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Harvard University - Statistical Inference and Modeling for High-throughput Experiments 

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Statistical Inference and Modeling for High-throughput Experiments
 at 
edX 
Overview

A focus on the techniques commonly used to perform statistical inference on high throughput data.

Duration

15 hours

Start from

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Total fee

Free

Mode of learning

Online

Schedule type

Self paced

Difficulty level

Intermediate

Official Website

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Credential

Certificate

Statistical Inference and Modeling for High-throughput Experiments
 at 
edX 
Highlights

  • Reset deadlines in accordance to your schedule.
  • Instructors -
  • Rafael Irizarry & Michael Love
  • Effort - 2?4 hours per week
  • FREE
  • Add a Verified Certificate for ?11,116
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Statistical Inference and Modeling for High-throughput Experiments
 at 
edX 
Course details

Who should do this course?
  • This course is designed for those who want to learn about Statistical Inference and Modeling
More about this course
  • In this course participants learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis
  • 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
  • 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

Statistical Inference and Modeling for High-throughput Experiments
 at 
edX 
Curriculum

Understand a series of concepts, thought patterns, analysis paradigms, and computational and statistical tools, that together support data science and reproducible research

Fundamentals of reproducible science using case studies that illustrate various practices

Key elements for ensuring data provenance and reproducible experimental design

Statistical methods for reproducible data analysis

Computational tools for reproducible data analysis and version control (Git/GitHub, Emacs/RStudio/Spyder), reproducible data (Data repositories/Dataverse) and reproducible dynamic report generation (Rmarkdown/R Notebook/Jupyter/Pandoc), and workflows

How to develop new methods and tools for reproducible research and reporting

How to write your own reproducible paper

Statistical Inference and Modeling for High-throughput Experiments
 at 
edX 
Entry Requirements

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  • Not mentioned

Statistical Inference and Modeling for High-throughput Experiments
 at 
edX 
Admission Process

    Important Dates

    May 25, 2024
    Course Commencement Date

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    Statistical Inference and Modeling for High-throughput Experiments
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