MEDB 5501 – Biostatistics I
This course is the first course in the Biostatistics sequence and is intended for students, physicians, and researchers in the biological, clinical and medical fields. It will introduce statistical concepts, analysis methods, and research designs for applied data commonly encountered in the biological, clinical and medical research. Topics include an introduction to the use of SPSS, types of data, descriptive statistics, illustrative statistics, principles in sampling, hypothesis testing, parametric analysis concepts and techniques, nonparametric analysis concepts and techniques, correlation, and linear regression. Familiarity with basic statistics is not required. Statistical analyses involved in this course will be performed primarily using the SPSS statistical analysis package. Students can opt to use SAS for analysis however SAS will not be taught in this course. The student using SAS must have a working knowledge of SAS. The course will also cover the presentation of analytical results and graphic representation of data. (3 credit hour course) Prerequisites: An advanced math course (i.e., Calculus, statistics).
MEDB 5502 – Biostatistics II
This course is the second course in the Biostatistics sequence and is intended for students, physicians, and researchers in the biological, clinical and medical fields, and medical education. This course introduces statistical concepts, analysis methods, and research designs for applied data commonly encountered in the biological, clinical and medical research. Topics include diagnostic testing, hypothesis testing, power analysis, analysis of variance, analysis of covariance, multivariate analysis of variance, propensity scoring, simple and multiple regression, logistic regression, and survival analysis, propensity score concepts and evidence based medicine. Familiarity with the basic statistics and statistical techniques presented in Biostatistics I is required. Statistical analyses involved in this course will be performed primarily using the SPSS statistical analysis package. The course will also cover the interpretation, presentation and the write up of analytical results and graphs. Students can opt to use SAS for analysis however SAS will not be taught in this course. The student using SAS must have a working knowledge of SAS. The course will also cover the presentation of analytical results and graphic representation of data. (3 credit hour course) Prerequisites: MEDB 5501.
MEDB 5503 – Biostatistics III- Mixed Model
This course is the third in the Biostatistics sequence and is intended for students, physicians and researchers in the clinical, biological, and medical fields. The course builds on foundations of linear regression model and logistic regression model. It will cover hierarchical linear mixed model for normally distributed outcomes, and generalized linear mixed models for binary outcome, count data or liker scale outcome. The course offers step by step understanding of how to construct a model with random effect. Specifically, we cover model techniques to address data resulting from cluster randomized study, repeated measures and longitudinal study. Students will learn about these different mixed models and use SAS and R to perform the analyses. The course will also cover the presentation of analytical results and graphic representation of data. (3 credit hour course) Prerequisites: Biostatistics I, II, and intro to SAS and R.
MEDB 5506 – Introduction to SPSS
A working knowledge of statistical software is a vital skill for anyone involved in quantitative research. This class will introduce data management, simple descriptive statistics, and basic graphical display using the SPSS software package. Students will develop the fundamental skills needed to prepare datasets for analysis using SPSS. Students will conduct simple descriptive and graphic analyses and report those analyses in an appropriate and accepted manner. The class will introduce basic methods for data import, data management, simple graphics, and basic statistical analysis. This class will not cover advanced statistical methods. (1 credit hour course)
MEDB 5507 – Introduction to SAS
Course provides a working familiarity with SAS. Students are not expected to have advanced programming or statistical analysis skills, other than the ability to create and modify text files. Basic methods for data import, data management, simple graphics, and basic statistical analysis are introduced. This class will not cover advanced statistical methods, but will provide the student with a firm foundation to address these areas in advanced statistics classes or in the student's research efforts, including thesis/dissertation research. A basic understanding of statistical terminology and a working familiarity with computer-based data files (e.g., Excel) is necessary. (1 credit hour course)
MEDB 5508 – Introduction to SQL
This class is intended for researchers who need to access data stored in a relational database. It will not cover information about how to create and manage a database but rather how to use existing databases (ex: HealthFacts, EHR). You will learn how to extract data, filter, compute aggregate statistics, and join information from two different tables. The class will be taught entirely online. (2 credit hour course)
MEDB 5510 – Clinical Research Methodology
Introduction to clinical research methodologies, conducts, and applications. Course provides overview of use of clinical epidemiology and bionformatics in health care. (3 credit hour course)
MEDB 5511 – Principles & Applications of Epidemiology
This 3 credit-hour course will address the essential questions of “Who gets sick? What causes disease? And why do some groups get sick, but not others?” The course includes an overview of foundations of epidemiologic terminology and methods using examples drawn from current and historical literature. Lectures will introduce measures of disease frequency and occurrence in human populations, epidemiologic study designs and validity, concepts of causal inference, mitigation of non-causal associations, and concepts of interaction. Focused lectures will demonstrate the application of these concepts in select health and disease conditions. (3 credit hour course)
MEDB 5512 – Clinical Trials
This course is intended for students, physicians, and researchers who work in biomedical/clinical research and wish to understand the design, logistics, interpretation, and implications of clinical trials. The course requires no specific prerequisites, though this course will apply theory from biostatistics, bioethics, research methodology, and responsible conduct of research, research design/methods, and more. Clinical trial topics included in the course are ethical considerations, trial design and phases, logistical considerations when conducting a trial, and interpretation of all aspects of a trial (including its results). The course seeks to immerse participants in the most up to date terminology and reporting guidelines and encourages active application of course material by reading high-impact and recent clinical trial publications. Students will be presented material such that they can read about (and possibly conduct) clinical trials in the future and decide for themselves whether trial design decisions and interpretations are scientifically valid. (3 credit hour course)
MEDB 5513 – Overview of Health Services Research
Health services research (HSR) is a multidisciplinary field of investigation that examines how social factors, financing systems, organizational structures and processes, health technologies, and personal behaviors affect access to health care, the quality and cost of health care and ultimately population health and well-being. HSR includes study of individuals, families, organizations, institutions, community and populations and informs healthcare policymaking, improvements in clinical care, and the delivery of health care. In simple terms, HSR is the science that asks the following questions: What works? For whom? At what cost? And, under what circumstances? This course will provide an overview of the US health care system and the delivery of health care in the US, covering the structure, financing, and organization of the US health care system and how health care delivery and health outcomes are evaluated and monitored. Course information will integrate essential information on the structure of the US health care system with detailed evaluation of published research and discussion of the methodologic and research challenges of HSR. (3 credit hour course)
MEDB 5520 – Intro-Medical Informatics
Introduction to concepts of medical informatics and overview of the use of computers and information technologies in health care. (3 credit hour course)
MEDB 5561 – Responsible Conduct of Research
Ethical standards and responsible practices are required for successful scientific research. At many steps in the research process, there may be ethical issues that need to be resolved in a thoughtful, responsible manner. This 3 credit-hour course covers the landscape of scientific integrity– both the principles and day-to-day practicalities of research ethics. Education in research ethics is integral to the preparation of future scientists. Course materials will be balanced among theory, history, current events, and applications to cases, with topic areas to include intellectual property and conflict of interest, human subjects research, use of lab animals, authorship, mentoring, peer-review, etc. *Online participation requirements, too. (3 credit hour course)
MEDB 5573 – Statistical Consulting Practicum
This course is designed to provide students with an opportunity for statistical consulting training. Students will work on real consulting projects that were received through the Research and Statistical Consult Service. Projects may involve sample size calculation, study design, data analysis, generating statistical reports and manuscripts. Student will be able to apply their statistical knowledge and communication skills while learning how to work with other researchers. (2-4 credit hour course)
COMP-SCI 470 – Introduction to Database Management Systems
Database architecture, Data independence, Schema, E-R and Relational Database modeling, Relational algebra and calculus, SQL, File organizations, Relational database design, Physical database organization, Query processing and optimization, Transaction structure and execution, Concurrency control mechanism, Database recovery, database security. (3 credit hour course) Prerequisites: COMP-SCI 303 (or COMP-SCI 352). Co-requisites: COMP-SCI 431.
COMP-SCI 371 – Database Design, Implementation and Validation
This course discusses in detail all aspects of ORACLE database management systems. It covers in detail database design, implementation, and validation using ORACLE. In addition to these, it briefly covers ORACLE implementation, tuning, and implementation. The course is suitable for undergraduates and professionals alike. (3 credit hour course) Prerequisites: COMP-SCI 303 (or COMP-SCI 352).
COMP-SCI 5565: Introduction to Statistical Learning
Introduction to Machine Learning; Multivariate Distributions; Information Theory; Linear Algebra (Eigenanalysis); Supervised/Unsupervised Learning, Classification/Regression; Linear/Non-linear Learning; Introduction to Bayesian Learning (Bayes rule, Prior, Posterior, Maximum Likelihood); Parametric/Non-parametric Estimation. Recommended preparation: MATH 300; Familiarity with MATLAB. (3 credit hour course) Prerequisites: COMP-SCI 394R
COMP-SCI 5566 – Introduction to Bioinformatics
This course introduces students to the field of Bioinformatics with a focus on understanding the motivation and computer science behind existing Bioinformatic resources, as well as learning the skills to design and implement new ideas. (3 credit hour course) Prerequisites: COMP-SCI 303, a course or background in Biology (Genomics or Meta Models preferred).
COMP-SCI 5567 – Machine Learning in Bioinformatics
This course introduces students to the field of Machine Learning algorithms that are used in Bioinformatics, illustrated by several examples of applications to various problems. (3 credit hour course) Prerequisites: COMP-SCI 303, a course or background in Biology (Genomics or Meta Models preferred).
COMP-SCI 5590/ECE 5590: Supervised Learning, Machine Learning
This course covers the applications of machine learning techniques to the modern biomedical and biometric problems. In order to do so, the course will be presented in two parts. For the first part, a quick coverage of the most important pattern recognition and machine learning topics will be given. The second part will consist of select case studies and will be project-based. Students will be presented with a number of biomedical and biometric research problems and will be guided to find solutions to the assignments using the knowledge and the skills garnered during the first part of the class, as well as additional reading assignments. (3 credit hour course) Prerequisites: Either an intermediate knowledge of multivariate calculus, probability, and linear algebra with instructor’s consent, or 5590CI. Basic MATLAB programming skills are required.
LS-CBB 5530 – Cell and Molecular Biology I
Molecular aspects of gene structure and function in prokaryotic and enkaryotic organisms and their viruses. Emphasis in genome structure and organization and regulation of gene expression. (3 credit hour course) Co-requisites: LS-MBB 5561.
LS-CBB 5520 – Cell and Molecular Biology II
A presentation of the cellular and subcellular organization and function of eukaryotic cells. Discussions will emphasize basic concepts by which structure and functions are integrated. (3 credit hour course) Co-requisites: LS-MBB 5561, LS-MBB 5562.
LS-MBB 5561 – General Biochemistry I
The first semester of a two-semester sequence in general biochemistry. This course will emphasize the structure of biological molecules, thermodynamics and kinetics of biological reactions, and selected aspects of energy metabolism and metabolic pathways. (3 credit hour course) Prerequisites: CHEM 322R.
LS-MBB 5562 – General Biochemistry II
The second semester of a two-semester sequence in general biochemistry. This course will emphasize selected aspects of the biochemistry of metabolism and macromolecular assemblies. The molecular basis of genetic and metabolic regulation will be discussed. (3 credit hour course) Prerequisites: LS-MBB 5561.
BIOLOGY 5519 – Principles of Evolution
Synthesis of the modern concepts of evolution. Discussion of the biological processes that produce organic diversity through phyletic change. Discussed are variation, mutation, population genetics, natural selection and adaptation. (3 credit hour course) Prerequisites: BIOLOGY 206.
BIOLOGY 5525 – Bioinformatics and Data Analysis
Methods and procedures for the storage, retrieval and analysis of information in biomolecular and biological databases. Emphasis will be given to the use of database information in biological research and to recent developments in genomics and proteomics. (3 credit hour course) Prerequisites: LS-BIOC 341, LS-BIOC 360.