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Below is a listing of concentrations and their courses, offered by the Department of Management Science & Information Systems.
Note that not all courses are offered every semester.
As rapidly growing amounts of data are created and used in industry and research environments, there is an increasing demand for people who are able to pursue data-driven thinking and decision-making using meaningful insights derived from large and diverse data. This concentration prepares students to deal with situations involving data-driven decision-making such as finding patterns in large amounts of data and using such discoveries to solve data problems and make useful predictions. Students will be trained in statistics fundamentals, basic computer programming, and machine learning algorithms that tap into knowledge on both fronts.
Information Technology (IT) has been the driving force behind the new way of doing business. IT has enabled modern organizations to achieve tremendous progress in productivity, has opened new markets, and has created new product and service opportunities. This concentration helps future managers understand how IT could help to organize the complexity of modern organizations, manage relationships with customers, suppliers, and employees, and improve work efficiency.
This concentration prepares students to utilize collected data to make predictions and discern in them patterns of objects being observed. Statistical analyses, along with forecasting and data mining techniques will be taught. Classroom teaching will be combined with software use.
This concentration enables students to take advantage of detailed technical knowledge about decision making in business environments brimming with uncertainty and risk-related concerns. It balances between foundational theories concerning optimization and stochastics, as well as programming-language know-hows. Both operational and financial aspects of running real businesses will be covered. Special attention will be paid to the data extraction, forecasting, and pricing/revenue decision areas of the retail and airline industries.
Recent years have witnessed widespread use of computers and their interconnecting networks. This demands additional computer security and privacy measures to protect the information and relevant systems. This concentration prepares the students to meet the new challenges in the world of increasing threats to computer security and privacy by providing them with an understanding of the various threats and countermeasures. Students will be trained in the principles underlying security and privacy and will also learn the fundamentals security and privacy models, mechanisms, and state-of-the-art technologies like blockchains.
The courses below are specific to the Management Science & Information Systems concentrations listed above; however, other courses are available within other master's level programs that students can take through the rSBI program.
This course introduces statistics as applied to managerial problems. Emphasis is placed on conceptual understanding as well as conducting statistical analyses. Students learn the limitations and potential of statistics, gain hands-on experience using Excel, as well as various statistical techniques such as sampling, inference, and regression.
This course provides students with an understanding of database technology and its application in managing data resources. The conceptual, logical, and physical design of databases will be analyzed. A database management system will be used as a vehicle for illustrating some of the concepts discussed in the course.
The objective of this course is to study management’s role in the development and use of information systems that help businesses achieve their goals and objectives. This course will let students use a real software product, understand how it is developed, and how it is helping real business enterprises in achieving productivity and value. Students will learn the architecture, development methodologies, marketing infrastructure, support mechanisms, database interfaces, server security, and management issues as related to the software, customer interface, and documentation techniques. Extensive case studies will be used. Writing and presentations are major components. A professionally developed and publishable paper is due from each group of students.
This course prepares the students to meet the new challenges in the world of increasing threats to computer security by providing them with an understanding of the various threats and countermeasures. Specifically, students will learn the theoretical advancements in information security, state-of-the-art techniques, standards and best practices. Topics covered in this course include security policies, models and mechanisms for secrecy, integrity and availability; operating system models and mechanisms for mandatory and discretionary controls; and data models, concepts and mechanisms for database security. Basic cryptology and its applications; Security in computer networks and distributed systems; Identity threat; Control and prevention of viruses and other rogue programs.
Given the ubiquity of data collection and analysis nowadays, the challenge is to enable the legitimate use of collected data without violating privacy. From the organizational perspective, enabling safe and secure use of owned data can lead to great value added and return on investment. This course enables students to analyze the legal and social aspects of privacy and explores potential tools, techniques and technologies that can enhance privacy. The course introduces students to the core issues surrounding privacy, security, data storage and analysis and the technologies that have been developed to address those issues.
This course provides students a basic understanding of Blockchain—the underlying technology on which Bitcoin, Etherum, and the Libra cryptocurrencies are based. Blockchain has found numerous applications in banking, health-care, supply chain, auditing systems, and even in the music industry and other creative disciplines. This class introduces the foundational knowledge from Cryptography and Distributed Computing necessary to understand in detail how Blockchain is formed and operates, and presents a selection from the most successful applications of Blockchain technologies. Importantly, during this class, the students will have the chance of working on a real distributed Blockchain systems developed in Python. By dissecting actual state-of-the-art code, analyzing and modifying it, students will obtain working knowledge of subjects that address the most subtle details of Blockchain technologies.
Recent advances in information technology along with the phenomenal growth of the Internet have resulted in an explosion of data collected, stored, and disseminated by various organizations. Because of its massive size, it is difficult for analysts to sift through the data even though it may contain useful information. Data mining holds great promise to address this problem by providing efficient techniques to uncover useful information hidden in the large data repositories. Awareness of the importance of data mining for business is becoming widespread. The industry has created more and more job opportunities for people who have interdisciplinary data analytic skills. Indeed, this course intends to bridge the gap between data mining techniques and business applications. The students have the opportunity to learn both domain and technical knowledge to face the Big Data challenges in the industry.
This course teaches the principles of programming for business analytics using the Python and R programming languages. Programming is the fundamental skill based on which all Information Systems are built. The course provides students with a working knowledge of programming and fundamental insights into what a programmer does.
Over the last few years Information Technology (IT) teams have evolved and continue evolving to establish IT organizations as business strategic partners, and CIOs and technology leaders are now included in the executive teams and are expected to play a leading role in delivering business value while solving both business and technical problems. Companies are increasing their investments in acquiring and maintaining information on themselves, the markets and on competitors, and they need systems and IT teams to enable a strategic use of the information that makes it a business asset to the organization. Developing and executing an effective Information Technology strategy that enables business strategy is critical for creating business value and gaining competitive advantage. This Course presents a framework and methodology for assessing, developing and implementing an effective IT strategy that is aligned with business needs. The course will be a combination of directed readings, lectures, case studies, one individual assignment and one group project.
This course offers students a practical introduction to using Machine Learning algorithms, tools, and techniques for solving problems that fall under the umbrella of Data Science. It is structured around learning concepts from the field of Machine Learning and applying them to data-intensive problems. While the course covers theories of Machine Learning and tools such as R, the focus is on using them for solving data-driven problems. The students will be introduced to several real-life problems that involve analyzing data for prediction, classification, organization, estimations, and pattern recognition.
This course teaches the foundations of optimization relevant to data science. Concepts and tools to be covered will include convexity, the Karush-Kuhn-Tucker optimality conditions, and duality. Various algorithms in the context of machine learning will be discussed as well. These include gradient- and descent-based methods. Also touched on will be topics such as online and stochastic gradient methods.
This course provides students with a basic understanding of the modern theory and practice of dynamic pricing and revenue management. It covers topics such as market-response models, economics of revenue management, estimation and forecasting, single-resource capacity control, network capacity control, overbooking, dynamic pricing of reversible and irreversible varieties, auction, competitive pricing, joint inventory-price control, and control with ambiguity.
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Distinguished Professor Benjamin Melamed
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