Dual MS-MBA in Business Intelligence and Analytics Master's Programs

Corporate financial management requires the ability to understand the past performance of the firm in accounting terms; while also being able to project the future economic consequences of the firm in financial terms. This course provides the requisite survey of accounting and finance methods and principles to allow technical executives to make effective decisions that maximize shareholder value.

This 2-credit course focuses on data and database management, with an emphasis on modeling and design, and their application to business decision making. The course provides a conceptual understanding of both organizational and technical issues associated with data. The central theme concerns data modeling and databases. We examine organizational approaches to managing and integrating data. Among the topics included are normalization, entity-relationship modeling, relational database design, SQL, and data definition language (DDL). Discussed are specific applications such as strategic data management, master data management, and physical database design. The course concludes with a brief overview of Decision Support Systems, data warehousing and business intelligence, NoSQL databases (e.g., MongoDB) and cloud computing. The course includes a number of cases studies and modeling and design projects. Students in MIS 631 must also enroll in the associated 1-credit lab course MIS 632 Managing Data Lab.

This 1-credit lab course provides an experiential learning component for MIS 631 Data Management for which it is a co-requisite. MIS 632 provides hands-on experience in designing, implementing, and querying data bases. The relevant software is introduced using demonstrations, in-class exercises and homework exercises that are closely tied to and executed in synch with the conceptual and theoretical material covered in MIS 631. Specifically, students will gain hands-on experience in: (i) ERWIN - a widely used commercial tool for representing conceptual (e.g., E-R diagrams) and logical data models (e.g., relational DBMS), (ii) PostgreSQL (relational database software), (iii) SQL Structured Query Language) and (iv) MongoDB a NoSQL document data store. Students in MIS 632 must also be enrolled in the associated 2-credit lecture course MIS 631 Managing Data course.

This 2-credit course focuses on the design and management of data warehouse (DW) and business intelligence (BI) systems. The course is organized around the following general themes: business value of data, planning and business requirements, architecture, data design, implementation, business intelligence, deployment, data integration and emerging issues. Practical examples and case studies are presented throughout the course. Students in MIS 633 must also enroll in the associated 1-credit lab course MIS 634 Business Intelligence & Data Integration Lab.

Prerequisite: MIS 634 CoReq

This 1-credit lab course provides an experiential learning component for MIS 633 ML Engineering 2 for which it is a co-requisite. MIS 634 provides hands-on experience in designing, implementing, and querying data warehouses and large-scale database systems. The relevant software is introduced using demonstrations, in-class exercises and homework exercises that are closely tied to and executed in synch with the conceptual and theoretical material covered in MIS 633. Specifically, students will gain hands-on experience in using: (i) Alteryx - a widely used commercial tool for the Extract-Transform- Load (ETL) function, (ii) ERWIN - a widely used commercial tool for representing conceptual (e.g., E-R diagrams) and logical data models (e.g., relational DBMS) and (iii) a NoSQL database (e.g., MongoDB). Students in MIS 634 must also be enrolled in the associated 2-credit lecture course MIS 633 Business Intelligence & Data Integration course.

Prerequisite: MIS 633 CoReq

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 present throughout the course.

This course covers basic concepts in optimization and heuristic search with an emphasis on process improvement and optimization. This course emphasizes the application of mathematical optimization models over the underlying mathematics of their algorithms. While the skills developed in this course can be applied to a very broad range of business problems, the practice examples and student exercises will focus on the following areas: healthcare, logistics and supply chain optimization, capital budgeting, asset management, portfolio analysis. Most of the student exercises will involve the use of Microsoft Excel’s "Solver" add-on package for mathematical optimization.

This course introduces basic methods underlying multivariate analysis through computer applications using R, which is used by many data scientists and is an attractive environment for learning multivariate analysis. Students will master multivariate analysis techniques, including principal components analysis, factor analysis, structural equation modeling, multidimensional scaling, correspondence analysis, cluster analysis, multivariate analysis of variance, discriminant function analysis, logistic regression, as well as other methods used for dimension reduction, pattern recognition, classification, and forecasting. Students will build expertise in applying these techniques to real data through class exercises and a project, and learn how to visualize data and present results. This proficiency will enable students to become sophisticated data analysts, and to help make more informed design, marketing, and business decisions.

This course covers fundamental topics in experimentation, including hypothesis development, operational definitions, reliability and validity, measurement, and variables, as well as design methods, such as sampling, randomization and counterbalancing. The course also introduces the analysis associated with various experiments. At the end of the course, students present a project, which consists of designing an experiment, collecting data and trying to answer a research question.

This course introduces concepts and theories of social networks as well as techniques to conduct marketing research from a network perspective. Network concepts covered include graph-theoretic fundamentals, centrality, cohesion, affiliations, equivalence, and roles. Network theories covered include embeddedness, social capital, homophily, and models of network growth. Design issues will also be covered, including data sampling and hypothesis testing. Another focus of this course is on marketing applications of social network analysis, in particular the use of knowledge about network properties and behavior, such as hubs and paths, the robustness of the network, and information cascades, to better broadcast products and search targets. Application areas include customer profiling, community detection, targeting, sentiment analysis, and development of recommendation systems. Knowledge and skills learned in these required courses (e.g., R, python, machine learning) are applied to social network analysis.

The field of Big Data is emerging as one of the transformative business processes of recent times. It utilizes classic techniques from business intelligence & analysis (BI&A), along with a new tools and processes to deal with the volume, velocity, and variety associate with big data. As they enter the workforce, a significant percentage of BIA students will be directly involved with big data as technologists, managers, or users. This course will build on their understanding of the basic concepts of BI&A to provide them with the background to succeed in the evolving data-centric world, not only from the point of view of the technologies required, but also in terms of management, governance, and organization. Students taking the course will be expected to have some background in areas such as multivariate statistics, data mining, data management, and programming.

Business intelligence and analytics is key to enabling successful competition in today's world of "big data". This course focuses on helping students to not only understand how best to leverage business intelligence and analytics to become more effective decision makers, making smarter decisions and generating better results for their organizations. Students have an opportunity to apply the concepts, principles, methods associated with four areas of analytics (texts, descriptive, predictive, and prescriptive) to real problems in an application domain associated with their area of interest.

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. Additional topics include derivative markets.

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.

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.

Executives make decisions every day in the face of uncertainty. The objective of this course is to help students understand how decisions are made, why they are often less than optimal, and how decision-making can be improved. This course will contrast how managers do make decisions with how they should make decisions, by thinking about how “rational” decision makers should act, by conducting in-class exercises and examining empirical evidence of how individuals do act (often erroneously) in managerial situations. The course will include statistical tools for decision-making, as well as treatment of the psychological factors involved in making decisions.

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.

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.

This course introduces students to fundamental soft skills, work techniques, and technologies employed by management consultants. Topics covered in this course include project scoping, creating statements of work, meeting facilitation, project planning, design of presentations and written reports, management briefs, and delivery of status reports. The course will improve students’ ability to present analyses of issues and organizational problems in a concise, accurate, clear and interesting manner from the perspective of a consultant. This course is designed to be taken prior to the experiential graduate courses in the School of Business, including MGT 809: Industry Capstone Project.

In this course students work on an industry project with a team of their peers under the supervision of a faculty advisor and industry mentor. Students will work on project tasks and manage client expectations while applying their disciplinary and technical knowledge to the project. In addition to the project-specific deliverables, students will produce a statement of work, present weekly project updates, and a final presentation and project report to management. This three-credit course is tied to the Industry Capstone Program in the School of Business. Students must first apply for a project before registering for this course.

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.