Master of Science in BI&A

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 course focuses on data and database management, with an emphasis on modeling and design, and their application to decision support. The course is organized around the following general themes: Strategic Data Planning, Data Governance, Enterprise Data Integration, Data Management Approaches, Data Design for Transaction Processing vs. Decision Support, Data Management Functions, Abstraction and Modeling, Data‐ and Information Modeling (ER, Object‐oriented), Database Schemas (Conceptual Schema), Database Design (Functional Dependencies and Normalization), Query languages (SQL, DDL, QBE), Metadata Development and Application, Data Quality Approaches, Master and Reference Data Management (e.g., Customer and Product Data), Data, Analytics, and Business Performance, Introduction to Data Warehousing, OLAP, OLTP, and Data Mining, Strategic Data Policies and Guidelines (e.g. Enterprise Data and Integration, Governance, Markets, Customers, and Competitors, Leadership, Analysts and Knowledge Worker Skills and Training, Communities of Analysts). There are numerous case studies and modeling projects throughout the course.

This 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: Analytics & Competitive Advantage (Internal and External Processes, Customer and Competitor Intelligence), Business Intelligence and Industry Value Chains, BI Systems life‐cycle, Enterprise Planning, Project Management, Business Requirements, Architecture, Tool Selection, Data Design (Star‐schema, Surrogate Keys, ODS, Real‐time, Partitioned Tablespaces, Aggregations, MDDB (Cube Design), Conformed Dimensions), Methods for Tracking History, Implementation (ETL, Data Staging, and Physical Design), Data Visualization Techniques and Applications, BI Application Development (includes Portal and Dashboard Design), Complex Query Design, Deployment, Maintenance and Growth, and Emerging Issues. There are numerous case studies, class exercises, homeworks, and an end‐to‐end BI design project.

This course covers basic concepts in optimization and heuristic search with an emphasis on process improvement and optimization. The focus will be on the development of modeling skills rather than on the mathematics of optimization 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: transportation, logistics and supply chain optimization, capital budgeting, asset management, portfolio analysis. Most of the student exercise will involve the use of Microsoft Excel’s “Solver” add‐on package. A final module of the course covers the analysis of workflow logs and the fundamentals of process data mining.

This course deals with risk assessment and engineering in financial systems. It covers credit risk, market risk, operational risk, liquidity risk, and model risk. Topics include classical measures of risk such as VaR, methods for monitoring volatilities and correlations, copulas, credit derivatives, the calculation of economic capital, and risk adjusted return on capital (RAROC). The nature of bank regulation and the Basel II capital requirements for banks are examined. Case studies illustrate risk engineering successes and failures in financial enterprises.

This course focuses on understanding the basic methods underlying multivariate analysis through computer applications using R. Multivariate analysis is concerned with datasets that have more than one response variable for each observational or experimental unit. Topics covered include principal components analysis, factor analysis, structural equation modeling, multidimensional scaling, correspondence analysis, cluster analysis, multivariate analysis of variance, discriminant function analysis, logistic regression, and other methods used for dimension reduction, pattern recognition, classification, and forecasting. Through class exercises and a project, students apply these methods to real data and learn to think critically about data analysis and research findings.

This course focuses on understanding the basic methods underlying multivariate analysis through computer applications using R. Multivariate analysis is concerned with datasets that have more than one response variable for each observational or experimental unit. Topics covered include principal components analysis, factor analysis, structural equation modeling, multidimensional scaling, correspondence analysis, cluster analysis, multivariate analysis of variance, discriminant function analysis, logistic regression, and other methods used for dimension reduction, pattern recognition, classification, and forecasting. Through class exercises and a project, students apply these methods to real data and learn to think critically about data analysis and research findings.

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 because designing good experiments involves thinking about how to analyze the obtained data. Experiments test cause‐effect relationships; this course has very broad applications across all the natural and social sciences. 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 focuses on data mining and knowledge discovery methods, models, and algorithms, and their applications in solving real world business and operation problems. We concentrate on demonstrating how discovering the hidden knowledge in databases will help managers make near real‐time intelligent business and operation decisions. The course is organized around the following general themes: End‐to‐End System Approach to Data Mining and Knowledge Discovery, Data Preprocessing (advanced), Linear Regression, Logistic Regression, Business and Operations Applications, Data Preprocessing (advanced), Min‐Max Normalization, Z‐Score Standardization, Linear Regression, Logistic Regression, Association Analysis, k‐Nearest Neighbor Algorithm, k‐ Means Clustering Algorithm, Model Evaluation Techniques, and case studies.

This course introduces the most relevant algorithms of generative and discriminative estimation. Main topics include autoregressive and moving average models, seasonality, long memory ARMA and unit root test, volatility modeling, linear methods for classification, kernel methods, support vector machines, Bayesian and Markovian graphical models, EM algorithm, inference, sampling methods, latent variables, hidden Markov models, linear dynamical systems, reinforcement learning, and ensemble methods (boosting, bagging and random forests.) The course will also explore applications of the learning algorithms to finance, marketing, and operations.

In this course, students will learn how to analyze social network data and apply the analyses to develop marketing strategies. The course focuses on network concepts, including graph‐theoretic fundamentals, centrality, cohesion, affiliations, equivalence, and roles, as well as design issues, including data sampling and hypothesis testing. Theoretical areas covered include embeddedness, social capital, homophily, and network growth. 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 After taking this course, students should be able to statistically analyze and describe large scale networks, model the evolution of networks, and apply the network analyses to marketing research.

In this course, students will learn through hands‐on experience how to extract data from the web and analyze web‐scale data using distributed computing. Students will learn different analysis methods that are widely used across the range of internet companies, from start‐ups to online giants like Amazon or Google. At the end of the course, students will apply these methods to answer a real scientific question or to create a useful web application.

The capstone practicum brings together the key elements of the business intelligence and analytics curriculum. Students have an opportunity to apply the concepts, principles, and methods they have learned to real problems in an application domain associated with their area of interest. At the end of the course, students present their projects in a poster session for review by industry practitioners in pharmaceutical and life sciences.

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, and methods associated with four areas of analytics (text, descriptive, predictive, and prescriptive) to real problems in an application domain associated with their area of interest.

This course investigates statistical methods implemented in multiple quantitative trading strategies with emphasis on automated trading and based on combined technical-analytic and fundamental indicators to enhance the trade-decision making mechanism.  Topics explore high-frequency finance, markets and data, time series, microscopic operators, and micro-patterns. Methodologies include, but not limited to, Bayesian classifiers, weak classifiers, boosting and general meta-algorithmic emerging methods of machine learning applied to trading strategies.  Back-testing and assessment of model risk are explored.