This 3-credit course covers the major mathematical and statistical concepts that underly the field of business analytics to prepare students for the more advanced courses in the BI&A curriculum. The course material will span Linear Algebra, Differential Calculus, and elementary Probability and Statistics. It does so at an elementary level but at a level sufficient to prepare students for success in the more advanced topics covered in the remainder of the BI&A curriculum. Each mathematical and statistical concept is illustrated through one or more business applications that bridge the gap between theory and practice and demonstrate how analytics is applied in business.  Additionally, the course is oriented towards data management and database disciplines to provide a seamless connection to the courses in the required database courses,  Furthermore, concepts are illustrated via examples drawn from Digital Marketing, Finance, and Economics.  The Pythonprogramming language is used for examples and homework assignments throughout the course because Python is the lingua franca of the business world.

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), Temporal 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: 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.

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 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 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.

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.

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.

Covers marketing analytics techniques such as segmentation, positioning, and forecasting, which form the cornerstone of marketing strategy in industry. Students will work on cases and data from real companies, analyze the data, and learn to present their conclusions and make strategic recommendations.

Introduces the tactical and strategic issues surrounding the design and operation of supply chains, to develop supply chain analytical skills for solving real life problems. Topics covered include: supplier analytics, capacity planning, demand-supply matching, sales and operations planning, location analysis and network management, inventory management and sourcing.

Artificial Intelligence (AI) is an interdisciplinary field that draws on insights from computer science, engineering, mathematics, statistics, linguistics, psychology, and neuroscience to design agents that can perceive the environment and act upon it. This course surveys applications of artificial intelligence to business and technology in the digital era, including autonomous transportation, fraud detection, machine translation, meeting scheduling, and face recognition. In each application area, the course focuses on issues related to management of AI projects, including fairness, accountability, transparency, ethics, and the law.

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 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 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.

The care and use of data are essential to nearly all enterprises. As they enter the workforce, our students are increasingly expected to understand the entire value chain for data intensive products and services. This course builds on their previous studies in data engineering/data science/management, to train them to think critically about the process, tools, techniques, technologies, and governance for an entire data pipeline, from data through application, and to execute and document such a pipeline. The students will be presented with a combination of data and required business application information and will create case studies of a complete data pipeline. The data/application combinations will require students to think critically about all of the components of a complete project pipeline; to program such a pipeline using appropriate technology; and to write a clear, detailed report on the project, including the reasons for the decisions that were made and the alternatives that were considered.

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 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.

This course uses advanced technologies, such as IBM's Blue Mix and Google's TensorFlow, as building blocks, allowing student teams to exercise their ingenuity to develop applications that use AI and machine learning in entirely new business application areas. The products of cognitive computing are beginning to appear in the marketplace, while so-called "deep-learning" AI applications are finding their way into healthcare, energy management, security, marketing and financial services.

This course introduces fundamentals of deep learning with a focus on business applications to students in the School of Business, who, mostly, are beginners of this field. It starts with basic constructs of neural networks and progresses into widely used models including convolutional neural networks, recurrent networks, generative models, and reinforcement learning. Extensive hands-on experiments are provided in class or as assignments for students to practice each model, understand its applicable scenarios, and build practical skills. In addition, various successful deep learning business applications will be studied in this class. Moreover, the potential implications and risks of applying deep learning in the business world will be discussed, and relevant techniques to address such issues will be provided. The objective of this course is to provide students the fundamental concepts of deep learning and to build students’ practical skills of applying deep learning to solve real business problems. Prerequisite course required MIS 637 or equivalent and BIA 660.

Artificial Intelligence (AI) is an interdisciplinary field that draws on insights from computer science, engineering, mathematics, statistics, linguistics, psychology, and neuroscience to design agents that can perceive the environment and act upon it. This course surveys applications of artificial intelligence to business and technology in the digital era, including autonomous transportation, fraud detection, machine translation, meeting scheduling, and face recognition. In each application area, the course focuses on issues related to management of AI projects, including fairness, accountability, transparency, ethics, and the law.

Business and communication skills are developed through a strong learning culture nurtured by seminars, industry-supported job-skills workshops, talks by industry leaders and an active student club.

The centerpiece of the program is a rigorous 12-course curriculum that emphasizes both theory and practice, culminating in a practicum course in which students work on real applications alongside industry mentors in a student’s area of interest.

Exceptional software skills are a requirement to manipulate, analyze and mine data. To that end, students attend a series of free boot camps that provide training in industry-standard software packages, such as SQL, R, SAS, Python and Hadoop. These intensive boot camps occur over four three-day weekends in the fall and spring.

The Hanlon Financial Systems Center at Stevens is home to two labs that offer the kind of technology in use on Wall Street, from top-of-the-line data management software to Bloomberg terminals, and a number of large data sets that support research and industry-strength educational exercises. It's powered by hardware that enables the study and manipulation of enormous volumes of data.