Systems & Data Architectures

Open to Full-Time MS in AI in Business Students 

(Internship and Non-Internship Track)

This certificate demonstrates expertise in data systems and management and front-end development for AI-driven applications. It highlights specialized skills valued in today’s data-driven industries. MSAIB students can apply credits from their master’s degree toward earning this certificate without incurring additional tuition costs. This cost-effective credential enhances résumés and sets graduates apart, emphasizing their ability to create systems and data-based solutions for an evolving landscape.

Specialize in Systems & Data Architectures.

This Advanced Certificate of Achievement demonstrates a competence in systems & data architectures.

To earn it, an MSAIB student must complete 7.5 credits. Students are required to take the following required courses (each worth 2.5 credits): 

CIS 438: Agentic AI Application

This course introduces the basics of developing web-based AI and business analytics applications, with three objectives. The primary objective is to prepare students for projects that require not only the analysis of data or the use of modern AI techniques, but also the creation of an efficient and fully functional user interfaces supporting many concurrent users. The second objective is to pave the way for subsequent courses so that students can use what is taught in this course to create a front-end user interface for what they learn in other analytics courses. The third objective is to help students develop a personal branding site showcasing their knowledge base and skill sets. 

CIS 467: Data Management and Warehousing

This course focuses on database design, management, and warehousing concepts to support AI and analytics efforts. The course introduces ER diagrams, SQL programming and related frameworks for data retrieval and transformation as well some advanced SQL features such as user-defined functions, triggers and procedures.

CIS 431: Big Data

This class offers an introduction to big data concepts, environments, processes, and tools from the perspective of data analysts and data scientists. The course sets the scene for the emergence of big data as an important trend in the business world and explain the technical architectures that make analyzing data at scale possible. The hands-on portion of the class focuses on the major tools of the Hadoop big data ecosystem such as HDFS, Hive, Zeppelin, Spark and Spark MLlib. In addition, students gain a broad understanding of the role of MapReduce, Tez, Impala, YARN, and other big data technologies. Students use a live Hadoop cluster hosted on Amazon Web Services (AWS) and thus also have an opportunity to understand the characteristics of cloud computing and storage solutions and their growing role in big data analytics.