In this episode of UMBC Mic'd Up podcast, we explore the exciting world of data science and how it can lead to discovering new technologies. Our guest, Daniel Rimdans, M.P.S. '22, Data Science, who was laid off from their job as an environmental scientist during the pandemic, shares how his fascination with data led him to pursue further education. We learn how data collection, analysis, and insight development are integral to primary research across a range of fields.
Our guest also talks about their experience at UMBC and how the university's data science program offers multiple tracks, including spatial analytics and healthcare, tailored to individual career paths. As an environmental scientist, he was particularly interested in the spatial analytics track.
Join us as we delve into the fascinating world of data science and its potential for innovation and discovery. Whether you're a science enthusiast or just curious about the intersection of data and technology, this episode is a must-listen.
Learn about UMBC's Graduate program in Data Science:
https://professionalprograms.umbc.edu/data-science/
In this episode of UMBC Mic'd Up podcast, we explore the exciting world of data science and how it can lead to discovering new technologies. Our guest, Daniel Rimdans, M.P.S. '22, Data Science, who was laid off from their job as an environmental scientist during the pandemic, shares how his fascination with data led him to pursue further education. We learn how data collection, analysis, and insight development are integral to primary research across a range of fields.
Our guest also talks about their experience at UMBC and how the university's data science program offers multiple tracks, including spatial analytics and healthcare, tailored to individual career paths. As an environmental scientist, he was particularly interested in the spatial analytics track.
Join us as we delve into the fascinating world of data science and its potential for innovation and discovery. Whether you're a science enthusiast or just curious about the intersection of data and technology, this episode is a must-listen.
Learn about UMBC's Graduate program in Data Science:
https://professionalprograms.umbc.edu/data-science/
Dennise Cardona 0:00
Hello, and thank you for joining us for this episode of UMBC's Mic'd Up podcast. My name is Dennise Cardona from the Office of Professional Programs at UMBC. We are going to be talking today with a recent graduate of our data science graduate program, Daniel Rimdans. I hope that you enjoy this episode. Thank you so much for joining us today. Daniel. It's wonderful to have you here on the UMBC Mic'd Up podcast.
Daniel Rimdans 0:25
Thank you. It's a privilege happy to be here.
Dennise Cardona 0:28
So you just graduated from data science, the graduate program at UMBC. And data science in December?
Daniel Rimdans 0:36
Yes, I did. This is February, as we're speaking now, I graduated two months ago.
Dennise Cardona 0:41
Oh, how does that feel?
Daniel Rimdans 0:44
It feels amazing. So, so freeing, but also very rewarding.
Dennise Cardona 0:48
Yeah, I would imagine. So what was your path like to get to in the graduate program? Like what made you decide, hey, I think I want to study data science at UMBC at the graduate level, and what what made you decide to do that?
Daniel Rimdans 1:03
Well, first, I had my undergraduate in environmental science, and I had done some research, working with data. And sorry, my cat is trying to make an appearance.
Dennise Cardona 1:16
Oh, I love it.
Daniel Rimdans 1:17
I've done a lot of work with data. And that sort of piqued my my interest as far as moving forward, I realized that data was central to all of my research interests, because I hadn't come across any kind of primary research that didn't involve some type of data collection, analysis and developing insight from that data. Coincidentally, during the pandemic, in 2021, I was laid off from my job as an environmental scientists. At that point, I already knew I wanted to further education. So I'm like, okay, this is a perfect opportunity. I'm just going to take the dive and start researching how to move forward. And I was already in the Baltimore area. UMBC got me hooked, because of the multiple tracks, there's the data science program Central. But then there's several other tracks that you can do spatial analytics, which I was very interested in, because I was environmental scientist, there's healthcare track, there's, for those who are more inclined, computer science, you can vary that way. So yeah, very diverse and tailored to whatever individual needs or career paths that you might want to go into.
Dennise Cardona 2:34
I'd love to talk Tell me a little bit more about spatial analytics. What does that involve? What, for people listening to this podcast or viewing it on YouTube? And they don't know what it is either way, what is it?
Daniel Rimdans 2:46
Well, spatial analytics is analytics. But with spatial data. So pretty much you're going to be working with data that has to do with the geolocators maps, you might be creating maps, turning data into maps, or, you know, as an environmental scientist, so you're doing sampling, or you're looking at climate patterns, these are all things that have to do with location could be weather. So different types of topics, even for tech companies like Lyft, or Uber. You know, they're working with a lot of location, GPS enabled services. And so you might end up in a company like that, working with location data, turning that into maps, or analyzing what kind of directions or paths the most most efficient and quickest way to reach a certain destination, you could go several different ways with it.
Dennise Cardona 3:44
Sure. I think about marketing, because that's what I'm in marketing. And we use geo-targeting, sometimes for some of our campaigns to make sure that we're reaching the right audience. And we're not spending what we shouldn't be spending and things like that. We're trying to attract a certain geographical location. I'm assuming that's all part of spatial analytics. And the data there is very important, very powerful and helping to tell a story, because I really, that's what data is, it's about telling a story, isn't it?
Daniel Rimdans 4:13
Yeah, it is the new gold and it doesn't lie.
Dennise Cardona 4:16
Yeah, it's new gold. I love that. So since you've graduated, what are you doing now? Are you are you working in the data science field at this point in a role? Tell me, tell us about what's going on in your path now?
Daniel Rimdans 4:28
Yes, I am a Predictive Analytics Fellow at the US Food and Drug Administration, FDA. It's been very exciting as I started there as a summer fellow, an internship during my, second leg of my Master's at UMBC and actually got connected to that opportunity through one of the data science UMBC professors, who was a supervisor, my current supervisor currently at the FDA. So, yeah, UMBC as far as having professors, faculty, they're directly connected to opportunities in the industry and government. So super excited to be in government because I can use my technical skills to help people directly the American people. And I'm gonna plug in spatial analytics here, again, because you're looking at medication and pharmaceutical quality, who's being most impacted by what, where, and as you can imagine, medicine, you know, think about the pandemic and your Pfizer, Moderna vaccine shots and Johnson and Johnson, where are they being manufactured? A lot of those vaccines came from outside the country. So there goes your geospatial analytics again, who's making what, where, and how is it affecting people? Yes. So I'm at the FDA long Gasser doing data science work there.
Dennise Cardona 5:59
That's great, what a great opportunity, the fact that you met, that your instructor was somebody who helped you realize the potential out there and get you connected. That's really powerful, those network connections that you make when you're in school. And, you know, obviously, being a great student, great steward of your studies, that those are the kinds of opportunities that come someone's way when they put their selves out there and willing to learn and grow. And you sound like you're a curious person about these things, which is, I'm assuming you really need to be when you're when you're out there, and you're trying to figure out these important points, these important data, what it means to the general public to, like you said, the, you know, American citizens, what does it mean to the world at large? And when you're dealing with things like pharmaceuticals, medical, medicine, life saving medicines, and in the face of pandemics and things like that, that's really powerful. How do you think how did the program prepare you for this role that you're in?
Speaker 2 7:01
The UMBC Data Science master's program prepared me for my career best buy, and I tell this to my friends, so you know, anyone I come across what was your favorite thing about the program? I didn't quite expect it. But what was most important and prepared me best for the field was having professors, majority of the professors were actual professionals in the field. So several of my professors, you know, my current boss, and now was my former professor worked for the FDA. And this is going to be sort of a funny coincidence, my current landlady, who I rent from, is also an adjunct in the UMBC Data Science Program, also working professional data scientist, and is friends with my current boss, because they had worked together at a previous company. So it is, it is eerie, almost the amount of connections that can develop through faculty in the Data Science program, because they're out there working. So they're applying exactly what it is what it is, they're teaching you in class, you can see the field applications you can get opportunities at current organizations that this professors are in and that's the story of my life right now.
Dennise Cardona 8:24
Go UMBC go Retrievers. It's, it's wonderful to have that connection. And you just highlight you highlighted the magic of that right there. And you know, it really is a wonderful thing. When you are learning from people who are out there in the field doing the work. It's not I'm sure that you learn some theory, obviously, that's what part of graduate school is, but it said applied nature, being able to hear how that, how that is applied out in the world. That I think is where learning happens. That's where that the sparks go off. They light bulbs light up to life. And that's how that's how people that's how you transfer that learning out into the real world is is that way so yeah, that's fantastic. When you went into the program, did you have any expectations? And were they met? I know you mentioned one was a surprise being that that the faculty, our industry practitioners. What else were you expecting from the program? And were those met?
Speaker 2 9:26
I would say the I, I expected it to be more than Analytics program. A lot of universities and colleges and even I mean, you go online you can find on analytics courses to pay a couple $100 And, and you can do that. So I thought okay, I already have some experience working with data. I had worked in Excel, you know, conditional statements in Excel and manipulating data. So I expected this is what I'm going to build off of, um, but then, it struck me the difference between data analytics and data science, there's, there's a big difference between the two. And I learned that by seeing how much computer science and research was involved in a data science program, it's not just getting numbers and crunching them and spitting it out, you have the opportunity to, for example, in the capstone project, design your own research endeavor, really a topic that you're interested in, you get to not only make graphs in Excel, for example, but to find a topic that you're interested in completely up to you. Here goes some project management skills also, what kind of data are you going to use? How are you got to design your study to answer a specific question. So you're applying the scientific research method in there, as opposed to you know, just analytics, you want to measure or see a difference, measurable difference gets an insight from numbers that are in front of you. So it's more than it's more than making a graph, it was a lot more technical than I expected. And that's prepared me equipped me, that are further afield than people who say, you know, chose to go do an online course, in data analytics, I have a bit of computer science technique in there because of the several programming languages that I have learned. And then there's application of the scientific research method, as well as data analytics itself, making visualizations and pie charts and grafts. So all of that combined has given me sort of a more boisterous knowledge base and experience base to be able to have an edge ahead of others in the field.
Dennise Cardona 12:05
It sounds so well rounded, really, I mean, you If so, you know, you're not only just get like you said, you get you get, you get the spreadsheet, you get that kind of number crunching technology. But then there's also that management aspect, the analytical aspects, the, all of that all comes together. And it makes you a more well rounded person. And obviously a better candidate for positions out there in the data science field, being able to lead and manage, eventually to to be able to step up to those roles. You're, you're well suited for that now, because of this background. It sounds like
Daniel Rimdans 12:38
Yes, go Retrievers.
Dennise Cardona 12:40
So when you're in your class, you've got peers, you're learning alongside people. What was that? Like? What were your peers? Like? Was it collaborative? Did you do? Did you have to do group projects together? Did you kind of play off of each other to maybe, you know, get some brainstorming underway to be able to understand concepts? What was that like in the classroom?
Daniel Rimdans 13:00
There was not a single class that I took in the UMBC Data Science master's program that didn't involve some level of group work. My first semester less so there was individual assignments and projects, and then opportunity to do group work. But from my second semester forward, I mean, the majority of the assignments and midterms and final exams, were group projects, and then presentations to go along with them. So it was a mixture of challenge. You know, you meet people who are from all of our actually, majority of students in the UMBC, Data Science master's program are from other countries. So there is a lot of diversity. I mean, people who have different backgrounds, a lot of them computer science, majors, computer engineering, you no mathematicians. And so there's the challenge of working with people who have either a wider knowledge base, or they're from a different expertise, you get to be challenged and glean off of them. What do they know that I don't know? How can I learn from them in order to come together with this project in a way that will be not just not just one that can get a good grade, but something that can be presented even outside of the UMBC sphere, some of those projects I have in my portfolio, and I can show to potential employers. Here's a program that I developed with classmates and friends of mine with different people who have different strengths and weaknesses. So you figure that out, learn from them. And you also get the opportunity to impart some of your knowledge or whatever your specific technical skill is. On other people. I might be better at making visualizations another person might be better at, you know, data extraction. repeating that data into a different form. So it's, it's like making a pot of soup, all the ingredients have to come together in order to make something magical because each one leans on the other, you know, you get some flavor from the computer scientists to then talk to the mathematician who then adds his own special spin on it, and you create something beautiful, at the end of that you have a program that you can present, get an A on, and then also showcase in the future as part of your portfolio, you save that as part of your code base, we call it.
Dennise Cardona 15:38
Well, you served that up beautifully. In terms of the analogy, I love the analogy, soup.
Daniel Rimdans 15:44
Come get, come get your pot of soup at UMBC.
Dennise Cardona 15:48
We're gonna make that our new tagline, I love it, I really do it, it really highlights the importance of being able to have that open mindset to be able to work with a variety of different people from different backgrounds, from different spheres. Because that's really where we get uncomfortable when we have to do that, group projects are never comfortable. I've not been comfortable in a group project in my, you know, my experience so far. But then all of a sudden, I grow into that comfort. By the end of the semester, I'm like, I'm so glad that I joined with you with you all to be able to do this, because you it's really good to get uncomfortable, and then figure it out and then grow from learning to be able to be open minded to take other people's perspectives, and then to be able to share yours, to be able to have that voice to be able to have that conversation. And that's the real world isn't a Daniel, I mean, that's when you go out into the real world. We're all part of teams. And that's, you know, we're not all the same person, thank goodness, that would be horribly boring. And we would never get anywhere in the world, if we were all just the same and had the same mentality and the same skill sets and the same strengths and weaknesses, there's beauty to being able to put together a really good pot of soup with different ingredients. Like you said, I love that analogy.
Daniel Rimdans 17:05
And such I would ask such as the nature of the data science program, again, back to the different tracks that I talked about in the beginning, because every indust, industry that you could possibly think of whether it's you know, you mentioned marketing, or I'm in a pharmaceutical quality, somebody else might be doing geospatial data for a tech company or weather data. There's just data being collected a bunch of data, and you need to know, depending on your subject matter area, what to do with it, how to manage it. So there goes the whole diversity of data and data science itself.
Dennise Cardona 17:45
Absolutely. What would you say, was your greatest takeaway from studying at UMBC?
Daniel Rimdans 17:53
My greatest takeaway from getting our masters in data science at UMBC was being being able to be flexible, because data science stems out of you know, maybe a combination of statistics, math, and then computer science. And the tech field of computer science moves so quickly. So it was very encouraging to see professors on their feet, you know, in class, being able to not only teach us from past knowledge, but discover in front of us new technical advancements, you know, a certain program that's been outdated, there's now a new version, there's now a new way of, you know, a new syntax for programming to get a certain output that you want. So it's not a it's not a get all the knowledge know it once and for all type of field, you need to be able to move because technology is advancing very quickly in different ways as we can see around us. So being able to embrace that on learn a certain technique that maybe you worked before, but now it's not the fastest anymore, and people want it faster. People want outputs, the most efficient way. So you need to move. And I learned that flexibility, right for my first semester. At first, it was surprising, you know, to see my professor in class go to a certain, there's sites that you go to StackOverflow to get help with coding or what's what's wrong with my syntax, what's happening. So, I was a bit taken aback to see that my professors were also researching best practices in class, but that's exactly how the field is being able to find the next best thing that works. And become a professional at it and be so good at it that you can then impart that wisdom on others.
Dennise Cardona 19:49
But a wonderful, wonderful close out to a really fantastic conversation. Oh gosh, I've had such a great time listening to you talk about data science and just the application and the importance of it out in the world. Daniel, I always like to end a podcast with a professional development type of element because I a lot of people tune in. And that is what they're after with these, when they were listening to these podcasts is professional development, personal development. So my question to you is, what was the biggest lesson that you've learned so far in life? What would you say that is?
Daniel Rimdans 20:25
From the data science program, specifically, you have or just?
Dennise Cardona 20:28
It could be, or just in general to as a professional working out there in the world? You know, what, what was the biggest lesson that you learned that you can now take out into the world?
Daniel Rimdans 20:36
Wow. You know, I'm going, this just works perfectly, first of all, the metaphor of a pot of soup, and I'm going to refer to Amazon that started out, I don't know, a decade or two ago, don't quote me on that. But Amazon was actually the bookstore, you know, like trying to sell books online. That's what they started out trying to do. And then today, you have, you know, this tech behemoth, who they're now investing in electric vehicles, renewable energy. So it's like, okay, here's this company that started out as sort of a bookstore. And now they're, in their own way, making advances to help prevent climate change. It's quite interesting. There it goes that development, so they figured out how to, you know, sell books cheaper than other companies, and then moved on added on to that other products. And now you have sustainability, renewable energy. And I think that that's what's happened to me, maybe it's like a pandemic, and getting laid off, from my job as an environmental scientist to see that, okay, perhaps I can use this as an opportunity to build on my experience, and the nature of the data science program at UMBC, as such that no matter what background you're from, because there's so much data being harvested from whatever field that you could possibly have an interest in. It is a perfect add on, of technical skill that you can put 'cause I started out doing natural science research as an environmental scientist added on to that data science because I work with data in the field that I'm in, and I'm better prepared and much sharper to be able to go in and say, okay, now I not only have skill as a researcher, a natural, natural, natural science scientist, but also some computer science, science, and statistics and math. There's the analytical and technical know how that comes with that. And I think that I'm going to go forward pursuing life in that way that I can develop an add on, because I need to be flexible, I need to see what skills are in demand. And then I mean, perhaps even you Dennise, working in schooling at the same time, you realize, okay, this is going to make me more marketable, it doesn't mean abandoning your initial passion, which, for me, it was environmental science, you know, how the environment helped people. And I'm in the government and trying pharmaceutical quality and the people can have access, especially underserved communities that are more likely to be impacted by whatever quality issues that happened with draw products, so that I can help with that. I can help people which seems more you know, okay, sociological, but yeah, I'm doing it as a scientist.
Dennise Cardona 23:51
I love it. I love that Daniel. Yeah. So Right. There's so much potential out there in the world. And what a great lesson to learn that we can continue to develop, continue to pursue our passions and not abandon them, but just level them up and develop them and grow them. And to make the world a better place.
Daniel Rimdans 24:10
Acquire more and move on. Acquire more knowledge, you know, an extra certification or an extra degree, whatever it is to, you know, add to your potential and move on.
Dennise Cardona 24:22
I love it. And for anybody who was listening to the podcast, and maybe heard your cat make those cute little sounds, who, what is the name of your cat?
Daniel Rimdans 24:33
His name is Tiger. This is not a video you're wondering what Tiger looks like. He looks like Garfield. That's, you know, he, he eats too much and thinks of the world revolves around him so I apologize on behalf of Tiger and hope that he also spiced up this interview made it a better pot of soup.
Dennise Cardona 24:51
I love I love it Tiger was crawling across the screen a few times they are if you weren't able to see it, but if you could hear him. Oh, you're so adorable. I love wine. I love when fur babies come and join us. So fantastic. It was great having Tiger and it was wonderful having you on here as well, Daniel.
Daniel Rimdans 25:10
Thank you so much, Dennise. I'm happy to sort of give back some feedback and encourage people through my experience in the UMBC Data Science master's program. Do it.
Dennise Cardona 25:21
Thank you so much for listening to this episode of UMBC Mic'd Up podcast. I hope that you enjoyed it. If you'd like to learn more about our offerings, do a quick search for Data Science graduate programs at UMBC or click the link in the show notes.