[1] Graduate Programs

• ALY 6980, Capstone [Winter 2022, Spring, 2022, Fall 2024]: This 14 weeks intensive course (for Statistical Modeling and Applied Machine Intelligence concentrations) offers an advanced practicum in the development and delivery of predictive data analysis for strategic decision-making in organizations. Students apply the principles and tools of analytics to a comprehensive real-world problem or project within a sponsoring organization. Students are expected to present analytical insights and recommendations for the successful implementation of their capstone project. Additionally, students develop individual research proposals aligned with the business domain of the sponsor organization. Students taking this requires need to have completed all ALY courses relevant to their program concentration. 

• ALY 6080 [Spring 2023, Spring 2024]: This 14 weeks intensive course offers a practicum in the development and delivery of analytical tools (interactive dashboards and/or predictive data analysis models) for strategic decision making in organizations. Students get an opportunity to apply the principles and tools of analytics to solve real-world problems in business organizations and to develop and present analytical insights and recommendations for successful implementation of the sponsor project. Students taking this requires need to have prior training in intermediate analytics (ALY6015), enterprise analytics (ALY6050), and visualization analytics (ALY6070). 

• ALY6040, Data Mining Applications [Spring-A 2024]: This 6 weeks intensive course introduces the theories and tools for intensive data analysis methods and data mining techniques such as rule-based learning, decision trees, clustering, and association-rule mining. Also covers interpretation of the mined patterns using visualization techniques. Offers students an opportunity to gain the knowledge and experience to apply modern data-mining techniques for effective large-scale data pattern recognition and insight discovery. Data analysis software is introduced. Student teams evaluate, analyze, and report data for the methods used and insights discovered during case studies. 

• ALY6030, Data Warehousing and SQL [Summer 2024, Fall 2024]: This 6 weeks intensive course focuses on the purpose and design of data warehouses, and the mining and management of patterns in large databases. Offers students the opportunity to learn how organizations construct data warehouses from operational databases, about different data warehouse architectures, how to build a data warehouse, and how to structure databases for efficient data mining. Explains how data warehouses are contributing to decision making, what activities may follow the observations, as well as potential issues that analysts may encounter when dealing with data warehouses. Discusses relational databases and Structured Query Language (SQL) for the fundamentals in data modeling, database management, and SQL queries. Introduce other modern database systems such as NoSQL (non-SQL) and column-based databases.

• ALY 6020, Predictive Analytics [Fall-B 2020, Fall-B 2021]: In this six-week intensive course, students are introduced to the end-to-end data-driven statistical modeling and predictive modeling approach in R with applications and case studies. It includes all the data and modeling steps in a full modeling cycle, including the data ETL process, exploratory data analysis and data cleansing for outlier imputation and data normalization, modeling steps such as model training, and validation, and testing. The course covers several commonly applied modeling techniques such as linear regression, logistic regression, classification model (k-nearest neighbor), decision tree, neural networks, and naive Bayes. 

• ALY 6015, Intermediate Analytics [Spring-B 2021, Winter-A, 2022]: In this six-week intensive course, students are introduced to fundamental data due diligence, reliability, data correction, and re-coding processes and practices, in addition to expanding upon the approaches to discerning and validating patterns in data through sound applications of the scientific method. From a modeling perspective, the course emphasizes on linear regression, chi-square and ANOVA testing, regularization, and generalized linear models. The goal of this course is to endow students with the fundamental data management, review, re-engineering, and exploration skills, as necessary data analytical competencies.

• ALY 6110, Big Data and Data Management [Spring-B 2024]: In this six-week intensive course, students are introduced to the core concepts of data collection and management. Topics include systems for collecting data and implications for practice; types of data (textual, quantitative, qualitative, etc.); and storing data with privacy and security issues in mind; including a discussion of business process outsourcing. Offers students an opportunity to obtain a high-level understanding of big data technologies for data accessibility, efficiency, and security of data management at scale, including big data storage and computing technologies and big data analytics applications. Students create a working system for data acquisition and management using publicly available data. Azure is also introduced and used in the lab sessions. 

• EAI 6010, Applications of Artificial Intelligence [Summer 2021]: Artificial intelligence powers a wide range of innovations in our daily lives with applications across numerous fields and industries, including finance, healthcare, education, and transportation. This 6-week intensive course explores numerous industrial applications of AI with emphasis on solving specific needs or problems. Topics include AI learning paradigms, natural language processing, computer vision, industrial and robotic applications as well as distributed AI. 

• ITC 6000, Database Management Systems [Fall-B 2019, Winter 2020, Spring 2020, Fall-B 2021, Winter-A, B 2022]: In this 6-weeks intensive course, we primarily emphasize on developing query writing and relational data model design skills. We exploit open-source database technology such as SQLite (command shell-based; and visual tool) to write SQL for the construction of relational databases and write SQL queries of simple to significant complexity on a variety of sample databases to address business needs that require data retrieval from a single to multiple tables. Thereafter, we cover the basic concepts of database design based on the principles of the relational data model. Students are expected to design a database (entity-relation diagram satisfying requirements of at least a third normal form) and implement it using SQLite to provide a database solution for fairly complex business problems. Finally, the topics of data quality, concurrency control, data security, and data warehouse are discussed. 

[1] Undergaduate Programs

• ALY 3015, Intermediate Statistics for Data Analytics [Fall 2021]: This semester-long course introduces students to some of the most commonly used statistical techniques for analyzing real-world data. This course extensively uses RStudio & R language to perform data analysis and report generation. Each technique is introduced in the context of a business problem from which the student identifies and describes the features that the input historical data must have in order to make appropriate use of such a technique. Variations of the motivating problem are used as a way to simulate class discussion and model refinements. These simulations are also used as a means for the students to communicate the outcomes of the analytic model (and of the decisions made during modeling) to interested parties

[1] Graduate Programs

• AD 571, Business Analytics Foundations [Fall 2022, Spring 2023, Fall 2023, Spring 2024]: This 14 weeks course presents fundamental knowledge and skills for applying business analytics to managerial decision-making in corporate environments. Topics include descriptive analytics (techniques for categorizing, characterizing, consolidating, and classifying data for conversion into useful information for the purposes of understanding and analyzing business performance), predictive analytics (techniques for detection of hidden patterns in large quantities of data to segment and group data into coherent sets in order to predict behavior and trends), prescriptive analytics (techniques for identification of best alternatives for maximizing or minimizing business objectives). Students will learn how to use data effectively to drive rapid, precise, and profitable analytics-based decisions. The framework of using interlinked data inputs, analytics models, and decision-support tools will be applied within a proprietary business analytics shell and demonstrated with examples from different functional areas of the enterprise. R, SQL, and Power BI software are used in this course. 

[1] Graduate Programs

• Database Applications Development [Fall 2018; Spring 2019]: Managerial and operational decisions in organizations are increasingly based on data stored in computerized databases. The quality of these decisions depends on the quality of the database design. This course focuses on database design and database applications development, and educates students so that they can design and build practical and theoretically sound organizational databases and associated applications. To do so, students must be able to link the data needs of organizations with the technology of relational databases. Exercises and exams ensure that students understand relational database concepts and can apply the practical tools used to design and develop relational databases. The course project integrates theory with practice through the design and development of a database application for Android platform to meet specific organizational needs for data.  

• Innovations with Information Systems [Fall 2018; Spring 2019]: This course focuses on information systems (IS) and innovation. It provides the knowledge and skills to assess and apply existing and emerging information technology innovations to enable organizational processes and create business opportunities. These concepts are put into practice as students discuss case studies and reading material, and execute a group project to propose and evaluate IT based innovations. This course is primarily case-based. For the case method to be an effective teaching tool, students must be well prepared and must actively participate in all aspects of case discussions. 

[2] Undergraduate Programs

• Creating Value Through Innovations [Fall (term) 2018]: This course focuses on the ways value can be created and captured through innovation. Focusing on the assessment of customers, organizational capabilities, and competition, students will consider a variety of different types of innovations and their associated ethical and financial value propositions. Students will learn analytic tools to successfully assess and commercialize technology, product, and service innovations in a variety of contexts.

• Business Data Management [Spring (term) 2019]: This course introduces students to the theory and practice of database management and the application of database software to implement business information systems that support managerial and operational decision making. Special topics covered include relational data models, query languages, normalization, locking, concurrency control and recovery. The course covers data administration and the design of data tables for computerized databases. Students will use a commercial database package to design and implement a small business database application.

[1] Doctoral Program

• Healthcare Management and Policy Research [Spring 2016; Spring 2017]: This core course is designed for PhD students (tracks: health management or health policy) with the overarching goal of preparing them to conduct research with academic rigor.  The course heavily depends on reading and analysis of research articles (mostly from top journals) and unpublished dissertations (from leading doctoral programs in the country) in healthcare management, and health policy disciplines covering a wide variety of research methods, research topics, data sources, and theoretical frameworks. The course also exposes students to the manuscript writing, live 'journal-style' peer review process on term papers, and presentation skills. Students will also have the opportunity to review instructor's manuscripts from their primitive conference versions to published version (in leading journals) along with review comments of rejections, and revisions up to acceptance phase. Students are exposed to different research methods prevalent in healthcare management and policy disciplines through assigned readings (research articles, and unpublished dissertations). In addition, the term paper assignment for this course emphasizes on manuscript writing with comprehensive literature review, design of a feasible study grounded in theory or conceptual framework and based on publicly available data sources, selection of appropriate research methods, identification of potential analytical issues and methodological solutions.

• Policy Issues in Health Information Technology [Fall 2014; Fall 2015; Fall2017]: This elective course is designed for doctoral students. This course builds on an extensive reading list of research articles, regulations, and policy reports to critically examine policy and regulatory issues related to the use of information technologies in health care. The focus is on three areas: clinical, consumer, and population health informatics. The primary emphasis is on the U.S., but international approaches will also be discussed. Specific topics may include an in depth analysis of key regulations and related policy efforts that shape the health informatics field, with significant focus on the Health Information Technology for Economic and Clinical Health (HITECH) Act, Health Insurance Portability and Accountability Act (HIPAA), Stark Law, Patient Protection & Affordable Care Act, the CMS Innovation Center, will be covered.  

• Operations, Technology and Decision Management [Spring 2014; Spring 2015§]: This elective doctoral course, delivered in seminar style, exposes students to recent research in the field of health and medicine related to operations management, technology, and decision-making. The reading materials (published journal articles) spans a broad spectrum of theoretical issues and methodological options that can be considered to pursue future research in the related areas. Building on the literature in the healthcare and management journals, students will familiarize with how extant research has addressed management and policy challenges of clinical and administrative decision making in healthcare. The organizational context may include health services organizations, health systems, and public health agencies. Examples of topics may include, but not limited to, patient scheduling, physician and provider productivity, intra/inter–organizational care coordination, healthcare supply chain management, clinical and managerial decision processes, multi–criteria decision making among others. The selection of research articles will cover different types of quantitative research methodologies that leverage operations research, econometrics, and information systems. 

[2] Masters Program

• Information Technology in Healthcare Management [Fall 2013]: This elective course was redesigned for MPH students with specialization in healthcare management. This course provides an overview of essential operational processes in a healthcare organization and the application of information technology (“IT”) resources to those processes. Students are introduced to different health IT systems used at individual, organizational, interorganizational, and state or national levels. Additionally, management of health IT resources, and applications of spreadsheet modeling and optimization are demonstrated.

 [1] Undergraduate Program  

• MIS 325, Introduction to Management Information Systems [Fall 2006; Fall 2007]: Management and effective use of information systems and e-business technologies to improve business decision-making, conduct electronic commerce, revitalize business processes, and gain competitive advantage.   

• Principles of Database Management [Spring 2005; Spring 2006]: Database and data warehousing concepts, design principles, and methods of use in assisting management decision-making and in building Web-based database applications. Focus on widely used commercial database environments.