Danny Grant - Kogod MS Analytics Career Outcomes

The following is an computer-generated summary of the video transcript.

Hi. My name is Danny Grant and I graduated from the S Analytics program back in 2016. From a career outcomes perspective, I've been very appreciative of the results that I've gotten out of the program. Even before I graduated the program, my capstone project was with D. C. United, the local MLS soccer club in the D C area, and the work I was able to perform on that project was extremely interesting. We were able to translate statistics from International League players. How would that translate into the MLS and which candidates are actually very high priority candidates, maybe even candidates that are kind of flying under the radar. So we're able to highlight a few candidates that we thought might be interesting. Showcase that to our client, who then was able to take that to his bosses. I believe even one trade was able to be executed using some of our analysis, which was extremely exciting for us. So that has been an experience that has been a stepping stone conversation starter with several interviewers around just what had been my experiences in the past and so that has been a boon to my career. Even before I left the program, I was able to transition out of my current industry at the time, which was payroll and into the consulting world. Uh, I was able to transition over to Deloitte as a consultant there, where I was able to work on some very interesting projects as well. The one that was really the most interesting that I worked on was fuel efficiency for fighter jets were able to model. When would a particular plane likely need to have some maintenance them? And so they can try to get ahead of something actually breaking on the plane, which adds additional costs and downtime and everything like that, so trying to be proactive as opposed to reactive. I was able to implement things like fuzzy matching and a whole bunch of different algorithms that were very interesting to learn and to use on the fly. In addition to that, I was able to work on a project around Text Analytics where we were trying to take medical rule, change comments from the public and be able to categorize them into, You know what kind of what kind of comment is this? Is this more technical? Is this more policy, etcetera? And being able to simplify life for the Centers for Medicare and Medicaid services so that they would be able to more efficiently be able to respond to these comments even beyond the project work? I was able to expand my horizons as well with this first job where I was able to work in the fields of cyber security project management, Uh, and even setting up some devops environment. So very technical type of work, data engineering, type of work. The first drop was a huge success in my eyes in terms of my career, and it was something that really was able to be accomplished through this program. It set me up for success there not only be able to pretty strong technical work, but also be able to translate that to business value, which is one of the shrinks I feel in this program being coming from The co got school of business as opposed to a more arts and sciences type of department. So this is a very strong program for that currently I am working for giving me, which is a start up doing data platform product work where we are trying to showcase. What is the duty spending their money on and how can they spend that money a little bit better? So some of the projects that I've worked on there have included things like trying to forecast training ammunition, uh, so that they can more effectively use the money that they have set aside for that without having to have huge surpluses of inventory on hand or run out of inventory when they really need it? Um, so we have been able to implement things like double exponential smoothing algorithms there that have been very well received. We've also done work in the labour economic field, which is not my field of expertise, but I'm still able to apply several aspects of analytics that I learned through this program and be able to translate that back into business value, which again is, I think is a strong motivator behind this program. Lastly, some of the poorest topics that have been very critical to my success had been things like the technical work, which is including, like what I mentioned double exponential, smoothing and different algorithms of that nature, but several of the projects where we had to collaborate with students and being able to take diverse backgrounds and diverse perspectives, being able to move that forward into a coherent project that is concise and is backed up by technical data analysis. At the same time, it's simplified, Digestible for non technical stakeholders is something that has been huge and helping to kind of move forward in my career.