PITT Classifieds>PITT Online Courses>Statistical Inference and Modeling for High-throughput Experiments

Statistical Inference and Modeling for High-throughput Experiments

About this Course

In this course you’ll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation. We provide several examples of how these concepts are applied in next generation sequencing and microarray data. Finally, we will discuss hierarchical models and empirical bayes along with some examples of how these are used in practice. We provide R programming examples in a way that will help make the connection between concepts and implementation. Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts. These courses make up two Professional Certificates and are self-paced: Data Analysis for Life Sciences: PH525.1x: Statistics and R for the Life Sciences PH525.2x: Introduction to Linear Models and Matrix Algebra PH525.3x: Statistical Inference and Modeling for High-throughput Experiments PH525.4x: High-Dimensional Data Analysis Genomics Data Analysis: PH525.5x: Introduction to Bioconductor PH525.6x: Case Studies in Functional Genomics PH525.7x: Advanced Bioconductor This class was supported in part by NIH grant R25GM114818.

Created by: Harvard University

Level: Intermediate


Related Online Courses

Decisions made by humans are rarely made by data alone. Human decision-makers have cognitive biases, are affected by emotions, and make conceptual leaps beyond what the data may suggest. The best... more
La ciencia de los datos se encarga de la extracción, preparación, análisis y presentación visual de datos. Existen diferentes lenguajes de programación que otorgan posibilidades para realizar proy... more
Do big data and UX speak to you? This MOOC will give you the methods and tools to analyze the whole spectrum of data we handle in UX, from qualitative user research and quantitative user testing... more
Statistical inference and modeling are indispensable for analyzing data affected by chance, and thus essential for data scientists. In this course, you will learn these key concepts through a... more
Cuando se trata de herramientas para el análisis de datos, siempre tenemos las siguientes preguntas: ¿Cuál es la diferencia entre tantas herramientas que existen?¿Cuál es la mejor?¿Cuál deberi... more

CONTINUE SEARCH

FOLLOW COLLEGE PARENT CENTRAL