So I hope I've convinced you of the importance statistics

in genomic big data science.

So, then the next question might be how do you get help in statistics?

I'm a firm advocate that the best way to get help in statistics is to actually go

out and learn a little bit of statistics yourself, and there are a large number of

online resources that are available to you to do this.

There's a statistics class as part of this specialization.

We also offer a gen,

a JHU data science specialization that has a large statistical component as well.

There are also a large number of other online resources and

courses that are available to you if you want to go out and

learn how to do the statistics yourself.

If you know a little bit of statistics and

just need a little bit of help with whatever analysis you're actually working

on at the moment, one way to do that is to go out to Q and A sites.

I'd recommend sites like Cross Validated and

Stack Over the Flow where you can go out and actually post questions, both about

specific packages that you might be interested in, and also about specific

analysis types that you might be, have questions about which model to fit, or

you might have questions about whether the model that you fit makes a lot of sense.

So, the one thing that you might do if you need a little bit more help than even that

is go out and actually get some more expertise, in in wo,

in other words you might find somebody to help you out.

One way to do that is to, if you're sort of the principal investigator of a lab is

to hire a single, lonely bioinformatician.

So a lonely bioinformatician is someone who is hired to do computational biology,

sits in a biology lab, but

isn't supported by other people who do computational science necessarily.

So this is a very hard job, and it's possible to do it, and

there are actually some very excellent lonely bioinformaticians out there, but

it's actually a very hard way to actually perform computational analysis in general

because this person won't necessarily have access to all of the different help and

resources that a person that's in a center for computational biology might have.

So often the best way if you need deep statistical or

computational expertise the best way to get it is to go out and

start a collaboration, a long term collaboration.

And so, the long term collaborations can be formed by formed by identifying

a center for computational biology, or biostatistics where there are a large

number genomic data scientists, computational genomic data

scientists working, they can go out and help you actually perform your analysis.

And so here at Johns Hopkins we have such a center, the Center for

Computational Biology, that brings together people from biology,

from biostatistics, from computer science, and so these folks all sort of work on

computational biology, but dive deep into the problems.

So forming these long term collaborations can really help solve statistical and

computational problems.