Curriculum: Master of Science in Information Systems

July 31, 2022
36-44 credit hours
August 22, 2022
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curriculum icon Curriculum at a Glance

The University of Cincinnati Online’s Master of Science in Information Systems program provides a flexible, innovative curriculum, accommodating students with diverse educational backgrounds and work experiences. The program is known for its strengths in the areas of ERP, business intelligence, database design and modeling, and project management. The full-time academic program can be completed in two semesters with remaining courses completed during the six-month paid internship period. Flexible class options accommodate working and part-time students.

Experience-based learning

Master of Science in Information Systems students must engage in either a six-month internship (co-op), work on an employer’s information systems project or information systems research. Information systems research should culminate in a written research paper.

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To receive an MS IS, there are certain requirements that need to be obtained. Completion of the following categories:

For students with undergraduate business degree (36 credit hours)
  • 20 hours core
  • 14 hours elective
  • 2 hours Industry Practicum
For students without undergraduate business degree (44 credit hours)
  • 20 hours core
  • 14 hours elective
  • 2 hours Industry Practicum
  • 8 hours Basic Business Knowledge (BBK)
MS IS Core Courses
  • IS 7012 – Web Development with .Net (2 credit hours)
  • IS 7020 – Systems Analysis and Design (2 credit hours)
  • IS 7024 – XML and Web Services (2 credit hours)
  • IS 7030 – Data Modeling (2 credit hours)
  • IS 7032 – Database Design (2 credit hours)
  • IS 7034 – Data Warehousing and Business Intelligence (2 credit hours)
  • IS 7036 – Data Mining for Business Intelligence (2 credit hours)
  • IS 7050 – Enterprise Resource Planning 1 (2 credit hours)
  • IS 7060 – IS Project Management (2 credit hours)
  • IS 8044 – IS Security (2 credit hours)
Electives
  • IS 8034 – Big Data Integration (2 credit hours)
  • IS 8036 – Survey of Machine Learning and Artificial Intelligence (2 credit hours)
  • BANA 6043 – Statistical Computing (2 credit hours)
  • BANA 6037 – Data Visualization (2 credit hours)
  • BANA 7025 – Data Wrangling (2 credit hours)
  • BANA 7046 – Data Mining 1 (2 credit hours)
  • BANA 7038 – Data Analysis Methods (2 credit hours)
  • Open Electives (2 credit hours)
Industry Practicum
  • IS 7092 – Industry Practicum 2 (2 credit hours)
Basic Business Knowledge
  • ACCT 7000 – Foundations in Accounting (2 credit hours)
  • IS 7011 – Information & Technology Management (2 credit hours)
  • BANA 7011 – Data Analysis (2 credit hours)
  • FIN 7000 – Foundations in Finance (1 credit hour)
  • MRKT 7000 – Marketing Foundations (1 credit hour)

Web Development with .Net

This course is an introduction to the development of web-based applications, using Microsoft's Visual Studio and covering ASP.Net using Visual C#. Students will be expected to develop a simple web application that incorporates these technologies. Students will learn how to integrate the front-end (web site) with the back end (database) of an application. The course will cover the implementation of navigational structures, input and validation controls, and data controls in web applications.

Systems Analysis and Design

There is no activity more fundamental to the field of information systems (IS) than the analysis, design, and development of systems. In this course, students will learn to analyze and document the requirements for a system, using two distinct approaches to process modeling. The first of these is BPMN (Business Process Modeling Notation) - a technique that is quickly becoming the standard for business process modeling. The second is an Object Oriented approach, using UML (Unified Modeling Language) - specifically, students will learn to draw use-case diagrams, class diagrams, and sequence diagrams.

XML and Web Services

This course introduces the concept of Service-Oriented Architecture (SOA) and its two main components - web services and XML. First, the course covers the structure of XML files, including XML Schemas and namespaces. Next, techniques to transform (XSLT) and extract information from XML files (XPATH) are presented. Finally, the main components of Web Services, such as WSDL and SOAP, are discussed. The course uses Visual Studio 20008, Visual C#, ASP .Net, and Windows Communication Foundation as a way for students to practice the concepts discussed in the lectures.

Data Modeling

This course provides in-depth coverage of the principles of data modeling. Starting at the highest level of abstraction, the data requirements culled out from user requirements specification are rendered as a conceptual data model using Entity-relationship modeling grammar. Students then learn how to map the conceptual model to the logical tier using relational modeling grammar, in preparation for the ultimate database design. Workshop sessions are included to provide students hands-on modeling opportunities. A basic introduction to Structured Query Language (SQL) is also included.

Database Design

This course provides in-depth coverage of the principles of database design. It is a follow on to IS 7030. Having learned to develop relational data models in the first course, students start this course with concepts related to validating and revising the database design using normalization theory. This is followed by relational algebra and structured query language (SQL) for data definition (DDL), data manipulation(DML), data control (DCL), and deeper level of data querying (DQL) for the implementation of the database design. Finally, higher level normalization concepts are introduced. Workshop and laboratory sessions are included to provide hands-on learning experience in normalization procedures and SQL.

Data Warehousing and Business Intelligence

This course is designed for the comprehensive learning of data warehousing technology for business intelligence. Data warehouses are used to store (archive) data from operational information systems. Data warehouses are useful in generating valuable control and decision-support business intelligence for many organizations in adjusting to their competitive business environment. This course will introduce students to the design, development and operation of data warehouses. Students will apply and integrate the data warehousing and business intelligence knowledge learned in this course in leading software packages.

Data Mining for Business Intelligence

This course is designed for the in-depth learning of data-mining knowledge and techniques in the context of business intelligence. The topics include association rules, classification, clustering and text mining. Students will apply and integrate the business intelligence knowledge learned in this course in leading software packages.

Enterprise Resource Planning 1

Enterprise Resource Planning (ERP) Systems are large, cross-functional systems designed to promote integration among the various business areas. While there are many ERP systems, SAP has, by far, the highest market share. An important step in implementing SAP is configuration, which involves selecting options in SAP to align with the specific requirements of the business. This course is a hands-on introduction to SAP configuration. Specifically, students will go through the process of setting up a small trading company on SAP, including setting up the organization structures, master data, and rules; and processing transactions to test the setup. The course covers three SAP modules - FI, MM, and SD.

IS Project Management

This course focuses on the management of IS projects, although many of the concepts examined also apply to other projects. Planning, organizing, staffing, and controlling projects require traditional management skills as well as an understanding of specific project management tools and techniques. This course starts with an overview of project management concepts. It then discusses project planning, monitoring, and controlling. It also covers the politics of projects, project staff, and teamwork issues. The Project Management Institute's "A Guide to the Project Management Body of Knowledge," along with current research and management trends related to IS project management, provide the framework for the material covered in this class. The course uses Microsoft Project for hands-on exercises.

IS Security

This course is an overview of the field of Information Security, Privacy, and Assurance. It introduces students to the key issues associated with protecting information assets, determining levels of protection and response to security incidents, and designing a consistent, reasonable information security system, with appropriate intrusion detection and reporting features. Topics covered in the course include: inspection and protection of information assets, detection of and reaction to threats to information assets, pre- and post-incident procedures, technical and managerial responses, and an overview of the Information Security Planning and Staffing functions.

Big Data Integration

This course presents an overview of the principles of data integration, the fundamental basis for developing useful and flexible business intelligence platforms. Modern data integration needs differ from traditional approaches in four main dimensions that parallel differences between big data and traditional data: volume, velocity, variety, and veracity.

Data Mining for Business Intelligence

This course is designed for the in-depth learning of data-mining knowledge and techniques in the context of business intelligence. The topics include association rules, classification, clustering and text mining. Students will apply and integrate the business intelligence knowledge learned in this course in leading software packages.

Statistical Computing

This is a course on the use of computer tools for data management and analysis. The focus is on a few popular data management and statistical software packages such as SQL, SAS, SPSS, S Plus, R, and JMP although others may be considered. Data management and manipulation techniques including queries in SQL will be covered. Elementary analyses may include measures of location and spread, correlation, detection of outliers, table creation, graphical displays, comparison of groups, as well as specialized analyses.

Data Visualization

This course provides an introduction as well as hands-on experience in data visualization. It introduces students to design principles for creating meaningful displays of quantitative and qualitative data to facilitate managerial decision-making.

Data Wrangling

This course provides an intensive, hands-on introduction to data management and data manipulation. You will learn the fundamental skills required to acquire, munge, transform, manipulate, and visualize data in a computing environment that fosters reproducibility.

Data Analysis Methods

This course covers the fundamental concepts of applied data analysis methods. Various aspects of linear and logistic regression models are introduced, with emphasis on real data applications. Students are required to analyze data using major statistical software packages. BANA 7038 should not be taken for credit by MS-Business Analytics students.

Industry Practicum 2

This course is associated with the experiential component of the MS-IS program. It is a follow on to IS 7090. The purpose of this course is to allow students to engage in longer and more complex projects (whether as part of a co-op/internship, or an independent project) that go beyond the scope of a single course (IS 7090).

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