University at Buffalo School of Management

The MIS practicum course is the capstone course for the MS-MIS program. This course is geared towards providing the student with practical experience in the MIS area through an MIS internship with an external organization or an MIS project within the university under the academic supervision of a faculty mentor in the MSS department. The course will allow the student to integrate knowledge and skills acquired in the MS-MIS program through their application in a practical setting. AVAILABLE ONLY FOR MS MIS STUDENTS – F/S grading only. OFFERED SPRING OR SUMMER MGS 650 INFORMATION ASSURANCE (3) This is an interdisciplinary course in Information Assurance that has two primary objectives: 1) to introduce students to fundamental concepts, terminologies, IA models and practices. 2) to view how different fields of disciplines interact in this area. The course will familiarize students with the technical, legal, socio-political, and managerial issues of IA. Broadly, the issues that we will cover in this course include: security investigation and analysis; ethical, legal, and professional aspects of Information assurance; risk management and implementation and maintenance of information assurance. Prerequisite: MGS 602 or permission of instructor. NORMALLY OFFERED IN FALL. MGS 651 MANAGEMENT OF COMPUTER NETWORKS (3) The area of computer networks is currently the fastest growing part of the MIS world. This course will discuss concepts in the management of computer networks. It is an introductory course which will discuss issues in designing computer networks, administration, network operating systems and network interconnection alternatives. A hardware lab on networking will provide a "hands-on" part of the course. The course will provide the MBA (MIS concentration) student with the foundation skills to develop a career in the field of networks through their exposure to network administration, design, pricing, etc. In addition, the hardware lab for hands on network administration is unique and is not available for student use anywhere else on campus. Course topics will be drawn from: introduction to microcomputers and Local Area Networks; LAN hardware, systems software, and installation; LAN administration; Wide Area Networks; and emerging technologies. Prerequisite: MGS 602 or permission of instructor. NORMALLY OFFERED IN SPRING. MGS 653 SOCIAL NETWORK ANALYTICS (3) The aim of the course is to provide students with an overview of measures, models and methods of analysis that can be used to study social networks to further business interests within organizations using data from internal and external IT data sources. The focus of the course will be on modeling methods and IT tools to analyze large volumes of data for predictive and descriptive analysis. Students will also learn the use of standard statistical software packages such as SAS and special network analysis software. NORMALLY OFFERED IN SPRING MGS 655 DISTRIBUTED COMPUTING (3) A Distributed Computing System (DCS) interconnects many autonomous computers to satisfy the information processing needs of modern enterprises. The objectives of this course are twofold: (1) gain a solid understanding of the technical issues, concepts and systems in the rapidly advancing technologies in distributed computing, and (2) acquire a substantial level of skills in designing, programming and implementing web based systems. The course is organized into two parts: study of DCS technologies, and study of object-oriented systems. We will address communication and networking services, application support services, distribute object management, interoperability/open systems, and distributed system design. We will overview the art of object oriented programming and follow it up by a detailed study of Java. Prerequisite: MGS 602 or concurrent with MGS 602. NORMALLY OFFERED IN FALL. THIS IS A FOLLOW-UP COURSE TO MGS 602 MGS 657 ONLINE ANALYTICAL PROCESSING: DATA WAREHOUSING WITH SAS (3) In large organizations data invariably resides in multiple-platforms and in multiple formats. Therefore, it becomes essential to pre-process the data at appropriate levels of normalization for meaningful analysis. 57

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