Online Master of Science in Business Analytics Program

This workshop serves as an orientation toward the experiential and team-based models of learning used at Graziadio. Students engage in exercises and reflection that are meant to enhance communication, ethical decision-making, career development, and other relevant skills for interpersonal effectiveness in business. This course is graded credit or no credit.

This course begins with a relatively advanced treatment of model building for decision-makers (e.g., simulation models using Crystal Ball) and continues with a comprehensive presentation of the use of SPSS to analyze discrete multivariate models (i.e., models for purely categorical response variables). Whilst some attention is given to long-standing techniques for categorical data, like chi-square tests and contingency table analysis, the primary focus of the course will be "modeling techniques," particularly logistic regression, discriminant analysis, and neural networks. Cases and practical illustrations used in the course derive from a variety of business disciplines.

Optimization problems arise in a multitude of ways as a means of solving problems in engineering design, portfolio design, system management, parameter estimation, statistics, and the modeling of physical and behavioral phenomena. This is an introductory course in numerical methods for continuous optimization in finite dimensions. Optimization modeling techniques and numerical solution methodology will be applied to a range of important problems in operations, marketing, and finance. The optimization tools that we consider cover much of what is known as mathematical programming. We begin with linear programming and then progress through to nonlinear programming and integer programming.

Students gain critical skills for succeeding in today's data-intensive world, including business case study, data analysis, and making recommendations to management. They learn how to utilize database systems (SQL and NoSQL) and analytics software built upon R, Python, and SAS. They learn how to make trustworthy predictions using traditional statistics and machine learning methods. Topics include Supervised (prediction and classification) and Unsupervised (exploratory data analysis, principal components, cluster analysis) learning; Prediction models including multiple linear regression, artificial neural networks, regression trees, K-nearest Neighbors; Classification models including logit/probit models and classification trees.

Computer modeling simulations play a crucial role in all branches of business decision-making. This course systematically explores methodological issues in connection with computer simulations. Special emphasis is put on the relation between models and simulations as well as the role of computers in the practice of modeling and simulation for decision-making. A simulation, as used in this course, is the execution of a model, represented by a computer program that gives information about the system being investigated. The simulation approach of analyzing a model is opposed to the analytical approach, where the method of analyzing the system is purely theoretical. As this approach is more reliable, the simulation approach gives more flexibility and convenience. The activities of the model consist of events which are activated at certain points in time and in this way affect the overall state of the system. The points in time that an event is activated are randomized, so no input from outside the system is required.

Communications with Data is a structured approach for communicating data insights, and it involves a combination of three key elements: data, visuals, and narrative. The focus of this course is to provide experience in communicating a specific position or point of view from data through a combination of data-driven visuals and a carefully crafted storytelling technique.

One of the major classes of problems in the field of decision analysis is one-time decisions where a group of alternatives must be compared on the basis of multiple (and possibly competing) goals and objectives. This type of problem, called a multi-attribute decision, is found in many resource allocation and policy-making applications. As leaders in business increasingly consider the social and environmental consequences of their firms' actions, the ability to solve multi-attribute decision problems is becoming progressively more important. There are also many personal decision-making problems that involve multiple attributes (both quantitative and qualitative), such as choosing a job or purchasing a home. The challenge in this type of decision is to create a value model that allows explicit comparisons between alternatives that often differ in many ways.

From an analytical perspective, this class will focus on the difference between knowing what the stats mean and knowing which stats are meaningful. In this context, students will explore. This means first identifying what metrics are important for decision-making and focusing on these rather than "vanity" metrics. In addition to focusing on analysis and the use of dashboards, this class will equip students to make critical decisions regarding trade-offs in terms of what is most important to decision-makers: reach vs. engagement, retweet vs. click, traffic/day vs. traffic/post, subscribers vs. uniques or pageviews vs. attention.

In this course, students will acquire an understanding of the business value of big data, the importance of effective management of big data, and develop technical competencies in using leading-edge platforms for managing and manipulating structured and unstructured big data.

Business analytics refers to the ways in which enterprises such as businesses, non-profits, and governments can use data to gain insights and make better decisions. Business analytics is applied in operations, marketing, finance, and strategic planning among other functions. The ability to use data effectively to drive rapid, precise, and profitable decisions has been a critical strategic advantage for companies as diverse as Wal-Mart, Google, Capital One, and Disney. With the increasing availability of broad and deep sources of information—so-called "big data"—business analytics are becoming an even more critical capacity for enterprises of all types and sizes. In this course, you will learn to identify, evaluate, and capture business analytic opportunities to create organizational value.

The internet, electronic commerce, point of sale, and electronic marketing systems are providing almost overwhelming amounts of data to marketers and other decision-makers in organizations. The challenge is to identify how to transform data into usable and actionable information. Key competitive differentiators can be found through the collection, interpretation, and understanding of data and the resulting actions taken. This course will cover the use of information technology and systems that enable and enhance marketing strategies and tactics. This course will prepare managers to face the challenges of various information systems, data collection methodology, and organization; the process of mining valuable information from the data; and ethical situations created by data collection and information use.

This course covers theories and applications of business analytics, including association rules, clustering, classification, recommender systems, and the basics of text mining. The focus is on extracting business intelligence from firms' business data for various applications, including (but not limited to) customer segmentation, customer relationship management (CRM), personalization, online recommendation systems, web mining, and product assortment. The emphasis is placed on the "know-how"—knowing how to extract and apply business analytics to improve decision-making. Students will also acquire hands-on experience with Python open-source business analytics software.

In this course, students will be introduced to key concepts and approaches to business process analysis and improvement. The main focus of this course is both understanding and designing business processes that accomplish specific desired outcomes. Students will learn how to identify, document, model, assess, and improve core business processes. Students will be introduced to process design principles. The way in which information technology can be used to manage, transform, and improve business processes is discussed. Students will be exposed to challenges and approaches to organizational change, domestic and offshore outsourcing, and inter-organizational processes.

In this course, students are presented with analytical techniques that apply option pricing methods, which were initially developed for financially traded instruments, to the valuation of options on real assets. The "real option" approach to asset valuation quantifies the value of managerial flexibility, which is typically not captured in standard discounted cash flow valuation approaches. The course includes a review of the fundamental theory of decision analysis and options as well as an introduction to numerical techniques for solving dynamic programming problems, such as binomial lattices and trees. Hands-on experience with software tools used for the numerical analysis of problems using these ideas is also provided.

This course focuses on a data-driven approach to supply chain management decision-making. Specifically, the class introduces and explains how analytics is being used by logistics and supply chain practitioners. A strong emphasis will be placed on the development and use of analytics-based models to illustrate the underlying concepts involved in both intra-and inter-firm logistics operations. Three topics of specific focus are demand forecasting, SCM data analysis, and vendor selection and management. A variety of industry-based cases will be utilized to illustrate current SCM practices on a global basis. This course will also make extensive use of simulations to further emphasize the interactive nature of supply chains.

This course offers a comprehensive introduction to the fundamentals of healthcare research methodologies, including research design, data collection, and applied statistics. In addition, the course will introduce students to basic operations research/management (OR/OM) techniques and demonstrate how those tools can also be applied in health service management. A basic knowledge of all such methods is critical for anyone who manages an enterprise, conducts research, or formulates policies in a healthcare setting. The class offers a case-based, participatory approach to learning. All data management and analyses are performed in Excel or Excel add-ons.

This course provides an introduction to and overview of the variety of topics and diverse functions of project management. The fundamental theory of each function will be explored, and the essential project management skills, practices, and tools will be identified.

This course is designed to provide an overview of applied analytics concepts and functions within the sports, entertainment, and media industries. Students in this course will learn about how organizations within these interconnected industries utilize data and apply analytics to make more informed strategic business decisions that create a sustainable competitive advantage. Course topics will be presented and analyzed via a series of current issue case studies and collaborative workshops facilitated by the instructors of this course along with industry leaders. Students will also have an opportunity to work on a variety of practical application projects on topics such as dynamic and variable ticket pricing, corporate sponsorship valuation, customer relationship management, fan engagement, retention of season ticket holders, and social media and digital marketing analytics.

Research and statistics reveal that a risk-based approach utilizing data analytics across three dimensions: volume, variety, and velocity is critical for effectively providing Information Security analytics. An essential element of a risk-based approach is the use of user-behavior analytics (UBA) to compare and contrast threats against normal behavior. This type of analytics enables business leaders to understand and learn from user-behavior to assess risk, anticipate, and respond to security breaches. This course will equip business managers to effectively recognize and address the key risks to business information systems and data.

Business Analytics and Intelligence (BA & I) empowers organizations to anticipate and shape business outcomes through data integration, analysis, and delivery. Enabled by increasingly potent enterprise infrastructure, companies must analyze massive volumes of constantly changing, multi-structured "big data" to remain competitive in the global marketplace. By employing business analytics and intelligence, companies of all sizes leverage these resources to support evidence-based decision-making, stimulate business process optimization, sustain competitive advantage, and create integrated, organization-wide solutions to complex business problems. This course introduces techniques to transform data into business intelligence and to use analytics to create business value. Students will acquire the knowledge required to develop solutions to real-world problems through a combination of readings, case studies, applied projects, technology demonstrations, guest lecturers, and assignments to analyze and interpret real data. Typical topics may include data mining, data dimension reduction, predictive analytics, data visualization, and coverage of enterprise information strategy, master data management, business intelligence systems, and collective intelligence.

This course will provide students with the opportunity to study specific contemporary issues or topics in business and management. This course may be repeated as content changes.

Organizations and organizational units increasingly employ competitive intelligence (CI) to support decision-making and management, and to build and sustain competitive advantages. As the formal practice of CI has grown in adoption and sophistication, information professionals are often charged with intelligence-related responsibilities. This course examines competitive intelligence models, functions, and practices, the roles of information professionals in CI, and the management of CI. Discussion and practice topics may include intelligence ethical and legal considerations; identifying intelligence needs; intelligence project management, research methods, analysis, production, and dissemination; the uses of intelligence; intelligence sources and tools; managing the intelligence function; and the evolution of CI. A working knowledge of print and electronic business information sources is recommended.