An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.

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来自 约翰霍普金斯大学 的课程

Statistics for Genomic Data Science

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An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.

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Module 1

This course is structured to hit the key conceptual ideas of normalization, exploratory analysis, linear modeling, testing, and multiple testing that arise over and over in genomic studies.

- Jeff Leek, PhDAssociate Professor, Biostatistics

Bloomberg School of Public Health

Welcome to week one of Statistics for Genomic Data Science.

I just want to give you a quick overview of what we're going to be learning

about this week.

So this week we're going to be talking about two main things.

First, we're going to be talking about background, both about Statistics and

about Genomics, as well as installing all of the relevant software.

For example, the Bioconductor packages and

showing you how to install Bioconductor packages and so forth.

This is basically just sort of our ramp up week in that sense and

the sense that we're going to sort of give you all the background information so

that you can kind of tackle the rest of the weeks of the class.

Then the next thing that we're going to do is we're going to talk a little bit about

background of Statistics.

So we're going to talk a little bit about what are the main and

key concepts in Statistics, and whether who's the target that we're shooting for

in the rest of the class.

And then finally we're going to talk a little bit about exploratory data

analysis this week.

And so we're going to talk about what kind of plots you can make.

What kind of summaries of data that you can make.

And how can you sort of interact with data at a very raw level

that gives you some intuition about what's going on.

I think this is one of the funnest parts of Statistics is

exploratory data analysis.

And so we're going to lead off with one of the best parts