with the present-day challenges of marketing activities and serves as a vehicle for the application and integration of the concepts, analytical tools, and problem-solving approaches to developing marketing strategy.

Prerequisite: BANL 6100. This course applies econometric methods to real-world economic questions in order to quantify economic relationships. By providing the basic tools to do empirical analysis, this course empowers the student to become a more sophisticated consumer of economic research done by others. A foundation is built first with estimation, hypothesis testing, and confidence intervals with simple and multiple regression. This core set of econometric skills is extended with the use of dummy variables, autocorrelation, heteroscedasticity and simultaneous equations. This course uses the statistical programming language R and the R-Studio programming tool.

Prerequisite: BANL 6100. This course focuses on the process of creating goods and services. Concepts, functions and basic techniques of operations management are reviewed. Topics include decision-making, forecasting, scheduling, project management, production planning, inventory management, strategy, and quality control. Emphasis will be on quantitative methods, hands-on problem solving, and case studies. The course includes a team analysis of a real-world operations problem.

Prerequisite: BANL 6100. The course covers a variety of standard statistical methods used to analyze multivariate data. It emphasizes the implementation and interpretation of these methods across business domains. Topics covered include computation of summary statistics, analysis of categorical data, loglinear models for two- and higher-dimensional contingency tables, the multivariate normal distribution, MANOVA, principal components analysis, factor analysis, and other topics such as canonical correlation and cluster analysis. The R statistical computing package is used throughout.

Business analytics refer to the use of data, statistical, and quantitative analysis to derive decisions and actions, and is applied in many business functions including but not limited to operations, marketing, finance, and strategic management. This course is designed to provide insights into sport business analytics. Students are introduced to the skills, technologies, analyses, and practices essential to understand and evaluate business performance in sport industry, convert data into actionable information, and assist managerial decision making.

This course introduces concepts and principles of business process improvement, and quality assurance in organizations. It examines the primary tools and methods used to monitor, measure, improve, and control business processes, and quality from a holistic supply chain perspective. Topics include statistical process control, Lean Six Sigma principles, and continuous improvement.

Prerequisite: BANL 6100. This course focuses on the process of creating goods and services. Concepts, functions and basic techniques of operations management are reviewed. Topics include decision-making, forecasting, scheduling, project management, production planning, inventory management, strategy, and quality control. Emphasis will be on quantitative methods, hands-on problem solving, and case studies. The course includes a team analysis of a real-world operations problem.

This course integrates marketing strategy with the enhancement of value in firms, products, services, and industry. The course focuses on competitive and customer metrics and analysis, development of marketing strategy, and implementation of strategic marketing plans in business organizations. The course incorporates current developments in marketing to acquaint students with the present-day challenges of marketing activities and serves as a vehicle for the application and integration of the concepts, analytical tools, and problem-solving approaches to developing marketing strategy.

Prerequisites: BANL 6100, MKTG 6610. This course prepares students for a career in marketing analytics. This involves analyzing data using a set of statistical tools to facilitate informed decision making. Topics include methods used in online marketing, grocery stores, and financial markets. The course also explores customer big-data techniques and their theoretical foundations to help students acquire practical skills through hands-on experiences. Students are provided with the tools and the confidence necessary to analyze real-world questions and present their findings.

Prerequisite: BANL 6100. The course consists of applied training in foundational topics for supervised learning such as Linear Regression, Nearest Neighbors, and Neural Networks. It first builds a sound understanding of data preparation, exploration, and reduction methods. It covers both prediction as well as classification processes. The emphasis is on understanding the application of a wide range of modern machine learning techniques to specific decision-making situations across business domains, rather than on mastering the mathematical and computational foundations of the techniques. The R programming language will be used for instruction.

Prerequisite: BANL 6100. The course consists of applied training in foundational topics for unsupervised learning such as Association Rules, Cluster Analysis, and Text Mining. It first builds a sound understanding of data preparation, exploration, and reduction methods. It covers both prediction as well as classification processes. The emphasis is on understanding the application of a wide range of modern machine learning techniques to specific decision-making situations across business domains. The R programming language will be used for instruction.

Prerequisite: BANL 6100. This course will introduce some commonly-used survey methods for conducting research, evaluating programs and outcomes. Analysis of survey data, explanation of survey outcomes, and implications of differences in sampling techniques will be also emphasized. The R programming language will be used for survey data analysis.

Prerequisites: MKTG 6610, BANL 6100. An examination of methods useful for analyzing customer databases and developing data-driven marketing strategies. The exploration of insights from statistical analysis of customer life cycles, customer lifetime value, response experimentation, and predictive modeling. This course includes customer data analysis leading to performance metrics, marketing accountability, and dashboards.

Prerequisite: MKTG 6610. Key to a cutting edge marketing strategy today for all organizations is a relevant and meaningful social connection with customers and potential customers. Social media has become a cutting edge organizational tool to create, foster, and develop relationships with consumers, customers, clients, partners, funders, boards, volunteers – even competitors. We will focus on constituents’ social interactions, social media platforms, how to build social media strategies and market through those platforms, and finally, how to monitor and measure their effectiveness.

Prerequisite: MKTG 6610. This course will equip students with strategic abilities to help organizations adapt to the rapidly evolving digital markets of the future. It will cover a range of situations as brick and mortar businesses go partly or entirely digital and new business models emerge (such as Airbnb and Uber) that disrupt traditional business models. Using case studies and readings, students will learn the latest and upcoming changes in digital marketing strategy and practice. In addition, students will engage in an experiential learning project (e.g. search engine marketing) with a real business to appreciate the link between strategy and execution.

Prerequisite: BANL 6100. This course focuses on the process of creating goods and services. Concepts, functions and basic techniques of operations management are reviewed. Topics include decision-making, forecasting, scheduling, project management, production planning, inventory management, strategy, and quality control. Emphasis will be on quantitative methods, hands-on problem solving, and case studies. The course includes a team analysis of a real-world operations problem.

Prerequisite: BANL 6100. The course covers a variety of standard statistical methods used to analyze multivariate data. It emphasizes the implementation and interpretation of these methods across business domains. Topics covered include computation of summary statistics, analysis of categorical data, loglinear models for two- and higher-dimensional contingency tables, the multivariate normal distribution, MANOVA, principal components analysis, factor analysis, and other topics such as canonical correlation and cluster analysis. The R statistical computing package is used throughout.