Masters In Business Analytics

This course provides a comprehensive introduction to the foundational principles of intelligent systems, artificial intelligence (AI), and business analytics. By integrating concepts from AI, computer science, and business analytics, this course prepares students to design, develop, and implement intelligent systems that solve complex business problems. Students will explore key ideas and techniques underlying the design of intelligent computer systems, focusing on modern AI applications such as machine learning, knowledge representation, decision-making, and optimization.

The course also covers the study and application of business analytics, offering students the opportunity to learn how data can be used effectively within organizations to enhance decision-making, optimize operations, and maintain a competitive edge. Topics include descriptive analytics, predictive analytics, software engineering principles, and the ethical considerations of deploying AI and analytics in real-world environments. Through a combination of theoretical study and practical application, students will gain the skills necessary to leverage AI and business analytics in various professional settings.

This course covers manipulating structured data using different data management techniques, and analyze data requirements. Students learn to design relational databases and use SQL to define, query and update them and explore non-relational schemaless databases, and query them. Concepts of the cloud, big data, and cybersecurity as they relate to the management of database systems.

This course reviews the key analytics methods for using data through the perspectives of applied statistics and operations analysis. The course covers application of these methods to business areas including marketing, supply chain management, and finance. Topics include business-analytic thinking; application of business analytics solutions to business problems; data mining, supervised and unsupervised machine learning; methods for detecting co-occurrences and associations; and achieving and sustaining competitive advantage by using business analytics methods.

Focuses on the principles and practices of managing data at scale. It emphasizes the valid and efficient collection, storage, management, and processing of datasets to support computation and data driven systems important to data science and data analytics functions.

Fundamentals of data engineering pipelines with particular focus on extracting, transforming, combining, validating and loading data for further analysis and visualization. Topics include, but are not limited to, navigating the Linux operating system, version control and collaboration, SQL and NoSQL databases, distributed computing, and high-level programming.

This course introduces students to the field of data mining and data analytics, covers key concepts, techniques, methods, and applications of data mining in the context of business. Offers students opportunities to learn how to distill key insights from a large amount of unknown data, which techniques to choose from, how to apply the techniques and methods to get the answer and insights from the data, and how to interpret the results from the analysis.

This course is focused on Systems, Applications and Products (SAP) functional and technical modules. Students learn how to use SAP software to manage multiple aspects of a business, including finances, operations, facilities, and human resources. Students will learn how to use the SAP functional modules to provide standard functionality to simulate actual business activity. SAP technical modules enable professionals to troubleshoot performance issues, schedule tasks, develop applications, download, and install updates and manage and execute migrations.

Students learn Machine Learning techniques and deal with the issues of extracting information and knowledge from large data sets. The extracted knowledge is subsequently used to support human decision-making with respect to summarization, prediction, and the explanation of observed phenomena. The course covers machine learning foundations, different methodological approaches such as logistic regression, decision, trees, and neural networks which are used to discover relationships and patterns, and implementation tools for machine learning for business applications.

In this course, we understand an in-depth study of the current state-of-the-art and master the research methodology used in Software Engineering. Selected topics will be from areas such as Software Engineering Methodologies, evidence-based best practice strategies, software maintenance, software testing, model-driven engineering, human factors in software engineering, emerging technology and applications, applying optimization techniques in software engineering, and empirical software engineering.

The ELITE Leadership course is designed to develop the soft skills necessary to manage staff and lead projects in today’s complex work environment. Students will construct a Personalized Activity Calendar that emphasizes ELITE’s Guiding Principles of Focus, Design and Assessment. Leadership principles associated with Team Building, Management Styles, Listening Effectiveness, Training & Coaching Techniques, Managing Motivations, Goal Setting, and Performance Reviews will be discussed in detail and introduced into an amended Personal Activity Calendar as a final course project.

This course provides graduate students with an opportunity to put into practice the theoretical knowledge they learned and the skills they have earned during their program of study in the software field. Students work in teams or an individual to define a problem or select a problem introduced by their faculty advisor to design, develop, and provide a substantial solution, then deploy a real-world system, demonstrate the system, and present their methodology and final product to faculty and peers.