MS in Health Informatics, Applied Data Analytics Concentration

This course is a technically-oriented introductory survey of information technology. Students learn about basic computer information, different types of business systems and basic systems analysis, design and development. Students also study basic mathematics, software development and create simple Java programs.  [ 4 cr. ]

This course is designed for IT professionals, and those training to be IT professionals, who are preparing for careers in healthcare-related IT (Health Informatics). This course provides a high-level introduction into basic concepts of biomedicine and familiarizes students with the structure and organization of American healthcare system and the roles played by IT in that system. The course introduces medical terminology, human anatomy and physiology, disease processes, diagnostic modalities, and treatments associated with common disease processes. IT case studies demonstrate the key roles of health informatics and how IT tools and resources help medical professionals integrate multiple sources of information to make diagnostic and therapeutic decisions.  [ 4 cr. ]

Undergraduate Prerequisites: (METCS570) - This course presents the fundamental principles, concepts, and technological elements that make up the building blocks of Health Informatics. It introduces the characteristics of data, information, and knowledge in the domain, the common algorithms for health applications, and IT components in representative clinical processes. It presents the conceptual framework for handling biomedical data collection, storage, and optimal use. It covers the concepts of population health and precision medicine and the information systems that support them. It introduces basic principles of knowledge management systems in biomedicine, various aspects of Health Information Technology standards, and IT aspects of clinical process modeling. Students design a simple Health Informatics solution as a term project.  [ 4 cr. ]

Health Information Systems are comprehensive application systems that automate the activities of healthcare delivery including clinical care using electronic health records (EHRs), coordination of care across providers, telehealth, management of the business of healthcare such as revenue cycle management, and population health management. The course covers the functionality of these systems, the underlying information technology they require and their successful operations. It addresses challenges in this rapidly changing field such as complex data, security, interoperability, mobile technology and distributed users. The course emphasizes applied use of health information systems through case studies, current articles, and exercises.  [ 4 cr. ]

Laws, regulations, and ethics guide the practice of health information management (HIM) and health informatics (HI). This course introduces students to the workings of the American legal system and concepts and theories of ethics, examines the legal, ethical, and regulatory issues that impact the protection of confidentiality and integrity of patient information, and, on the other hand, the improvement of accessibility of patient information to enable healthcare providers to make informed decision based on complete patient data. We will cover laws and regulations that are central to the HIM and HI professions, including Privacy Act of 1974, the Health Insurance Portability and Accountability Act (HIPAA), the Genetic Information Nondiscrimination Act of 2008 (GINA), the Health Information Technology for Economic and Clinical Health (HITECH) Act, the Food and Drug Administration Safety and Innovation Act (FDASIA), the 21st Century Cures Act, and the Confidentiality of Alcohol and Drug Abuse Patient Records Regulations, and more. The goal is to enable HIM and HI practitioners to make effective and informed decisions that prompt patient safety and care quality improvement.   [ 4 cr. ]

Undergraduate Prerequisites: (METCS570) - This course presents the details of information processing in hospitals, hospital information systems (HIS), and more broadly health information systems. It presents the architecture, design, and user requirements of information systems in health care environment. It focuses on Information Technology aspects of Health Informatics specifically addressing the design, development, operation, and management of HIS. The first part of this course covers the introductory concepts including information processing needs, and information management in health care environment. The second part covers detailed description of HIS including hospital process modeling, architecture, quality assessment, and applicable tools. The final part of the course covers management of HIS and related issues and extension of this topic to other health care organizations. The course will have a term project providing students a hands-on experience in design and research of HIS. Prereq: MET CS 580; or instructor's consent.  [ 4 cr. ]

Undergraduate Prerequisites: (METCS625) or instructor's consent - This course introduces fundamental concepts, principles of cybersecurity and their use in the development of security mechanisms and policies. Topics include basic risk assessment and management; basic legal and ethics issues, various cyber attacks, defense methods and tools; security principles, models and components; different crypto protocols, techniques and tools, including symmetric and asymmetric encryption algorithms, hashing, public key infrastructure, and how they can be used; security threats and defense to hardware, operating systems, networks and applications in modern computing environments. Hands-on labs using current tools are provided and required. Prerequisite: METCS535 or METCS625 or instructor's consent.  [ 4 cr. ]

Prerequisites: METCS 200 or 300 or Instructor's Consent. Not recommended for students without a programming background. - This course covers the concepts of object-oriented approach to software design and development using the Java programming language. It includes a detailed discussion of programming concepts starting with the fundamentals of data types, control structures methods, classes, applets, arrays and strings, and proceeding to advanced topics such as inheritance and polymorphism, interfaces, creating user interfaces, exceptions, and streams. Upon completion of this course the students will be able to apply software engineering criteria to design and implement Java applications that are secure, robust, and scalable  [ 4 cr. ]

This course covers the concepts of the object-oriented approach to software design and development using Python. It includes a detailed discussion of programming concepts starting with the fundamentals of data types, control structures methods, classes, arrays and strings, and proceeding to advanced topics such as inheritance and polymorphism, creating user interfaces, exceptions and streams. Upon completion of this course students will be able to apply software engineering principles to design and implement Python applications that can be used in with analytics and big data. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Creativity/Innovation, Critical Thinking.

Prerequisite: Programming experience in any language. Or Instructor's consent.  [ 4 cr. ]

Undergraduate Prerequisites: On Campus Prerequisites: MET CS 200 Fundamentals of Information Techno logy. Or instructor^s consent. ; Undergraduate Corequisites: Restrictions: MS CIS only. This course may not be taken in conjunction with CS 425 (undergraduate) or CS 535. Only CS 535 or CS 625 can be c ounted towards degree requirements. - This course presents the foundations of data communications and takes a bottom-up approach to computer networks. The course concludes with an overview of basic network security and management concepts. Prereq: MET CS 200, or instructor's consent. This course may not be taken in conjunction with MET CS 425 (undergraduate) or MET CS 535. Only one of these courses can be counted towards degree requirements.  [ 4 cr. ]

Undergraduate Prerequisites: Basic programming knowledge or instructor's consent. - Object-oriented methods of information systems analysis and design for organizations with data- processing needs. System feasibility; requirements analysis; database utilization; Unified Modeling Language; software system architecture, design, and implementation, management; project control; and systems-level testing. Prerequisite: Basic programming knowledge or instructor's consent.  [ 4 cr. ]

Prereq: AD100 Pre-Analytics Laboratory and ADR100 Introduction to R
This 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.   [ 4 cr. ]

Formerly titled CS 544 Foundations of Analytics with R.
The goal of this course is to provide students with the mathematical and practical background required in the field of data analytics. Probability and statistics concepts will be reviewed as well as the R tool for statistical computing and graphics. Different types of data are investigated along with data summarization techniques and plotting. Data populations using discrete, continuous, and multivariate distributions are explored. Errors during measurements and computations are analyzed in the course. Confidence intervals and hypothesis testing topics are also examined. The concepts covered in the course are demonstrated using R. Laboratory Course. Prereq: MET CS546 and (MET CS520 or MET CS521), or equivalent knowledge, or instructor's consent.  [ 4 cr. ]

Prereqs: AD100,ADR100,AD571
Enterprises, organizations, and individuals are creating, collecting, and using a massive amount of structured and unstructured data with the goal to convert the information into knowledge, improving the quality and the efficiency of their decision-making process, and better positioning themselves in the highly competitive marketplace. Data mining is the process of finding, extracting, visualizing, and reporting useful information and insights from both small and large datasets with the help of sophisticated data analysis methods. It is part of business analytics, which refers to the process of leveraging different forms of analytical techniques to achieve desired business outcomes through requiring business relevancy, actionable insight, performance management, and value management. The students in this course will study the fundamental principles and techniques of data mining. They will learn how to apply advanced models and software applications for data mining. Finally, students will learn how to examine the overall business process of an organization or a project with the goal to understand (i) the business context where hidden internal and external value is to be identified and captured, and (ii) exactly what the selected data mining method does. R, SQL, and Power BI software are used in this course.   [ 4 cr. ]

Formerly titled CS 555 Data Analysis and Visualization with R.
This course provides an overview of the statistical tools most commonly used to process, analyze, and visualize data. Topics include simple linear regression, multiple regression, logistic regression, analysis of variance, and survival analysis. These topics are explored using the statistical package R, with a focus on understanding how to use and interpret output from this software as well as how to visualize results. In each topic area, the methodology, including underlying assumptions and the mechanics of how it all works along with appropriate interpretation of the results, are discussed. Concepts are presented in context of real world examples. Recommended Prerequisite: MET CS 544 or equivalent knowledge, or instructor's consent.  [ 4 cr. ]