Course Descriptions

This course introduces biomedical and health informatics. It covers the fundamentals of informatics as it applies to healthcare and research, as well as the expanding role of information technology for the delivery of healthcare. The course underscores the application of these systems to the practice of medicine, to enhance health outcomes, improve patient care, and strengthen the clinician-patient relationship. The course also emphasizes the clinical informatics content, which includes fundamentals of clinical and biomedical informatics, clinical decision making and process improvement, health information systems, ethics, and management. 

This course covers the basic concepts surrounding clinical information systems as they apply to healthcare. The focus of the course will be on the socio-technical challenges specific to the selection and implementation of these systems, human factors, evidence-based medicine, information retrieval, and clinical research informatics. Additional topics include a survey of other areas of informatics including telemedicine, imaging informatics, nursing informatics, bioinformatics, public health informatics, and consumer health informatics. 

This course introduces the foundations of computer science for healthcare professionals How to solve problems in the healthcare environment by writing computer programs. How and why computer programs work, with examples in the Python programming language. No prior computer programming experience is expected or required. 

This course focuses on decision support systems that aim to improve healthcare, with an emphasis on clinical decision support (CDS). Through interactive web-based simulators, students will gain familiarity with the following: Common decision support tools used in electronic medical records, decision science and logic as used in CDS, examples of decision tools used at the individual, cohort, and population levels and sharable CDS artifacts using FHIR standards. Common governance, regulatory, legal considerations, and best practices for CDS implementation and evaluation will be discussed. Upon successful completion of this course, students will have the skills to contribute towards designing, implementing, governing, and evaluating decision support systems to improve healthcare. 

This course is a monthly conference in biomedical and health informatics. It will introduce students to experts in the field of biomedical informatics, exposing them to key experimental and theoretical literature in the discipline, and encouraging discussion and further in-depth exploration of topics in clinical informatics. The course emphasizes the application of informatics to the practice of medicine, to enhance health outcomes, improve patient care, and strengthen the clinician-patient relationship. The course expands on fundamental clinical informatics content, including clinical decision making, process improvement, health information systems, ethics, and management. 

Prerequisite: None. 

This course will prepare students for engaging in the Clinical informatics and Data Science practicum. It will provide students with tools and skills in the areas of project planning, literature reviews, data analysis, career planning, oral presentations, and technical writing. Students will select an area of interest in which to apply the knowledge and skills gained during the didactic course in Clinical Informatics and Data Science, work with a mentor, develop a specific set of goals to be accomplished, engage in lectures, and participate in peer reviews. 

This course will give advancing students the ability to demonstrate substantive application of the knowledge and skills that have been acquired, with a focus on performing independent research. This is a semi-independent online course, where students will be assigned a mentor, and implement a research project they designed during DATA 611. Students can be embedded in a Clinical Informatics or Data Science setting within the University of Maryland. As an alternative to embedding students on-site, distance-learning students can work remotely under the supervision of an advisor. 

The goal of this class is to give students an introduction to and hands on experience with all phases of the data science process using real data and modern tools. Topics that will be covered include data formats, loading, and cleaning; data storage in relational and non-relational stores; data governance, data analysis using supervised and unsupervised learning using R and similar tools, and sound evaluation methods; data visualization; and scaling up with cluster computing, MapReduce, Hadoop, and Spark. 

Prerequisite: INFO 603 or demonstrated knowledge of the Python programming language. 

This course provides a broad introduction to the practical side of machine-learning and data analysis. This course examines the end-to-end processing pipeline for extracting and identifying useful features that best represent data, a few of the most important machine algorithms, and evaluating their performance for modeling data. Topics covered include decision trees, logistic regression, linear discriminant analysis, linear and non-linear regression, basic functions, support vector machines, neural networks, Bayesian networks, bias/variance theory, ensemble methods, clustering, evaluation methodologies, and experiment design. 

Prerequisite: DATA 601. 

The goal of this course is to introduce methods, technologies, and computing platforms for performing data analysis at scale. Topics include the theory and techniques for data acquisition, cleansing, aggregation, management of large heterogeneous data collections, processing, information and knowledge extraction. Students are introduced to map-reduce, streaming, and external memory algorithms and their implementations using Hadoop and its eco-system (HBase, Hive, Pig and Spark). Students will gain practical experience in analyzing large existing databases. 

Prerequisite: DATA 601. 

This course introduces students to the data management, storage and manipulation tools common in data science. Students will get an overview of relational database management systems and various NoSQL database technologies, and apply them to real scenarios. Topics include ER and relational data models, storage and concurrency preliminaries, relational databases and SQL queries, NoSQL databases, and Data Governance. 

Prerequisite: DATA 601.