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  • BIOSTAT 203: Introduction To The Practice Of Biostatistics I

    4.00 Credits
    Duke University

    This course provides an introduction to biology at a level suitable for practicing biostatisticians and directed practice in techniques of statistical collaboration and communication. With an emphasis on the connection between biomedical content and statistical approach, this course helps unify the statistical concepts and applications learned in BIOSTAT 201 and BIOSTAT 202. In addition to didactic sessions on biomedical issues, students are introduced to different areas of biostatistical practice at Duke University Medical Center and gradually participate in the actual research. Biomedical topics are organized around the fundamental mechanisms of disease from both evolutionary and mechanistic perspectives, illustrated using examples from Mendelian disease, infectious disease, cancer and cardiovascular disease. In addition, students learn how to interpret common biomedical assays, including high-throughput data. Core concepts are mastered through individual reading and class discussion of selected biomedical papers, team-based case studies, analysis of authentic research problems encountered by program faculty, and guided participation in actual research projects. Corequisites: BIOSTAT 201, BIOSTAT 202 Credit: 4
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  • BIOSTAT 204: Introduction To Statistical Theory And Methods Ii

    3.00 Credits
    Duke University

    This course provides formal introduction to the basic theory and methods of probability and statistics. It covers topics in statistical inference, including classical and Bayesian methods, and statistical models for discrete, continuous and categorical outcomes. Core concepts are mastered through mathematical exploration, simulations, and linkage with the applied concepts studied in BIOSTAT 205. Prerequisite: BIOSTAT 201 or its equivalent. Corequisites: BIOSTAT 205, BIOSTAT 206 Credit: 3
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  • BIOSTAT 205: Applied Biostatistical Methods Ii

    3.00 Credits
    Duke University

    This course provides an introduction to study design, descriptive statistics, an analysis of statistical models with continuous, dichotomous and survival outcomes, with one or more predictor variables. Topics include mixed effects models, likelihood and Bayesian estimation, generalized linear models (GLM) including binary, multinomial and log-linear models, basic models for survival analysis and regression models for censored survival data, clustered data, and model assessment, validation and prediction. Both parametric and non-parametric techniques are explored. Core concepts are mastered through team-based case study and analysis of authentic research problems encountered by program faculty and demonstrated in practicum experiences in concert with BIOSTAT 206. Computational exercises use the R and SAS packages. Prerequisite: BIOSTAT 202 or its equivalent. Corequisites: BIOSTAT 204, BIOSTAT 206 Credit: 3
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  • BIOSTAT 206: Introduction To The Practice Of Biostatistics Ii

    4.00 Credits
    Duke University

    This course revisits the topics covered in BIOSTAT 203 in more challenging biomedical contexts, with reading and discussion of more complex studies, especially those integrating high-throughput data analysis, and with a continued emphasis on the development of communication skills via written and oral presentations. Additional topics include the creation of effective statistical graphics, and the generation of appropriate documentation for analysis programs. Prerequisite: BIOSTAT 203 Corequisites: BIOSTAT 204, BIOSTAT 205 Credit: 4
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  • BIOSTAT 207: Statistical Methods For Learning And Discovery

    3.00 Credits
    Duke University

    This course surveys a number of techniques for high dimensional data analysis useful for data mining, machine learning and genomic applications, among others. Topics include principal and independent component analysis, multidimensional scaling, tree based classifiers, clustering techniques, support vector machines and networks, and techniques for model validation. Core concepts are mastered through the analysis and interpretation of several actual high dimensional genomics datasets. Prerequisites: BIOSTAT 201 through BIOSTAT 206, or their equivalents. Credit: 3
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  • BME 100L: Modeling Cellular and Molecular Systems

    1.00 Credits
    Duke University

    An introduction to the application of engineering models to study cellular and molecular processes and develop biotechnological applications. Topics covered include chemical equilibrium and kinetics, solution of differential equations, enzyme kinetics, DNA denaturation and rebinding, the polymerase chain reaction (PCR), repressor binding, gene expression, receptor-mediated endocytosis, and gene delivery to tissues and cells. Selected laboratory experiments apply concepts learned in class. Prerequisites: Mathematics 103 and Biology 25L or equivalent; or consent of the instructor. Instructor: Gimm, Tian, Truskey, You, or Yuan
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  • BME 101L: Electrobiology

    1.00 Credits
    Duke University

    The electrophysiology of excitable cells from a quantitative perspective. Topics include the ionic basis of action potentials, the Hodgkin-Huxley model, impulse propagation, source-field relationships, and an introduction to functional electrical stimulation. Prerequisites: Biomedical Engineering 153L, and Mathematics; 107 or consent of the instructor. Instructor: Barr, Bursac, Grill, Henriquez, or Neu
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  • BME 120: Introduction to Business in Technology-Based Companies

    1.00 Credits
    Duke University

    This course covers fundamental business concepts and how they affect technology and engineering functions in a company. Students will learn to look at business problems from multiple dimensions, integrating technical issues with marketing, finance, management and intellectual property. Teams consisting of students from the Pratt School of Engineering and Trinity College of Arts and Sciences (Markets and Management Studies program) will work together to develop and present a business plan for a technical product concept. Students will learn the elements of a business plan and how to pitch a technology-based product concept. Topics covered include marketing of technical products, competitive strategy, market research, financial statements and projections, capital budgeting, venture capital, intellectual property, patent searching, regulatory affairs, and reimbursement. Requirements: Junior or Senior standing and permission of instructor. One course. Instructor: Boyd
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  • BME 153L: Biomedical Electronic Measurements I

    1.00 Credits
    Duke University

    Basic principles of electronic instrumentation with biomedical examples. Concepts of analog signal processing, filters, input and output impedances are emphasized. Students are exposed to system design concepts such as amplifier design and various transducers. Laboratories reinforce basic concepts and offer the student design opportunities in groups. Prerequisite: Physics 62L; or consent of instructor. Instructor: Grill, Izatt, Malkin, K. Nightingale, or von Ramm
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  • BME 154L: Biomedical Electronic Measurements II

    1.00 Credits
    Duke University

    Further study of the basic principles of biomedical electronics with emphasis on transducers, instruments, micro-controller and PC based systems for data acquisition and processing. Laboratories focus on measurements and circuit design emphasizing design criteria appropriate for biomedical instrumentation. Prerequisite: Electrical and Computer Engineering 51L or Biomedical Engineering 153L and Biomedical Engineering 171 or Electrical and Computer Engineering 54L; or the consent of the instructor. Instructor: Malkin, Trahey, Wax, or Wolf
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