 I'm Lou Gross and I'm the professor at the University of Tennessee, Knoxville, and this is a boring presentation, it's about a report, can't imagine anything more boring than that, but it's a report that directly relates to this effort, it's from the National Academies who were essentially hired by the National Science Foundation to produce a report about undergraduate data science education. And I will start out with a caveat to say that the effort of the people involved, despite an effort to be inclusive, was nowhere near as inclusive as the folks in this room. And so you should take all of our, the recommendations from things like this with a grain of salt because it doesn't have the same set of voices that I have heard here at this conference. So, and by the way, I have a copy of it and it goes to Wendy Graham who I think really deserves a lot of applause. She was not acknowledged at the beginning here but she's basically the underpinnings of a lot that has gone on here in data and environmental issues for decades. So Wendy, it's for you. And real quickly, with regard to the summary of what goes on here, there's an introduction there is a set of basically our perspective of what some of the skills for a data science test would be including a description of what we call data acumen. A set of descriptions of different ways that data science programs might arise. Oh, yeah, I'm still here. And then a whole set of sort of somewhat prescriptive approaches to how someone might start a data science program and then a whole chapter on evaluation. And we argued that evaluation should be part of every data science program from the very beginning. And a few key points. First of all, this is the infancy of data science. There's no fixed rules yet on how to do it. The future will continue to be many different kinds of roles for people in data science. And it really does borrow from a lot of prior disciplines and there needs to be multiple approaches to how people go about developing programs and might be mentored through these programs that will also benefit from coordination from the variety of professional societies that touch on this space. We haven't heard a lot about professional societies at this, but there's many of them that tie into this. At the undergraduate level, a few of the key points is that the field is going to evolve and that means the programs need to evolve. There's a wide variety of pathways for undergraduates as a result of the evolution of these programs and it should cater to and promote diversity, demographic and intellectual in all the students that it serves. There are a whole set of core competencies. We call them data acumen and that evaluation of the programs is critical. I'm going to sort of end here with a description of what we call data acumen because it's not an easy thing to incorporate all of these various concepts and skills and I'll separate concepts and skills here as we did in the report. So there's mathematical foundations, there's computational foundations, there's statistical foundations, there's issues of understanding data management and curation of data, how you describe and visualize data that includes things like metadata structures, data modeling and assessment, whole issues of workflow and I haven't heard the term reproducibility here maybe, it's one of those key issues in modern data science and we also emphasize that it has to be communicated, that communication is key and that's part of the education process for a data science person and there are alternative approaches to think about education that involves direct domain understanding that it may be that data scientists broadly trained in all these background areas is not going to be as useful as someone who has intuition in a particular field, in our case environmental data and understanding why the problems are of interest to environmental scientists and biologists and a whole host of issues associated with ethics associated with this as well. I will point out that there's a website listed up here NAS.edu slash envisioning capital D, capital S, envisioning data science, you will find there a whole set of webinars done in association with these reports and those webinars include one on enhancing diversity in data science that you might find useful, it's by two people who are not here and you might get a somewhat different perspective by looking at that and I will shut up.