Master of Science in Business Systems and Analytics Online

This course introduces students to the growing field of business analytics. Business analytics is the use of data, information technology, statistical analysis, and quantitative methods and models to support effective organizational problem solving and informed decision making. The course includes methods, tools and techniques for summarizing and visualizing historical data, which is relevant to descriptive analytics–the use of data to find out what has happened in the past or is currently happening; methods, tools and techniques for extracting information from existing data in order to determine patterns, which is relevant to predictive analytics–the use of data to find out what will happen in the future; and methods, tools and techniques for optimization, which is relevant to prescriptive analytics - the use of data to determine the best course of action in the future. Prerequisite: MBA 601, MBA 602 or equivalent course work.

This course focuses on data warehousing and data mining in organizations. Topics covered in the course include: data warehousing and mediation techniques aimed at integrating distributed, heterogeneous data sources; data mining techniques such as rule-based learning, decision trees, association rule mining, and statistical analysis for discovery of patterns in the integrated data; and evaluation and interpretation of the mined patterns using visualization techniques. Prerequisite: MBA 693.

This course introduces today's most valuable marketing research and analytics methods and tools and offers a best-practice methodology for successful implementation. Hands on projects and case studies provide students an opportunity to apply the marketing research and analytics techniques for solving key problems, ranging from product development, segmentation, pricing, campaign management, brand valuation, and digital marketing strategy. Prerequisite: MBA 690.

This course is built from the premise that technology has changed the role of the accountant. A heightened awareness of systems, technology, and data analysis is becoming increasingly required of individuals in the different accounting fields. Data has proliferated in business, and managers and accountants need to understand the implications for decision-making and tap into the data to provide better insights into a firm/client/customer/supplier, etc. This course is intended to provide students with an understanding of data analytic thinking and terminology as well as hands-on experience with data analytics tools and techniques. Students should leave this course with the skills necessary to translate accounting and business problems into actionable proposals that they can competently present to managers and data scientists. While there will be some use of tools in this course, the focus of this class is on concepts, not algorithms or statistical math.

This course is about the manager's responsibilities for problem solving and decision making, and those areas in which information technology can be used to gain the insight needed to support selection of decision alternatives. Students learn about the role of data, information and knowledge in managerial problem solving and decision making. Transactional processing and database management systems (DBMS) are used to store, manage and retrieve data in organizations. Decision support system (DSS) tools and technologies (such as natural language programming and influence diagramming) are used to organize data into information for decision analytics. Expert systems (ES) are used to synthesize information into knowledge for knowledge management. Students are required to use DBMS, DSS and ES software packages in a hands-on environment.

One of the skills that characterize great business data analysts is the ability to communicate practical implications of quantitative analyses to any kind of audience member. In this course, students will learn how to visualize data, tell a story, and explore data by reviewing the core principles of data visualizing and dashboarding. The course aims to focus on effective and high impact visualizations of common data analyses to help them convey conclusions directly and clearly. Students will be able to get practice in designing and persuasively presenting business “data stories” that use these visualizations, helping stakeholders make decisions and take action based on their business data capitalizing on design principles.

This course covers a broad overview of finance topics from a data analytics perspective. Students will learn the ins and outs of applied data analysis and a conceptual framework for thinking about data from both a statistical and machine-learning perspective with applications in finance. Students will learn to understand and apply concepts like capturing and analyzing new sources of financial data, building predictive models, and running simulations of market events, using concepts of data analysis and probability in investment science, risk management, valuations, rates of return and profitability analysis. The course aims to provide a theoretical and practical framework in which students will be challenged to solve real-world problems in the finance field and gain familiarity with commonly used stochastic models.

This course is about structured analysis and design methodology for complex business systems. Students become familiar and use Entity Relationship Diagram, Data Structure Diagram, Data Flow Diagram, Data Dictionary, and Process Specifications to develop Systems Specifications. These specifications are utilized as the blueprint to develop and implement relational databases and explore the Structured Query Language (SQL) used to manipulate and operate the database. Prerequisite: MBA 693.

This course is designed to introduce students to the principles of business application programming for business analytics using selected high-level languages such as R, Python, and Hadoop. Emphasis is placed on identifying the capabilities and limitations of statistical computing languages for big data. Students will learn skills and techniques to solve big data problems through a series of steps that involve identification of problems, design of the solution logic, formal representation of program specifications, and implementation. The focus is on accessing data from multiple sources, manipulating different types of programming objects, performing character manipulation, and generating reports. Students will design and develop several computer programs throughout the term.

This course introduces students to decision making and problem solving with simulation and optimization tools and techniques. Students learn to formulate and construct a decision model with spreadsheets and use the optimization tools, Monte Carlo simulation, and sensitivity analysis to generate and interpret solutions. The course covers different types of optimization and simulation models, including linear programming, sensitivity analysis, integer linear programming, goal programming, multiple objective optimization, simulation modeling, and queuing theory.

Financial Accounting: This course is an introductory study of financial accounting. This includes studying basic accounting language and concepts, recording financial transactions, and preparing and interpreting financial statements. Reporting and analyzing operating income, operating assets, and owner financing is also emphasized.

This course serves as an introduction to the financial system and its relationship to domestic and international business activity financing. Financial market components and phenomena such as financial instruments, institutions, the flow of funds, market efficiency, interest rate determination and term structure, exchange rates, and government policy are analyzed. An introduction is given to the concept of financial asset valuation and the time value of money.