Courses and Sequence
The Master of Science in Computer Information Technology at Elmhurst requires the successful completion of 10 courses for a total of 30 semester hours.
All courses are eight weeks in length. Class schedule is based on program start point and area of concentration. Please contact the program director to plan your courses accordingly. Instructors are always available to meet with individual students, either on-campus or virtually, by appointment.
Course offerings reflect the 2021-2022 Elmhurst University Catalog. One unit of credit equals four semester hours.
Project Management teaches students the art and science of project management as applied to a variety of business and technology settings. Students will learn and practice project techniques that relate to the five phases of project management: initiating, planning, executing, monitoring and controlling, and closing projects. The program allows students to immediately practice course concepts in various activities in which they will create key project documents, including a business case, project charter, scope statement, WBS and a project plan.
Topics include an integrated and detailed comparison of relational, hierarchical and network database systems. Database design and physical storage requirements, including distributed database design and related management issues, are discussed. High-level query languages using artificial intelligence techniques are reviewed along with other topics such as database compression, encryption and security.
Explores the threats and risks prevalent in today’s organizations as a result of the pervasive use of technology. Students learn risk evaluation techniques and identify security and control techniques to minimize the potential of a security breach.
This course discusses data communication fundamentals and concepts such as Nyquist and Shannon theories. Included in this discussion are the topics of architecture, topologies, applications and security of local and wide area networks (LAN/WAN), TCP/IP, packets and datagrams.
This course focuses on the architecture, components, design and installation of local and wide area networks (LAN/WAN). Included in the discussion are the topics of Network Operating Systems (NOS), DNS, DHCP, Active Directory, data storage, NAS, SAN, DNS, SMTP, SNMP, Apache/IIS Web Server and VPN. Students will also learn about Firewall administering networks using a network operating system such as Windows Server or UNIX.
This course discusses Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) as two of the three fundamental layers of the Cloud Computing model, and focuses on hardware being provided by an external entity. This course introduces students to IaaS using Microsoft Azure as the cloud computing service provider. Students will learn about provisioning and managing virtual servers (Windows and Linux), network security groups, and other virtualized hardware resources.
In this course, a final team project is developed and presented at the University’s Research and Performance Showcase to demonstrate the integration of all aspects of the MCIT program. In this course students will develop the necessary source code and cloud infrastructure (IaaS and/or PaaS) to support the implementation of the project they designed in the previous course.
Prerequisite: completion of other core courses.
Electives (Choose Three)
This course focuses on the integration of information systems in organizations, the process by which different computing systems and software applications are linked together physically or functionally. The course will explore tools and techniques for systems integration and automation, PowerShell scripting as well as proven management practices for integration projects.
This course discusses the fundamental technologies, products and procedures involved in creating and administering internetworks within industry. Various network technologies designed to be interconnected by routers, switches and other networking devices to create an internetwork are also discussed. Included are topics such as VLAN, routing models, design and implementation of internetworking with TCP/IP, and IPX/SPX using Cisco Internetworking Operating Systems (IOS) and Cisco routers and switches.
This course introduces students to datacenter virtualization concepts. Students will learn about hardware virtualization including a discussion of Tier-1 vs. Tier-2 hypervisors, virtual machine storage, virtual networking and access control. Upon completion, students should be able to perform tasks related to virtual machine and hypervisor installation, configuration, and the management of virtual machines using VMware.
This course introduces students to the principles of user interface and user experience design. Students will learn various design patterns, and how to apply them to the creation of storyboards, mockups and prototypes for web and mobile applications. These designs will be implemented in subsequent courses.
The ability to move data along the continuum from information to insight to action requires a strong foundation of skills in various quantitative methods. This course begins with a systematic and integrated overview of concepts from probability theory, statistics and mathematical modeling such as probability distributions, cumulative probability distributions, descriptive statistics, hypothesis testing, correlation analysis, linear regression, multivariate regression and mathematical model design. The course then proceeds to examine modern tools for conducting analyses using these quantitative methods on both small-scale and large-scale datasets. Case studies from a variety of settings are used to develop students’ abilities to successfully apply the techniques learned in this course to practical circumstances that often, because of the ambiguities involved, present limitations to the power of these mathematical tools. Topics from this course also provide the foundation for some subjects covered in the analytical methods course and the data mining and business intelligence course.
Business intelligence represents a conceptual framework for decision support. It combines analytics, data warehouses, applications and methodologies to facilitate the transformation of data into meaningful and functional information. The major objective of business intelligence is to enhance the decision making process at all levels of management. Data mining is a process that utilizes statistical analysis, probability theory, mathematical modeling, artificial intelligence and machine learning techniques to extract useful information and subsequent knowledge from large data repositories, commonly referred to as “big data.” This course examines a number of emerging methods proven to be of value in recognizing patterns and making predictions from an applications perspective. Students will be provided the opportunity for hands-on experimentation using software and case studies.