Statistical Inference and Modeling for High-throughput Experiments offered by Harvard University
- Private University
3 Campuses- Estd. 1636
Statistical Inference and Modeling for High-throughput Experiments at Harvard University Overview
Statistical Inference and Modeling for High-throughput Experiments
at Harvard University
A focus on the techniques commonly used to perform statistical inference on high throughput data.
Duration | 4 weeks |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Course Level | UG Certificate |
Statistical Inference and Modeling for High-throughput Experiments at Harvard University Highlights
Statistical Inference and Modeling for High-throughput Experiments
at Harvard University
- Earn a certificate of completion
Statistical Inference and Modeling for High-throughput Experiments at Harvard University Course details
Statistical Inference and Modeling for High-throughput Experiments
at Harvard University
Skills you will learn
What are the course deliverables?
- Organizing high throughput data
- Multiple comparison problem
- Family Wide Error Rates
- False Discovery Rate
- Error Rate Control procedures
- Bonferroni Correction
More about this course
- In this course, you'll learn various statistics topics including multiple testing problems, 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.
Statistical Inference and Modeling for High-throughput Experiments at Harvard University Curriculum
Statistical Inference and Modeling for High-throughput Experiments
at Harvard University
Data Science, Bioinformatics, Biostatistics, Data Analysis, R, Statistics
Statistical Inference and Modeling for High-throughput Experiments at Harvard University Faculty details
Statistical Inference and Modeling for High-throughput Experiments
at Harvard University
Rafael Irizarry
Designation : Professor of Biostatistics, T.H. Chan School of Public Health
Michael Love
Designation : Assistant Professor, Departments of Biostatistics and Genetics, UNC Gillings School of Global Public Health
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Statistical Inference and Modeling for High-throughput Experiments at Harvard University Contact Information
Statistical Inference and Modeling for High-throughput Experiments
at Harvard University
Address
1350 Massachusetts Ave, Cambridge, Massachusetts 02138, USA
Cambridge ( Massachusetts)
Phone
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