Principles of statistics applied to analysis of biological and health data, evaluation of public health and clinical programs. Gen Ed: R2 (Analytical Reasoning).
The main goal of this course is to prepare students with advanced computing skills for a career as a statistician or data analyst/scientist. By the end of this course, you should be able have mastery over the fundamentals of the R programming language, including concepts such as functional programming and meta programming.
This course provides an introduction to statistical computing with the R programming language. Students will learn how to efficiently manage, analyze, and visualize data using R.
The goal of this course is to prepare students with necessary computing skills for a career as a statistician or data analyst/scientist. By the end of this course, you should be able to use various tools to extract data from different sources(structure or unstructured), and transform them into forms that are ready for analysis and modeling. You will also be able to build web based tools to deliver your data products using R Shiny.
The goal of this 3-credit course is to introduce fundamentals of probability theory, statistical inference tools and their application to biostatistics. The course is intended for first-year graduate students in Biostatistics MS program. The topics in this course include basic concepts of probability, random variables, important probability distributions (e.g., normal, exponential, binomial and Poisson), marginal distribution, conditional distribution, joint distribution, expectation and variance, conditional expectation, law of large numbers, central limit theorem, sampling distributions, point estimation, maximum likelihood estimation, method of moments and estimating equations, interval estimation, hypothesis testing. Examples from biostatistical applications will be used whenever possible. Simple simulations with R software will be used to illustrate some concepts in probability and statistical inference.
This course will provide fundamental statistical concepts and tools relevant to the analysis of high-dimensional genomics data arising from population-based association studies. A first-course in statistics is assumed.
This course is for students who want to learn essential statistical and computational skills for health data science. Students will obtain hands-on experience in implementing a wide range of commonly used statistical methods with real data from public health and biomedical research using the statistical programming language R. The course motivates statistical reasoning and methods through real health data. The focus of the course is to train students in refining a scientific question into a statistical framework, choosing proper regression models, writing scripts and executing them in R, and interpreting scientifically meaningful findings.
Description not available at this time