Master's in Business Analytics & Artificial Intelligence

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 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.

This course deals with strategic uses of data, data structures, file organizations and hardware as determinants of planning for and implementing a enterprise-wide data management scheme. Major course topics include data as valuable enterprise resource, inherent characteristics of data, modeling the data requirements of an enterprise, data repositories and system development life cycles.

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 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 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 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.

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 the design and management of data warehouse (DW) and business intelligence (BI) systems. The course is organized around the following general themes: Knowledge Discovery in Databases, Planning and Business Requirements, Architecture, Data Design, Implementation, Business Intelligence, Deployment, Maintenance and Growth, and Emerging Issues. Practical examples and case studies are presented throughout the course. This course also includes hands-on application in various software packages.

Given a data matrix of cases-by-variables, a common analytical strategy involves ignoring the cases to focus on relations among the variables. In this course, we examine situations in which the main interest is in dependent relations among cases. Examples of “cases” include individuals, groups, organizations, etc.; examples of “relations” linking the cases include communication, advice, trust, alliance, collaboration etc. Application areas include social media analytics, information and technology diffusion, organization dynamics. We will learn techniques to describe, visualize and analyze social networks.

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.

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.

The significant amount of corporate information available requires a systematic and analytical approach to select the most important information and anticipate major events. Statistical learning algorithms facilitate this process understanding, modeling and forecasting the behavior of major corporate variables. This course introduces time series and statistical and graphical models used for inference and prediction. The emphasis of the course is in the learning capability of the algorithms and their application to finance, direct marketing, operations, and biomedicine. Students should have a basic knowledge of probability theory, and linear algebra.

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 real scientific question or to create a useful web application.

This course explores the area of augmented intelligence and its implications for today’s world of big data analytics and evidence-based decision making. Topics covered as part of this course include: augmented intelligence design principles, natural language processing, knowledge representation, advanced analytics, as well as generative AI and deep learning architectures. Students will have an opportunity to build business applications, as well as explore how knowledge-based artificial intelligence, generative AI, and deep learning are impacting the field of data science.

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.

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.

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 11-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.