So it's an interesting story. Ah, it was a combination of me taking interests and, ah, knowing how a specifically problem self. In addition, there was a little exploration and, you know, with any any story, there's always, like, serendipitous findings and and just learnings along the way. So, uh, not Teoh make the story really long. But when, um, when I was in high school, I was actually a pretty poor Matt student. And the reason was because it just felt like a chore with, you know, just doing problem after problem. And so one of the things I struggled with was why I did what I did. So I thought maybe it was an application person. And so, um, I kind of thought maybe he'd become a doctor, a medical doctor specifically or an engineer. But somehow, as they started taking interesting classes, um, I went back and said, Why does this work? So I ended up getting a degree in math, which was probably the right thing for me to do, which is again and pathetic away, because I just said I was not a good high school mat student, but, um, the more I grew Ah, learning math. The more I realized that it was actually the abstractions within math that I really actually like. And over time a zai majored in it. I ended up realizing that I had now a sufficient amount of knowledge where I could potentially try to do applications again because they seemed interesting. So a little roundabout, like I said, exploratory. So eventually, after I got my Masters degree, I started working for a cyber security company. That's about half time in my life. I'm now ready to do application, understand sufficient amount of So that essentially led me to becoming a data cited Post my peachy and that what really brought me here today. Yeah. Um, Davis's jobs are typically a death for the most part, So let me, let me maybe work backwards here.

this. Um, but, uh, so I'll talk a little bit about what life looked like pre Corona. And then, you know, stay tuned for what happens Post Corona. And so pre Corona is like a saying it's mostly a desk job. There's definitely some amount of travel involved. Um, um, a lot of it will go towards professional growth and a conference travel, depending on where you're employed. And in addition, the work hours from a day. A week to week perspective depends on requirements. As with anything, if you're having fun, you know time flies. One of the requirements that ends up being, ah, part of my job are continuing, continuing learning, so things that you may not have been exposed to. But here's a new problem. So quite a bit of time goes into just reviewing and understanding literature and what's out there as fires, responsibilities and decisions go, um, in my specific Well, one of the things that ends up happening is we work a lot with We work a lot with business partners who, bringing problems to you that they have identified that would require the use of either like machine learning or artificial intelligence tool to solve their overall problems. So in that case, what ends up happening is that we need to work with the business partners, and we need to figure out whether the hair thoughts and rick ornaments of how to solve the problem fit within the scope of what I would be doing it by specific job. And then what we would do is make decisions along with stakeholders and everyone else involved involved in the project to specify instead of solutions and tools that we would be, um, so I will actually say there's two specific I'll give you a general answer. Here is well, because, um, as as people are getting started, especially newer students, there's there's a wide definition of who qualifies.

different answers. In general, there are three big tools that are out there. Um, and I think we know mall are sass and python. Um, typically, financial services, especially on the business side, end up being fairly sas heavy. Um, and then depending on if the company has a more statistics focused, they will use our and and or if they have requirements, that so, for example, Time series is a very common thing. If you have a lot of times, here's work that you do, that there are more packages in R and Python, but again, it's there's no hard rules. It's too what, um, gets which told that you would use for my specific purpose right now. And you know, this is the 3rd 1 and this is the beast. In the newer mission learning age that we have is python and all the and all the and all the libraries within it. And this includes all the deep learning libraries I kid learns for modeling and in In addition, there's also a lot to do with how you wrangle date it, and in over the last couple of years I'd say there's been a big movement towards using, um, auto ML packages like H 20 or data robot that coming that help you do your modeling job a little bit more efficiently? Yeah. So I'd say there are three major challenges that one faces. The 1st 1 would be understanding the problem in sourcing of the data, so typically.