Courses:
Master of Science in Business Analytics and Artificial Intelligence with a Specialization in Healthcare Analytics Online

This course discusses the process of business analytics by developing a business intelligence solution, including problem definition, data preparation, descriptive and predictive analyses, evaluation of results, implementation and deployment. Data-oriented methods using spreadsheet and structured query language (SQL) are emphasized for business transaction capturing, data aggregation and online analytic processing (OLAP). Students will employ a variety of software tools in the development of a data warehouse, including ETL (extraction, transformation and loading) and visual data representations (e.g., data cubes).

Duration: 7 weeks
Credit Hours: 3

Novel problems require innovative solutions - this course introduces students to the power and flexibility of programming and scripting languages such as R and Python, applied to problems in business analytics. Students will learn how to acquire and deploy software packages relevant to their problem, then use them together with tools such as SQL to collect and prepare data, customized analyses according to specific needs, and create outputs which effectively communicate the results.

Duration: 7 weeks
Credit Hours: 3

This course introduces students to the concept of social media analytics and techniques used to analyze social media data such as texts, networks, and actions. Students will learn how to extract data from popular social media platforms and analyze such data using industry-standard software tools to identify trends, sentiment, opinion leaders, and communities through social network analysis and natural language processing.

Duration: 7 weeks
Credit Hours: 3

This course introduces students to data visualization and dashboarding. Students will learn best practices in data visualization, data retrieval using structured query language (SQL), polish analytical skills, and learn how to design dashboards to support managerial decisions. Students will have the opportunity to gain hands-on experience in data retrieval and visualization. Students will use Tableau as their main tool for data visualization and dashboarding but will develop transferable skills which can apply to most common software packages in the field.

Duration: 7 weeks
Credit Hours: 3

This course provides students with the knowledge, skills and techniques for unsupervised machine learning, deep learning, and artificial Intelligence for Business Analytics. Several techniques will be applied to diverse business applications. Prerequisite: QUMT 6303 or QUMT 3341 or equivalent.

Duration: 7 weeks
Credit Hours: 3

An introduction to statistical methodology to include probability concepts, inference techniques, analysis of variance, regression analysis, chi-square, and other non-parametric analyses. This course focuses on the use of the computer in performing statistical analysis.

Duration: 7 weeks
Credit Hours: 3

This course addresses real-world business applications of machine learning and artificial intelligence for strategic decision-making. Students will implement AI and machine learning solutions to unlock valuable insights from complex business data. The course enables students to drive business transformation through AI-powered analytics and data-driven business intelligence. Prerequisite: QUMT 6303 or QUMT 3341 or equivalent.

Duration: 7 weeks
Credit Hours: 3

This course introduces the principles and techniques of prescriptive analytics. These provide business entities and policy makers with rational tools for evaluating performance, making decisions, designing strategies, and managing risk. Students will learn how to use analytical models to evaluate uncertainty that is prevalent in many business decisions. Since Business problems often have alternative solutions, students will learn how to use analytical models to assess various business solutions and identify the best course of action. This course involves a hands-on learning experience with spreadsheet modeling and other analytical packages. The emphasis is on how to employ these analytical methods to facilitate managerial decision making in diverse industries and functional areas.

Duration: 7 weeks
Credit Hours: 3

This course focuses on spreadsheet modeling to support decision making by organizations in service industries, such as healthcare, banking, distribution, and education. Students develop critical thinking and problem solving skills to address real-world problems. The spreadsheet modeling capability acquired is highly practical for managers and administrators. Course topics cover display charts, data exploration. decision-making logic, reference functions, financial impact of loans and investments, project management, what-if analysis, goal seek. visual basic programming, and other advanced tools.

Duration: 7 weeks
Credit Hours: 3

This course provides the knowledge about fundamentals of health information systems (HIS) and the role of information systems in efficient operation of healthcare organizations. The course specifically focuses on: evolution of HIS, HIS components and basic HIS functions, technology infrastructure for healthcare organizations, basic concepts such as EHR, HIE, CPOE, and COSS, HIS standards such as HIPAA, HL7, and DICOM, strategic information systems planning for healthcare organizations, systems analysis and project management, information security issues, and role of HIS professionals in health organizations.

Duration: 7 weeks
Credit Hours: 3

Upon completion of this course, the students will build skills to acquire, create, examine, and manage healthcare data. The course introduces students to contemporary sophisticated data management and analytics software that is most used by the healthcare industry. The students will develop competency in data formats, data conversion, data export-import, data acquisition and cleansing, data dictionary and data manipulation methods, setting domains, constraints, optimum data types, advanced SQL queries, data visualization, and management of data resources including backups and restore.

Duration: 7 weeks
Credit Hours: 3

This course introduces concepts, techniques, and tools for managing and understanding data in healthcare. The course focuses on teaching students to use healthcare data to make decisions, transform health care delivery, and improve public health. Students will learn how to collect, process, analyze, visualize, and report structured and unstructured clinical and operational data. Topics covered include healthcare data measurement, statistical analysis, and data mining. This course will discuss challenges related to healthcare analytics such as data privacy, security, and interoperability.

Duration: 7 weeks
Credit Hours: 3

This course offers a comprehensive introduction to deep learning and its applications in artificial intelligence, integrating both theoretical foundations and practical applications. Students will explore essential concepts including convolutional neural networks (CNNs), unsupervised learning, generative adversarial networks (GANs), and more. Through a combination of lessons, quizzes, hands-on exercises, and a group project, students will develop expertise in applying deep learning techniques to solve complex problems in areas like image classification, object detection, and natural language processing. By the end of the course, students will be equipped to design deep learning solutions across a variety of industries, such as healthcare, e-commerce, insurance, transportation, and cybersecurity.

Duration: 7 weeks
Credit Hours: 3

This course covers the fundamentals of generative AI theory and applications, such as Large Language Models (LLMs), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs), while emphasizing hands-on experience in deploying these models in real-world business scenarios. Students will gain expertise in using state-of-the-art frameworks and tools to generate text, images, and other media, while also learning how to fine-tune and optimize these models for specific business tasks such as content generation, personalization, automation, and creative augmentation. Prerequisite: INFS 6351 or INFS 6359 or INFS 6370 or QUMT 6350.

Duration: 7 weeks
Credit Hours: 3