The rSBI Certificate in Management Science & Information Systems

The Rutgers Stackable Business Innovation (rSBI) Program

Concentrations and Courses

Data Analytics and Machine Learning

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.

Managerial Information Technology

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.

Data Forecasting and Mining

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.

Business Analytics for Operations

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.

Concentration Course Descriptions

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.

Data Analysis and Decision Making

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.

  • Course Number: 22:960:575
  • Prerequisites: Basic statistics and calculus
  • Credits: 3
  • Delivery Mode: In-person

Business Data Management

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.

Course Number: 22:198:603
Prerequisites: None
Credits: 3
Delivery Mode: In-person

Information Technology for Managers

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.

  • Course Number: 22:198:609
  • Prerequisites: None
  • Credits: 3
  • Delivery Mode: In-person

Data Mining

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.

  • Course Number: 22:198:650
  • Prerequisites: None
  • Credits: 3
  • Delivery Mode: In-person

Business Analytics Programming

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.

  • Course Number: 22:198:660
  • Prerequisites: None
  • Credits: 3
  • Delivery Mode: In-person

IT Strategy

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.

  • Course Number: 22:198:670
  • Prerequisites: None
  • Credits: 3
  • Delivery Mode: In-person

Machine Learning for Data Science

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.

  • Course Number: 26:198:685
  • Prerequisites: Experiences with programming and statistics
  • Credits: 3
  • Delivery Mode: In-person

Optimization Foundations for Data Science

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.

  • Course Number: 26:711:685
  • Prerequisites: None
  • Credits: 3
  • Delivery Mode: In Class

Dynamic Pricing and Revenue Management

This course provides students a basic understanding on 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.

  • Course Number: 26:711:685
  • Prerequisites: None
  • Credits: 3
  • Delivery Mode: In Class

Enrollment to Open 2021- Request Information

Enrollment for The Rutgers Stackable Business Innovation Program (rSBI) will begin in 2021, however; you can start taking courses today as a non-matriculated student to get exposure to the high-quality content this program has to offer. To express interest in the Rutgers Stackable Business Innovation Program (rSBI) or enroll in a course, please complete the form below we will contact you.

 

Program Manager
Luke Greeley

Program Director
Distinguished Professor Benjamin Melamed

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