Statistical Inference and Modeling for High-throughput Experiments

You'll learn about multiple testing problems, error rates, error rate controlling strategies, false discovery rates, q-values, and exploratory data analysis in this course. The course then moves on to statistical modeling and how it is used to analyze high-throughput data. This course will specifically cover parametric distributions, such as binomial, exponential, and gamma, as well as maximum likelihood estimation. This course also includes various examples of how these concepts are applied to data from next-generation sequencing and microarray experiments. Finally, you and your professors will examine hierarchical models and empirical bayes, as well as some real-world applications. This course uses R programming examples to assist students understand the principles and how to put them into practice. This is one of the best online Statistical Inference courses.

Because of the wide range of educational backgrounds among students, this course has been separated into seven parts. You can enroll in the complete series or pick and choose whatever classes you want to study. If you're a statistician, you might want to skip the first two or three courses; similarly, biologists might want to skip parts of the introductory biology sessions. Throughout the first three courses, the statistics and programming portions of the lesson become increasingly tough. Advanced statistical concepts, such as hierarchical models, will be taught in the third course, while advanced software engineering abilities, such as parallel computing and reproducible research concepts, will be taught in the fourth.

At a glance

• Institution: HarvardX
• Subject: Data Analysis & Statistics
• Level: Intermediate
• Prerequisites: PH525.1x and PH525.2x or basic programming, intro to statistics, intro to linear algebra
• Language: English
• Video Transcript: English
• Associated programs: Professional Certificate in Data Analysis for Life Sciences

What you will learn

• 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

Instructors: Rafael Irizarry, Michael Love

Offered by: Harvard University

Enroll here: https://www.edx.org/course/statistical-inference-and-modeling-for-high-throug