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.