Analytics MBA

Foundational courses are intended for students who do not have training or prior coursework in accounting, finance and statistics. Students who have passed the CFA level 1 exam are exempt; students who have not passed the exam will work with their advisor to determine which courses they may waive. A course in business writing and communication also is required. This can be fulfilled through a MOOC.

FIN 500 Financial and Managerial Accounting
This course will develop accounting analysis useful for managerial decision-making purposes. Topics will include an introduction to elements of financial accounting, cost-profit-volume analysis, manufacturing costs and elements of cost accounting, special decision analysis, budgeting, variances, and controllability and responsibility accounting.

FIN 523 Financial Management
This course covers the fundamental principles of finance. The primary concepts covered include the time value of money, principles of valuation and risk. Specific applications include the valuation of debt and equity securities as well as capital budgeting analysis, financial manager’s functions, liquidity vs. profitability, financial planning, capital budgeting, management of long term funds, money and capital markets, debt and equity, management of assets, cash and accounts receivable, inventory and fixed assets.

MGT 506 Economics for Managers
This course introduces managers to the essence of business economics – the theories, concepts and ideas that form the economist’s tool kit encompassing both the microeconomic and macroeconomic environments. Microeconomic topics include demand and supply, elasticity, consumer choice, production, cost, profit maximization, market structure, and game theory while the Macroeconomic topics will be GDP, inflation, unemployment, aggregate demand, aggregate supply, fiscal and monetary policies. In addition the basic concepts in international trade and finance will be discussed.

This course serves as a second semester sequence in corporate finance. Among the topics covered are: leverage on the balance sheet and weighted average cost of capital; bankruptcy, turnarounds, and recapitalizations; international currency hedging; stock options; private equity valuation; mergers and acquisitions; and the issuance of public and private securities.

An interdisciplinary course which examines the elements of, and the framework for, developing and implementing organizational strategy and policy in competitive environments. The course analyzes management problems both from a technical-economic perspective and from a behavioral perspective. Topics treated include: assessment of organizational strengths and weaknesses, threats, and opportunities; sources of competitive advantage; organizational structure and strategic planning; and leadership, organizational development, and total quality management. The case method of instruction is used extensively in this course.

The study of marketing principles from the conceptual, analytical, and managerial points of view. Topics include: strategic planning, market segmentation, product life-cycle, new product development, advertising and selling, pricing, distribution, governmental, and other environmental influences as these factors relate to markets and the business structure. 

In this course, students will evaluate and create their own prospective business strategies. They will develop an understanding of entrepreneurship and innovation in starting and growing a business venture. Students will be given an opportunity to actually start their own business or create a business in their company by learning how to take advantage of the new order of business opportunities of the information age. This course’s main objective is to show students how to identify these opportunities, be able to formulate and evaluate both qualitatively and quantitatively whether the opportunity is worth pursuing, and, of course, how it may be pursued. Actual case studies and experiences will be intertwined with the course content.

Project success depends, largely, on the human side. Success in motivating project workers, organizing and leading project teams, communication and sharing information, and conflict resolution, are just a few areas that are critical for project success. However, being primarily technical people, many project managers tend to neglect these "soft" issues, assuming they are less important or that they should be addressed by direct functional managers. The purpose of this course is to increase awareness of project managers to the critical issues of managing people and to present some of the theories and practices of leading project workers and teams.

Innovative organizations are led by people who relentlessly nurture creative collaborations. These leaders stimulate imagination, teach others how to turn imagination into creativity, and build group structures and processes to enable people to turn creative ideas into innovations that drive business results. This course builds individual awareness of creativity and collaboration skills while increasing the student’s capacity for both. It teaches the science behind techniques, tools, interpersonal skills, leadership skills, organizational strategies, and environmental designs that increase group effectiveness. The overall goal is to strengthen the student’s ability to lead others to address meaningful problems and possibilities wherever they may be found.

This course deals with the theory and methods associated with design thinking, a problem-solving protocol that spurs innovation and solves complex problems. Design thinking involves a unique form of inquiry which goes well beyond product and service design. Students will develop an appreciation for design and develop skills for studying design systems. These concepts and methods have wide applicability as they can be used to design organizations of people, information structures, compensation systems as well as the entire consumer experience. Applying these approaches can often create entirely new systems that are more useful and usable. The logic of this approach can sometimes solve “wicked problems” which have defied previous solutions.

This course leads students through the identification, analysis, definition, and deployment of service opportunities within public and private organizations. Each of these phases is analyzed in detail to encompass the principal activities, methods, tools and techniques applied in the respective phase. Students will learn how to identify appropriate supporting techniques and information technologies for the different phases of the service life cycle, assess the role of technology, and gauge the organizational impact of service-focused operations. The objective of the course is to enable students to identify, implement and evaluate innovative service offerings in their organization.

This course explores data-driven methods that are used to analyze and solve complex business problems. Students will acquire analytical skills in building, applying and evaluating various models with hands-on computer applications. Topics include descriptive statistics, time-series analysis, regression models, decision analysis, Monte Carlo simulation and optimization models.

This course introduces basic theory and methods underlying multivariate analysis. Students will study techniques used for regression, classification, dimension reduction, and clustering. They will build expertise in applying these techniques to real data through class exercises and a project, and learn how to present results. This proficiency will enable students to become sophisticated data analysts, and to help make more informed design, marketing, and business decisions. Python will be the default programming language used for the course.

This course will focus on Data Mining & Knowledge Discovery Algorithms and their applications in solving real world business and operation problems. We concentrate on demonstrating how discovering the hidden knowledge in corporate databases will help managers to make near-real time intelligent business and operation decisions. The course will begin with an introduction to Data Mining and Knowledge Discovery in Databases. Methodological and practical aspects of knowledge discovery algorithms including: Data Preprocessing, k-Nearest Neighborhood Algorithm, Machine Learning and Decision Trees, Artificial Neural Networks, Clustering, and Algorithm Evaluation Techniques will be covered. Practical examples and case studies will be presented throughout the course.

This course is intended to integrate all previously taken courses in the program by presenting a set of increasingly complex business problems. These problems can be solved through analytic skills taught in this and previous courses. In particular, the course is intended to reinforce the understanding of analysis as a way to build models that can focus attention on parts of the system that can be improved through intervention. The early part of the course uses synthetic data and empirical data readily available for analysis. The second part of the course encourages students to state and solve their own problem, gathering their own data as a part of the analytic process.