14 courses with the subject MSBD, 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.
MSBD 520Introduction to Biostatistics. . Pre-requisite(s): Elementary Statistics. Principles of3
biostatistics and the analysis of clinical and epidemiological data. Descriptions and derivations of statistical methods as well as demonstrations of these methods using SAS. Topics include basic analysis methods, elementary concepts, statistical models and applications of probability, commonly used sampling distributions, parametric and nonparametric one and two sample tests, confidence intervals, applications of analysis of two- way contingency table data, simple linear regression, and simple analysis of variance. 294
MSBD 550Applied Machine Learning for Biomedical Data Science. . Pre-requisite(s)3
(MSDS 530 or MSBD 530), (MSDS 535 or MSBD 540). Introduction to machine learning with biomedical applications. Survey of machine learning techniques, including traditional statistical methods, resampling techniques, model selection and regularization, tree-based methods, principal components analysis, cluster analysis, artificial neural networks, and deep learning. Students implement machine learning models with open-source software for data science. They explore data and learn from data, finding underlying patterns useful for data reduction, feature analysis, prediction, and classification. MSBD 551 Applied Machine Learning. 3 credit hours. Pre-requisite(s): MSBD 710, MSDS 525, 530.Introduction to machine learning with business applications. Survey of machine learning techniques, including traditional statistical methods, resampling techniques, model selection and regularization, tree-based 295
MSBD 610Network and graph theory for Biomedical data analytics. . Pre-requisite(s)3
Instructor approval. This course will cover the network and graph theory for biomedical data analytics. The representative power of graphs will be used to understand and model networks of biomedical data for various biomedical applications such as protein interaction networks, drug repositioning, genomics, etc. In this course, firstly a brief overview of graph theory will be provided to quantify the structure and interactions of networks, and then various methods and algorithms will be discussed to analyze the biomedical network data. Finally, a range of applications will be studied through real-world biomedical data sets.
MSBD 620Biomedical Signal Processing. . Pre-requisite(s): Instructor approval. This course3
introduces fundamentals of biomedical signal processing along with its applications in wearable sensor devices. The course includes topics on biomedical signal acquisition, techniques on processing the signals captured, including time domain approaches for event detection, time-varying signal processing for understanding the dynamical aspects of complex biomedical systems, and finally the application of machine learning algorithms to build predictive models for early insights on diseases. MSBD 720 Advanced Biostatistics. 3 credit hours. Pre-requisite(s): MSBD 525 or 710, or equivalent. Utilize current statistical techniques to assess and analyze biomedical and public health related data. Read and critique the use of such techniques in published research. Review of linear models, matrix algebra, and multiple analysis of variance. Introduction to random effects models, understanding and computing power for the GLM, GLM assumption diagnostics, transformations, polynomial regression, coding schemes for regression, multicollinearity. Determine what analytical approaches are appropriate under different research scenarios. 296
MSBD 725Advanced Scientific Computing: Stochastic Methods for Data Analysis, Inference and
Optimization. 3 credit hours. Pre-requisite(s): MSBD 535. Study of Monte Carlo methods, a diverse class of algorithms that rely on repeated random sampling to compute the solution to problems whose solution space is too large to explore systematically or whose systemic behavior is too complex to model. Introduction to important principles of Monte Carlo techniques and their power. Bayesian analysis and Markov chain Monte Carlo samplers, slice sampling, multi-grid Monte Carlo, Hamiltonian Monte Carlo, parallel tempering and multi- nested methods, and streaming methods such as particle filters/sequential Monte Carlo. Related topics in stochastic optimization and inference such as genetic algorithms, simulated annealing, probabilistic Gaussian models, and Gaussian processes. Applications to Bayesian inference and machine learning. Python or R for all programming assignments and projects. MSBD 726 Biomedical Imagining, Processing and Analysis. 3 credit hours. Pre-requisite(s): MSBD 725. Study of biomedical imaging and diagnostics concepts and methods, including mathematical treatment of tensor data structures, image processing, and methods of analysis. Typical data sets and studies may include radiology and pathology, e.g. CT, PET, SPECT, MRI, microscopy, ultrasound, and hyperspectral data. Computational studies may be performed in R, Julia, or Python. Upon completion of course, students should be able to apply AI and ML methods (from prior courses) to various biomedical diagnostic imaging.
MSBD 730Advanced Deep Learning. . Pre-requisite(s): Instructor approval. Advanced deep3
learning is used on data systems in many ways. The course introduces students to recent developments and advanced state-of-the-art methods in machine learning using deep learning and presents the mathematical, statistical, and computational challenges of building stable representations for high-dimensional data, such as images, text, and electronic health records. It aims to help students to become familiar with several deep learning methods, and to code them efficiently in Python using the current Pytorch package.
MSBD 735Advanced Epidemiology for Public Health. . Pre-requisite(s): Instructor approval3
Epidemiology is a discipline that is essential for understating and solving public health problems. It is a study of advanced analytical methods, tools, and study designs used to investigate disease transmission, chronic illness, and other public health phenomena. It provides a means of assessing the magnitude of public health problems and the success of interventions designed to control them. This course introduces students to the principles of essential issues in epidemiologic methodology. The focus is on how and why a given method, design, or approach might help us explain population health. The emphasis is on the strengths, limitations, and potential alternatives for a given approach. The origins, use, and potential of both classic and cutting-edge methods will be introduced.
MSBD 736Ethical, Legal and Societal Issues in Healthcare. . Pre-requisite(s): Instructor3
approval. Examination of case studies. Introduction to health care law and ethics, making ethical decisions, contracts, medical records and informed consent, privacy law and HIPAA.
MSBD 740Privacy and Security in Healthcare. . Pre-requisite(s): None. Security issues3
related to the safeguarding of sensitive personal and corporate information against inadvertent disclosure. Policy and societal questions concerning the value of security and privacy regulations, the real-world effects of data breaches on individuals and businesses, and the balancing of interests among individuals, government, and enterprises. Current and proposed laws and regulations that govern information security and privacy. Private sector regulatory efforts and self-help measures. Emerging technologies that may affect security and privacy concerns; and issues related to the development of enterprise data security programs, policies, and procedures that take into account the requirements of all relevant constituencies; e.g., technical, business, and legal.
MSBD 750Directed Reading and Research. Variable hours per semester may be offered (1–3
directed reading and research course provides students an opportunity to delve into a special topic of interest related to biomedical data science selected by the student under the guidance of a faculty member. The student and faculty member meet weekly to discuss the readings; the student will be required to write a comprehensive review paper on the semester’s reading.
MSBD 870Literature Review. Variable hours per semester may be offered (1–3
). The course provides doctoral students with advanced research skills and strategies for conducting a literature review leading to a dissertation. Through this course, students will produce an extensive and integrative literature review related to their dissertation topic. Students will search, retrieve, summarize, and synthesize relevant studies to produce a comprehensive literature review.
MSBD 880Proposal Manuscript and Defense. Variable hours per semester may be offered (1–3
This course provides the student with the opportunity to concisely describe a biomedical data science research problem and methodology. Preparation and defense of the dissertation proposal which clearly articulates the problem to be investigated in the field of biomedical data science, literature review, and what would need to be done to complete the dissertation. Student must successfully defend the proposal before a Dissertation Committee which will determine whether the student proceeds to complete the dissertation. MSBD 890 Dissertation and Defense. 12 credit hours. Pre-requisite(s): MSBD 880 Proposal Manuscript and Defense. Variable hours may be offered. The completion of Ph.D. dissertation is the culmination of the doctoral degree in this graduate program. The research topic of the dissertation must be related to the Ph.D. in Biomedical Data Science Ph.D. program.
