| source Georgetown (X) |
level |
department Biostatistics and Epidemiology (X) |
This course is designed for introductory biostatistical theory and application for students pursuing a master's degree in fields outside of the Department of Biostatistics, Bioinformatics, and Biomathematics. Students first learn the four pillars of exploring and displaying data apporpriately, exploring relationships between two variables, issues of gathering sample data, and understanding randomness and probability. On these pillars, students then can develop the platform for statistical inference including proportions and means, multiple regression, and ANOVA.
Score: 12.184517 Details | Listing | Web page
Credits: 3
Score: 12.184517 Details | Listing | Web page
The goal of the course is to convey an understanding of probability and distribution theory. The probability theory is necessary to provide a foundation for statistics. Probability theory: set theory and probability theory, conditional probability and independence, random variables, distribution functions, density and mass functions for continuous and discrete random variables. Transformation and expectations: distributions of functions of a random variable, expected values, moments and moment generating functions. Common families of distributions: discrete and continuous distributions, exponential family, and location-scale family. Multiple random variables: joint and marginal distributions, conditional distributions and independence, covariance and correlation, multivariate distributions, hierarchical models and mixture distributions. Sampling theory: normal theory, limit theorems.
Score: 12.184517 Details | Listing | Web page
This course will introduce the basics of statistical inference, parameter estimation, and hypothesis testing in preparation for more in depth coverage of specific models in later courses. Inference procedures: point and interval estimation, sufficient statistics, hypothesis testing, methods of constructing test and estimation procedures. Point estimation: criteria for estimators, maximum likelihood estimators, Bayes estimators, mean square error, unbiased estimators, asymptotic variance of estimators. Hypothesis testing: error probabilities, power function, one-sample inference about the mean with known and unknown variance, comparison of two samples, 2Ã2 contingency tables, shortcuts and non-parametric methods. Modeling and study design: missing data, extreme observations, transformations, factorial experiments, probability sampling, sample size, two-stage sampling, stratified sampling, nonsampling errors.
Score: 12.184517 Details | Listing | Web page
Credits: 3
Score: 12.184517 Details | Listing | Web page
Credits: 3
Score: 12.184517 Details | Listing | Web page
This course introduces students to three different open-source programming languages, Perl, Java, and R, as well as popular mathematical or statistical program languages, SAS and Matlab. For each language, we start with some basic introduction to syntax and semantics. We then work through those languages by developing some example applications using existing software libraries that are available for those languages (e.g., Bioperl, Biojava, caBio, and Bioconductor).
Score: 12.184517 Details | Listing | Web page
Credits: 1
Score: 12.184517 Details | Listing | Web page
Credits: 1
Score: 12.184517 Details | Listing | Web page
Credits: 1
Score: 12.184517 Details | Listing | Web page
Bioinformatics is the application of computer science, statistics, and mathematics to the management and analysis of large-scale, complex biological data. This course will enable students to obtain some understanding of the statistical methods needed to analyze such data. During the first weeks of the course, we will provide a basic introduction to database management systems and an overview of important biological databases including GenBank, UniProt, and iProClass. The course will then go on to describe the underlying theories and algorithms for sequence alignment (pairwise, multiple, nucleotides, proteins, statistical evaluation), sequence analysis (correlations, profiles, PAM and BLOSUM matrices), genome comparison (dot matrices), molecular evolution, and gene prediction. For each of these topics, available tools will be introduced during hands-on laboratory sessions.
Score: 12.184517 Details | Listing | Web page
The course will introduce the student to the fundamentals of pattern recognition and its application in extracting biological knowledge from high dimensional and low sample-size data. The course will discuss several supervised and unsupervised algorithms and how they can be applied for various purposes including feature extraction, feature selection, dimensionality reduction, clustering, and classification. Particular emphasis will be given to computational methods such as linear discriminant functions, nearest neighbor rule, weighed voting, artificial neural networks, fuzzy logic, support vector machines, genetic algorithms, and swarm intelligence. The course will present some examples of pattern recognition problems in genomics and proteomics (e.g., DNA base calling, analysis of microarray and mass spectral data, etc.) where pattern recognition methods offer a solution.
Score: 12.184517 Details | Listing | Web page
Credits: 3
Score: 12.184517 Details | Listing | Web page
The objective of the course is to explain in practical terms the basic principles of clinical trials, with particular emphasis on their scientific rationale, organization and planning, and methodology. Issues discussed include design of randomized and non-randomized trials, size of a clinical trial, monitoring of trial progress, and some basic principles of statistical analysis. The intent is to present the methodology of clinical trials with emphasis on the practical aspects.
Score: 12.184517 Details | Listing | Web page
Epidemiology overview and history; distributions of disease by time, place and person; association and causality; ecological studies; cross-sectional studies and surveys; case-control studies; analysis of case-control studies; types of bias in case-control studies; cohort studies; analysis of cohort studies; bias in cohort studies; population attributable risk; confounding factors; effect modification (interaction); analysis for confounding and interaction; multivariate analysis; sensitivity, specificity and screening; public health practice and prevention; special issues in cancer epidemiology, infectious disease epidemiology and genetic epidemiology. This course includes a discussion session.
Score: 12.184517 Details | Listing | Web page
Credits: 3
Score: 12.184517 Details | Listing | Web page
This course offers instruction, discussion and hands-on experience providing statistical consultation in applied scientific situations. These will typically include survival analysis, clinical trial/study design, tumor growth curves, microarray analysis, and proteomics projects. Instruction and experience will focus on consulting strategy. This includes preparing analysis plans and reports, communication and time management skills, and ethics/professional standards. Additionally, students will gain consulting practice to include interactions with investigators with actual projects and problems. Students will attend class and weekly consulting/debriefing sessions, prepare analysis plans and reports, and give oral presentations describing consulting projects over the semester.
Score: 12.184517 Details | Listing | Web page
Students will be involved in a research project under the supervision of a faculty member. While the consulting class will expose them to short-term projects, the practicum will provide them with an opportunity to implement a combination of the skills they have acquired and to extend them in a limited context. This practical experience should span 3-4 months. The project will be written up as a Masterâs paper including the following sections: background to the problem, experimental design, description of the data, analytical methods, results, and interpretation of the latter. This paper will be defended orally, after no fewer than two faculty members (the advisor and one other) have read it and deemed it ready for presentation.
Score: 12.184517 Details | Listing | Web page
This course requires department permission.
Score: 12.184517 Details | Listing | Web page