First of all, thank you so much for having me really appreciate being here, being able to share some of my knowledge and expertise. I'll start with the first question. How did I get to where I am today? Uh, my background is in education. I actually started teaching in the classroom in New York City public schools and after I was there for a few years, I wanted to do something different that had more of an impact. Eso I actually went into an educational nonprofit and worked on curating online assessments, understanding how to recruit and train instructors and teachers. And I did that for a few years, and after three years I had started to think about what else I could. Dio and I had never thought about starting a company. That wasn't something that that I was told that was even possible at the time. And this was back in 2000 and 14. It was serendipitous. I happened to know somebody who had moved back into the area, who was a mutual friend of somebody that I knew who was interested in starting a company, and he saw that data science was a really big opportunity because we saw data was going to be even more ubiquitous than it was back then, and not enough people understood how to use it correctly. So we spoke and we decided to start this company together. He brought in his quantitative background as well as his business background, and I brought in my educational expertise and together we started to build the program and we had a couple of trial runs. We figured out what worked and what didn't. And we initially started in a BDC context of business to consumer. We're marketing directly to students. What we found after the first few times that we were in the program was that we got a lot of questions from our students about being able to go in and actually trained their teams. So we started to see a really big opportunity where we could work with companies and with government agencies to build customized data science training programs specific to them and run that internally to organizations. We saw that nobody was really addressing that need and that we had a good system in place to build those programs and to run it with with live instructors. So we shifted our business model and that opened a whole new world of opportunities for us. Since 2017 2018, we've grown quite a lot, and that's largely due to the quality of the training programs that we provide. And we also have opened up a consulting services arm as well because we've seen that our clients are also interested in implementing some of the skills that we teach. So it was a trajectory where the first part of it waas me figuring out what I wanted to do and how it could have a bigger impact, seizing on this opportunity and then noticing the shifts in the market and adjusting accordingly.

those are all great questions because we work strictly with companies and with agencies. It's a variable answer. We train executives in shorter sessions. So those air maybe a few days at the most we train general staff and how to think about data, how to ask the right questions. And those are all non technical programs. The technical programs that we offer we do introduction in sequel and R and Python, but which is the most popular programming languages of data science. And we also teach in Excel tableau Power Bi I those air very heavy in the dead analyst world. And those programs can take anywhere from a few days if it's just introductory or if they want to go all the way up to text mining, neural networks and deep learning that that does take a little bit longer. I'd say most of our programs are usually a month long on that covers some foundations of data science or, if it's more advanced, it gets into a more advanced level

great question. It took us a long time to figure out a process that worked. When we initially started, we relied very heavily on instructors to build their own materials. What we found is that the result lacked a certain level of consistency across all of our programs. Given my instructional design background, we ended up creating a process that was a trial and error with our staff to create and develop all of the content in house that goes through several review cycles. So we have a team of data scientists and a team of instructional designers that work together to build out all of our courses. Usually it's a few rounds of editing and before every course that we run, even if it's a course that we've run before, we review the materials to make sure that the code is updated because, as you may know, encoding in the coding world, things get updated every few months, so it's important to make sure that we're teaching people the most up to date um, coding techniques and making sure that they're not running any errors in their in their code. So we do have a very robust review process that we do for every program that we run? Yeah, Oh, I'll also add, in terms of relevance, a big bonus from our consulting arm is the ability to directly interact with clients and see how they're implementing data science and data analysis. So we're able to take those learnings and see what's most important and then inject that back into our training programs, so all of the programs that we have are from real world examples.