This course provides the knowledge about fundamentals of health Information Systems and the role of Information systems in efficient operation of healthcare organizations. The course specifically focuses on: Evolution of HMIS, HMIS components and basic HMIS functions, technology infrastructure for healthcare organizations, basic concepts such as HER, HIE, CPOE, and CDSS, HMIS standards such as HIPPA, HL7, and DICOM, strategic information systems planning for healthcare organizations, systems analysis and project management, information security issues, and role of HMIS professionals in health organizations.
This course seeks to address the second objective of the MSBA program, "Students will use contemporary information systems and analytical tools to acquire and manage data for business analytics." By completing 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.
This course teaches students how to apply computing tools to novel analytic challenges in organizational contexts. For a series of organizational analysis case problems, students will learn how to choose appropriate data, store and format it for analysis, create customized computing solutions based on programming and scripting languages, and present the results in a variety of forms, including tabular and graphic/visualization methods. Students will apply software languages such as R and Python, in desktop, cloud, and high-performance computing contexts
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 software tools such as R to identify trends, sentiment, opinion leaders and communities.
This course introduces students to data visualization and dashboarding. Students will learn best practices in data visualization, data retrieval using a 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 the most common software packages in the field.
This course provides students with knowledge and skills in the various decision analytical techniques for managerial decision making including big data analytics. A number of well-defined data mining techniques such as classification, estimation, prediction, affinity grouping and clustering, and data visualization will be covered. The Cross-Industry Standard Process for Data Mining (CRISP-DM) will also be discussed. The data mining techniques will be applied to diverse business applications including target marketing, credit risk management, credit scoring, fraud detection, medical informatics, telecommunications, and web analytics.
This course introduces students to the management and coordination of enterprise data resources to improve enterprise-wide decision-making. Students will learn how to identify key performance indicators from enterprise data, how to differentiate enterprise analytics from other forms of analytics, how to determine what proprietary data will provide analytical advantage to maximize the impact on the enterprise, recent technologies for analytics and best practices from recent cases. Students will engage in an iterative process of exploring data from multiple functional areas within an organization to derive actionable insights as well as communicate findings to help enterprises improve the quality of their decisions.
This course provides students with comprehensive understanding of problems and solutions related to information security and information assurance in organizational contexts. Students learn how to conduct quantitative and qualitative security risk assessment analyses related to site safety and security, hardware and software reliability and risks, and network reliability and security. Students will carry out data collection and analytics methodologies which address expected failures, incidence and severity of attacks, accidents and acts of nature, and their impacts on operations and budgets.
This course introduces students to modern machine-learning methods that can be applied to build predictive models & discover patterns in data for better-informed business decision-making. Students will learn implementation of the machine learning techniques in R programming language for understanding complex datasets. This course will enable students to approach business problems by identifying opportunities to derive business value from data-driven business intelligence. Prerequisites: QUMT 6303 or QUMT 3341 or equivalent
This course introduces the principles and techniques of prescriptive analytics. These provide business entities and policymakers 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.
