Statistical Inference and Modeling for High-throughput Experiments

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This course is focus on the techniques commonly used to perform statistical inference on data acquired from High-throughput Experiments.

Last updated on July 22, 2021 2:26 pm

Introduction

This course is focus on the techniques commonly used to perform statistical inference on data acquired from High-throughput Experiments.

About this Statistical course on High-throughput Experiments

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, then you will be introduced statistical to modeling and how it is applied to high-throughput data.

In particular, discussion about parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation. The course provides several examples of how these concepts are applied in next-generation sequencing and microarray data. Finally, you will discuss hierarchical models and empirical Bayes along with some examples of how these are used in practice. The course provides R programming examples in a way that will help make the connection between concepts and implementation.

Given the diversity in educational background of our student’s course 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 you will be taught 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 makeup 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.

What you will learn from the Statistical course on High-throughput Experiments?

  • Organizing high throughput data
  • Multiple comparison problem
  • Family Wide Error Rates
  • False Discovery Rate
  • Error Rate Control procedures
  • Bonferroni Correction
  • q-values
  • Statistical Modeling
  • Hierarchical Models and the basics of Bayesian Statistics
  • Exploratory Data Analysis for High throughput data

Prerequisites

  • PH525.1x and PH525.2x or basic programming, intro to statistics, intro to linear algebra

Syllabus for Statistical course on High-throughput Experiments:

Introduction and Resources

  • Welcome and Frequently Asked Questions
  • Course Materials and R Resources
  • Pre-Course Survey

Week 1. Introduction and Motivation

Week 2. Error rates and proceeds Sures

Week 3. Statistical Models

Week 4

  • Hierarchical Modeling
  • Exploratory Data Analysis
  • Data Analysis for Life Sciences Series

Note: Your review matters 

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

Free Course
Verified Certificate at

$149.00

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  • EDX
  • Harvard University
  • Online Course
  • Self-paced
  • Intermediate
  • 1-4 Weeks
  • Free Course (Affordable Certificate)
  • English
  • Bioinformatics Data Analysis High-throughput data Statistics
Learning Experience
9
PROS: Courses make up two Professional Certificates and are self-paced. R programming examples helps make the connection between concepts and implementation. The course has divided the series into seven parts, you can take the entire series or individual courses that interest you. The course material is great and well structured Official and verified, easily shareable course completion certificate available.
CONS: Found the course difficulty to be medium. More difficult than the previous two courses. Discussion boards should increase transparency in communication.

Description

Introduction

This course is focus on the techniques commonly used to perform statistical inference on data acquired from High-throughput Experiments.

About this Statistical course on High-throughput Experiments

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, then you will be introduced statistical to modeling and how it is applied to high-throughput data.

In particular, discussion about parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation. The course provides several examples of how these concepts are applied in next-generation sequencing and microarray data. Finally, you will discuss hierarchical models and empirical Bayes along with some examples of how these are used in practice. The course provides R programming examples in a way that will help make the connection between concepts and implementation.

Given the diversity in educational background of our student’s course 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 you will be taught 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 makeup 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.

What you will learn from the Statistical course on High-throughput Experiments?

  • Organizing high throughput data
  • Multiple comparison problem
  • Family Wide Error Rates
  • False Discovery Rate
  • Error Rate Control procedures
  • Bonferroni Correction
  • q-values
  • Statistical Modeling
  • Hierarchical Models and the basics of Bayesian Statistics
  • Exploratory Data Analysis for High throughput data

Prerequisites

  • PH525.1x and PH525.2x or basic programming, intro to statistics, intro to linear algebra

Syllabus for Statistical course on High-throughput Experiments:

Introduction and Resources

  • Welcome and Frequently Asked Questions
  • Course Materials and R Resources
  • Pre-Course Survey

Week 1. Introduction and Motivation

Week 2. Error rates and proceeds Sures

Week 3. Statistical Models

Week 4

  • Hierarchical Modeling
  • Exploratory Data Analysis
  • Data Analysis for Life Sciences Series

Note: Your review matters 

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

Specification:

  • EDX
  • Harvard University
  • Online Course
  • Self-paced
  • Intermediate
  • 1-4 Weeks
  • Free Course (Affordable Certificate)
  • English
  • Bioinformatics Data Analysis High-throughput data Statistics

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

$149.00

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