This course discusses the process of business analytics based on data modeling 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 perform analyses with various software packages in business contexts.
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 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.
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, actions, hyperlinks, mobile, location and search engine data. Students will learn how businesses align social media analytics with their business strategies and gain insights on trends and user behaviors. An emphasis is placed on using various software tools to analyze real-world social media data.
This course focuses on realizing the business advantage of utilizing data to support managerial decisions. Students will employ a variety of software tools in the development of data warehouse, including ETL (extraction, transformation and loading), and visual data representations (e.g. dashboards, data cubes). Hands-on exercises include dimensional modeling, MS-Excel, and Tableau, among others.
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 various prescriptive analytic techniques and tools that can be used to analyze business decision problems and create business value. Topics may include deploy analytics such as aggregate planning models and complex problem solving. Analytical packages and modeling software such as Excel solver for linear and integer programming will be extensively used throughout the course. The emphasis of this course will be placed on the application of techniques and interpretation of the results.
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, studnets will learn how to use analytical models to assess various business solutions and identify the best course of action. Tjis 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.
