Albany College of Pharmacy and Health Sciences · Courses
HOI
6 courses with the subject HOI, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
HOI 600GIntro to Health Data Science
This foundational course introduces students in the MS in Health Data Science program to the core concepts, tools, and methods that define the field. Emphasizing the unique challenges and opportunities of working with health data, the course integrates statistical reasoning, computational techniques, and domain-specific knowledge to prepare students for applied work in public health, biomedical research, and clinical care settings. Students will learn to acquire, clean, analyze, and interpret complex health datasets using programming tools such as R, Python, and SQL. Key topics include exploratory data analysis, predictive modeling, machine learning, and effective data visualization, all grounded in real-world health data scenarios including electronic health records, claims data, and large-scale health surveys. The course also explores critical issues in data governance, reproducibility, and ethics, including HIPAA compliance, algorithmic bias, and responsible data sharing.
This course introduces students to the Python programming language, today’s leading data science language. Students will start the semester learning how to install and manage a Python installation. During the course, students will learn fundamental programming skills, including how to use Python to manage and learn from data. By the end of the course, students will know how to develop and deliver data products such as worklists and dashboards. HOI 610G Quantitative Regression Analysis (formerly PAD 725 G Econometrics) This course introduces students to multiple regression methods for analyzing data in economics and related disciplines. Extensions include regression with discrete random variables, instrumental variables regression, analysis of random experiments and quasi-experiments, and regression with time series data. Accordingly, the emphasis of the course is on empirical applications. (3) HOI 615G Health Outcomes (formerly PAD 615 G) This course will provide students with an introduction to the principles and techniques of pharmacoeconomics and health outcomes evaluation, and to the methodologies used by decisionmakers and stakeholders to draft and implement health policy. It builds on the economic principles presented in health economics (US and Global Health Care Systems) to describe the major components of the current U.S. healthcare system. Building on that foundation, the course introduces the techniques used for evaluation of health care interventions. These methods provide the basis for measuring and assessing the economic and non-economic consequences of healthcare interventions, emphasizing drug therapy, and pharmaceutical services. Examples of some of the economic methods introduced include cost of illness analysis, cost-minimization, cost-effectiveness analysis, cost-benefit analysis, and decision analysis. Non-economic measures discussed include general and disease specific quality-of-life (QOL) assessments and health status measurements. Students will demonstrate the ability to critique published studies which use
HOI 620Predictive Modeling for Clinical and Business Outcomes
This course introduces students to predictive modeling techniques and their application to healthcare data using Python. Students will first learn the mathematical and computer science foundations of machine learning such as the bootstrap and Monte Carlo modeling. Students will then learn a set of supervised modeling techniques such as k-means clustering, random forest, Naive Bayes, and XDG Boost. Students will learn how to determine which model to use in a given situation and how to discuss model results with non-technical stakeholders. HOI 625G Health Systems (formerly PAD 675 G) This course presents a systematic comparative analysis of the evolution, administrative structure, finance, and provision of medical care in selected countries throughout the world. Equity/inequity and the current and looming effects of globalization will be explored. This course will expand your understanding of health and illness by looking at them as socio-cultural and socio-economic phenomena. Important differences rooted in culture, ethnicity, social, economic and political factors will be examined to encourage innovative "framing" of U.S. health public policies. This course presents and facilitates the development of an analysis of major health service delivery and management issues from an international perspective. Each country in the world possesses and implements a unique health service delivery system. While there may be many factors, components and issues in common, there are nonetheless many differences. It is important to learn about and analyze other country’s healthcare systems, to learn how they treat similar issues and to discover innovations. Improvement often comes through change and innovations, and this study will not neglect the opportunity to learn from others, especially those middle and lower income countries implementing interesting and innovative reforms. By utilizing a comparable model of exploration, we will gain an understanding of the similarities and differences of industrial countries, third world countries and tribal programs in the US. (3) HOI 635G Statistical Programming (formerly PAD 636 G) The goal of this course is to introduce students to the use of the SAS programming language for analysis of health outcomes data. Students will learn to use the SAS environment to write programs for reading and processing data and to perform basic data management tasks. This course will use Base SAS and SAS Enterprise Guide to provide access to SAS software, and course-related data. (3)
The study of the distribution and determinants of health-related states or events often thought of as the “cornerstone” science of public health. This course introduces fundamental principles and methods used in epidemiology and applies these approaches to describe and evaluate causal relationships in health problems. (3) HOI 646G Epidemiology II (formerly PAD 694 G) Epidemiology is the study of the distribution and determinants of health-related states or events in specific populations and the application of this study to control health problems. These determinants are often seen in clinical practice and clinicians have a real opportunity to systematically evaluate various exposure-outcome relationships. The purpose of this course is to build on the foundations of epidemiology taught in Epidemiology 1. The emphasis of this course is application & variations on epidemiologic theory. The course is designed to develop critical thinking skills through the critique of journal articles, classroom discussion, lecture, and group exercises. Students will become aware of how to efficiently design and interpret epidemiologic studies. The course exposes students to common variants of traditional study designs and how these variations affect the validity & precision of
This course is an introduction to the statistical computing environment R. In this course you will learn how to write programs in R in order to perform tasks that quantitative researchers must perform. You will learn the packages and functions that are used in statistical analysis as well as techniques for managing data and using graphs to visually describe data. (3); Prerequisite: MAT 610; HOI 610
Big data refers to the idea that analysts manage, analyze, visualize, and extract useful information from large, diverse, distributed, and heterogeneous data sets to accelerate the progress of discovery, innovation, and information. Data are generated at such a great speed today that there is such large amounts of data that the challenge is how to develop efficient and effective computational tools to analyze the data to gain insight and make predictions; the interdisciplinary approach to machine learning, data mining, statistics, management, and analysis. This class will provide an overview of advanced machine learning, data mining, and statistical techniques that arise in data analytic applications. You will learn and practice data analytic techniques. (3); Prerequisite: MAT 610; PAD 636, PAD 725 HOI 750 G Capstone (formerly PAD 750 G) The capstone project is an integrative activity with a variety of final products based on the degree program and type
