Frustration is a Good Thing in Learning

At first sight of the the word frustration, most of us might not relate it to something positive. But Chuck Rainville '21, PBC, UMBC Data Science Graduate Program, feels it was instrumental in his learning.

"The program teaches you that to be frustrated is a good thing. Oftentimes, you are right at the threshold of making a discovery. So, if you're feeling that, and I felt it a million times with every homework assignment, that's a good thing. Frustration is an exercise in growth."

Tune in to learn about the many insights Chuck learned as a student in the PBC Data Science program at UMBC.

About UMBC's Data Science Graduate Programs
The Data Science graduate program at UMBC prepares students to respond to the growing demand for professionals with data science knowledge, skills, and abilities. Our program brings together faculty from a wide range of fields who have a deep understanding of the real-world applications of data analytics. UMBC’s Data Science programs prepare students to excel in data science roles through hands-on experience, rigorous academics, and access to a robust network of knowledgeable industry professionals. 

These programs were designed with working professionals in mind and offer courses in the evening and online to accommodate students with full-time jobs. With two campuses in Baltimore and Rockville, students can choose the location that best suits their needs. UMBC offers a 10-course Data Science Master’s program (M.P.S. in Data Science) as well as a 4-course post-baccalaureate certificate in Data Science.

Transcript

Dennise Cardona  0:00  
Welcome to this episode of UMBC's Mic'd up Podcast. My name is Dennise Cardona from the Office of Professional Programs. We are joined today by recent graduate of our data science post baccalaureate certificate program, Chuck Rainville. We hope you enjoy this episode. So Chuck, thank you so much for being here with us on the UMBC Mic'd Up podcast. It's really wonderful to be able to speak with alumni of our programs to see where they're at, and the outcomes that have been gained as a result of studying here at UMBC. Can you tell us what your current role is what you're doing?

Chuck Rainville  0:36  
I work for AARP. And I'm a senior research advisor, which is basically a technician, I have to be able to do the research process from the conception of the question to the back end communication of the question with all of the analytical bits taken care of in between. And so one of the things I got from the program was really having it reinforced that research is question driven. Sometimes we're just asked to give analysis. And we do it. But we don't know what question we're answering. And we don't can't frame it as a question. And we can't put it in any sort of digestible form. So it really helped me to think in terms of being more than a technician, it does the ABC thing right now, it's the beginning, middle end. And the technical parts, really just the middle, really just the B. So the data science really helped with that, because it made me have to think a little bit more about business need, who's going to consume this, they don't want to see impressive analytics, they know that the analytics are impressive, and that they are, but they but they want to see it distilled into something that's like I get it like, and that's really what I got, how to give people  insights, that kind of paint on between the eye, 

Dennise Cardona  2:00  
We talked a little bit before this an email that I'm also a non traditional student of UMBC of the Learning and Performance technology graduate program, and two semesters in and I have to say one of the greatest benefits of being a graduate student. And maybe it's specifically at this age, I'm not sure I'm in my 50s. But what it's teaching me Is that what you just what you just referenced that critical thinking aspect that you bring to the organization at this point, because that is sometimes the missing element, the missing piece that leaders that you're reporting to want that piece, they want you to be thinking critically about this data that you're analyzing and putting together and making sense of it for them. Getting down to the nitty gritty of it, and then explaining it in such a way being able to critically think about it and why it's important for them to know certain aspects of it. So I think that is I agree that is one of the big benefits of being in a graduate program.

Chuck Rainville  2:59  
When I got out of school initially, sometimes the question from bosses would be, is this number, right? How did you get to this number? I found over time that people don't ask that anymore. They assume that the numbers right, they assume that you arrived at it by the customs and norms of your own training. And they asked what does this number mean? Like, I know when big data came out, and I've seen like, he sees tons and tons of observations, drill down until like three or four critical points. For me, it was like how did that happen? And so, you know, I naturally had to find out.

Dennise Cardona  3:34  
That curiosity is for being a successful business person, as a graduate student, a successful human being just having a personality, or just

Chuck Rainville  3:43  
a lifelong learner. Like, I can't, I can't stand not to know things we consumed, like we sometimes purchase, you know, analytical products, and it's a liability to not understand exactly what it is, you're getting. It's an information to symmetry, like if the vendor is able to say, Oh, this is this will tell you exactly what you need to know. But you don't know exactly what question you're asking and stuff. So so I just kind of had to, to be a wise consumer of it of analysis. I had to I had to update what was out there in the market. My main training was probably about 20 years ago, I wrapped up my PhD then. And it was all the stuff that was available 20 years ago, when the internet, when there just wasn't the availability of that much you couldn't get the volume of data and you couldn't get it streaming, you couldn't get streaming data at all, there was no streaming sources. So that that had to be updated. Text Analytics, there was no text analytics. There were some some qualitative software's that had to be updated. Since that, since that's where it's going. I kind of had to go with it. I'll be working for another 15 to 20 years. So I'm not trying to work without those skills.

Dennise Cardona  4:53  
Right, exactly. What was your your major?

Chuck Rainville  4:56  
I graduated from American University's School of Public Affairs So I have a background in like micro economic theory, quantitative methods and models on how prosecutors get reelected. So it was splitting the difference between criminal justice and like micro economic theory and governance. But all I really wanted out of it was to figure out how to zip around in statistical software. And back then there was just a hand. I mean, it wasn't a handful of things, it was still the same test. Some of these applications are from the 30s. But it was the all data sets that I work with were flat files, there's no streaming datasets, there was no, the text, the capability of text was really limited. They were all laid out. Not a lot of longitudinal data sets, they were kind of laid out like sequel tables, but they didn't go anywhere. There's no you couldn't put links in that you couldn't do certain things. The idea of a flat file and it's almost I mean, I still work with flat files, but it's it's a little, there's so much more now. Yeah.

Dennise Cardona  5:59  
Now here UMBC, what did you study?

Chuck Rainville  6:02  
at UMBC, I studied the post baccalaureate data science program. So there's a four, four core sequence and eight core sequence. My big regret is my work wouldn't let me get away from my desk long enough to do the eight course sequence. I wanted to, I felt very welcomed in the program, everybody had different backgrounds, for the most part, you know, some more software development, some things that are just alien to me, but it's terrific. I mean, everybody, everybody's very conscientious about learning the methods are toggled between enough theory to to ground everything, enough code to kind of become mechanically excellent. And then enough, like, okay, let's unpack this. What do we have here? Like, what does this tell us? Again, that sort of ABC thing was terrific, exactly what I needed, you fall into these ruts where you just you just do your methods? You don't ask any questions, you just like, well, sounds like the boss needs me to do this. But what's their questions I got really bad, I'm too embarrassed to ask, the program was very good about that. Like, you can't just start hitting the buttons.

Dennise Cardona  7:02  
It's set you up for what the peer engagement, that peer engagement factor is so critical when you're when you're trying to learn something new, and to be able to work with each other and work on each other's strengths, build up the weaknesses, so that you're all in a in a level playing field in a way and learning from each other and sharing ideas and insights as you as you grasp them. Yeah. So how could you talk a little bit about that experience of collaboration within the classroom, and how that helped to how that benefited you, as a graduate student in the program,

Chuck Rainville  7:31  
the one thing I would have would have never would have been imagined would have been a benefit of collaboration was to my background is in survey methods. So it's basically you end up with a fixed number of responses from a sample of people who are supposed to represent the general population in some way with or without some sort of weighting factor. But for the most part, public opinion measures, measures of like self reported health, all of those type of things. That was my world. And I thought that was the world, hearing what other people were trying to do hearing what other people measured on a day to day basis, there was no way I was going to come across that again, like if I was going to take classes in just statistical methods, it would all be those type of files that I was already used to. So I kind of stumbled upon a whole bunch of alternative sources of data, ideas about well, what's worth measuring? What has a business use, and kind of realizing that I need to peek over the fence a little bit at my own organization figure out? Oh, there's a there's a group over there that all they read is the content of like scraped websites? Well, I've, I've heard a little bit about that. So I you know, I go, I peek over the fence, they let me in because I have a background now, right? It's not like I'm just an interloper, who, who does survey research. So now that I can speak their language, there's these collaborations that are happening in my workplace, because of these collaborations that happened in the program. We've actually started something called a community of practice, where it is basically your big data, people who are now talking to their survey people who are now talking to your social media scrape people who are now because there's been enough curiosity and like lateral drifting into like, people failing to hold their lanes that has had caused a kind of virtue virtuous accident, in the sense that we're all kind of the silos have come down just because of that, because we've figured there's enough people who have figured out what the common languages between these different sets of text and techniques. And I wouldn't have, I wouldn't have been curious, I wouldn't have even bothered. If I hadn't heard Oh, wow, like this is actually, this could actually be appended to survey data, or this might actually be useful to you. It wouldn't have occurred to me because I thought survey research was it.

Dennise Cardona  9:48  
Wow. So you're talking about almost a whole shift in the landscape of how data is processed across mediums and across, across organizations. And so now you have more of those I love that whole flattening and like that non silos. Yes. Whenever we work in silos, we end up retaining too much information on our own and not sharing it with others that might have might catch a glimpse of something that could totally change the output of something else. And so it's such a great, yeah, great opportunity to for an organization to be able to really get to the next level when everybody is working in the same system.

Chuck Rainville  10:24  
Yeah, and I mean, if you get really good in your own particular sandbox, and you think you know, just about all there is to know, you look, and you see, oh, there's actually another sandbox over there. I don't know that much about that. And it's leveling and humbling in that way, too. Because then you're you kind of have to walk over with your hat in hand. It's like, Hey, what do you guys do? And, you know, if you're not invited to every party that you stumble into, so I think the exposure to the methods that I got in the program, made them a little less wary of the guy from some other group who, right. Yeah, because I mean, you know, in business, it can be sometimes about breaking down silos and sometimes fortifying them.

Dennise Cardona  11:06  
Yeah, the intellectual property, right. There's this whole defense mechanism that sometimes happens. Yeah. And when that happens, and everybody loses out, because yeah, if you're not sharing information that can be critical. You, you really could be missing the step on some things.

Chuck Rainville  11:22  
But I think UMBC was good about the idea of like, have a sincere sense of conscientiousness about what you do, in the sense that like, if you don't really care about the questions, you don't really care about the methods, you're just trying to get your code to run, and live to fight another day, you're going to be limited, all of the soft skills will come with a conscientious mindset. If you're really concerned about what you're doing, then you become really sincere about what you're doing because the edges get sharpened. And so I think having applied these things, or trying to work with this, I kind of get the softer skill, the Why would this person want to cooperate with me, I sort of get all of that. But I think that was also just reinforced though, by the basic attitude that all the all of the professor's had to their craft. Nobody was rushing in like doing putting the finishing touches on their notes, with their shirt tails on tucked and everybody right everybody was just very conscientious and very prepared. And clearly it worked in applied settings, and didn't care to be like very, very abstract. Nobody was browbeating you with their intelligence, they were browbeating with you the fact that they were they have done this successfully. And they weren't even browbeating with you they were impressing you with the fact that they've done this,

Dennise Cardona  12:37  
that is very powerful, and learn from other people and their experiences is what I love about, about being in that kind of a classroom environment, even a virtual classroom environment, my program is completely online. And what I love is his discussions with people who are living and breathing, the kind of things that I want to learn. And so it's so much more enriching learning from that kind of experience than solely learning from maybe a passive way. And it's interesting that you talked about mindset. And I say that because a colleague of mine, and I were just having a conversation. And, you know, we're in marketing. So we tried to market our programs. One of the questions that we had for each other yesterday in our discussion was, what are the typical mindsets of students within a particular program? And it's so fascinating to me that you just talked about mindset, because that is such a crucial factor in even selecting a program like understanding what your own mindset is, and even asking those questions to the program. Director before you even enroll, like what is what is the typical mindset of the faculty of your program in general of the environment at UM BC? Have you have the students that I'm going to be collaborating with? Because there is a general mindset? And I love that you just pinpointed the conscientious mindset is very powerful. And when you are putting all that effort in to learn something, you want to be surrounded by conscientious people.

Chuck Rainville  14:07  
Right. And one of the things is, I think all but one of my courses had a, we're all in this boat together project. And you know, everybody's sorts themselves into their natural abilities and stuff like that. Normally, those type of projects are a source of trembling and trepidation, recommendation and you know, rage. But the students there, you know, no matter what their age, or what their background was, had had it pretty much modeled to them that the norms and customs of the program were to pretty much try your hardest. Pull your own weight. Admit when you can't do something, get help with it. And effectively, we put you into a team project situation, so that you can work like a team because you're going to be doing that the rest of your life, wherever you want to work. Not? And you know, and nobody was in there saying, Hey, can we can we maybe make this whole thing a little more theoretical? Nope. Right? Nobody was asked. Nobody wants that. Right? Yeah. Right, even though it would get you out of right? Can we just have a second exam maybe. So the throw you into the deep end, and everybody helps. You know, everybody swims. Every syllabus I get, I'd say, okay, like, let's go to the end, let's see what the actual deliverable is, like, while deliverable I'm so socialized. But let's see what the, let's see what the last thing they'll be grading is. Any notes, I was like, team project, and the first time I saw it, I'm like, Oh, I haven't done a team project. And, like, ever, but I do it every day when I go to work, but I never really think of it in those terms. And I'm comfortable if people were if they know what their skills are. And our roles are just kind of clear. Whereas they put me, you know, to with this guy whose background was in like spices, and I'm like, what's his? What's this guy gonna know? You know? And who, you know, but why don't you get it? It's like, what do you do at work? What distribution neat, like, you know, so so there's a kind of like, not only just kind of like the cross cultural exchange, but the, the negotiating with somebody that you don't have a common background with. But you do have the guardrails of what the expectations are within the program, where everybody's told you got to be serious about what you're doing, people are going to rely on your work, you have to be serious about it. So they've really impressed that on us pretty

Dennise Cardona  16:28  
quickly. What I love about that, it's, it reminds me of Game Day, practice, practice, practice. But when game day hits you, you want to be able to take off and know what you're doing. And by putting ourselves in these uncomfortable situations, because it is uncomfortable for certain people to be teamed up with somebody they don't know where the different background could be different cultural differences, different, completely different industries that they're intimidated by or not impressed by or whatever it could be, and then putting yourself in that situation to be able to learn. How do I work with this? If this is my real, if this is real life? This is game day? How am I going to if this was my job, what what what would I do here, and it puts you in that position to always be thinking on your feet. Yeah. And always being able to think strategically in a way that you both want to win. And you both want to win. It's a team effort. And so that, to me, is such a really, incredibly transformative process is going from that trepidation that you talked about to that realization, that man, this was really good for me,

Chuck Rainville  17:30  
by the final course, when I got the syllabus, I look towards the end, as I always do, and it's a team project. And I kind of raised my eyebrows because it's like, well, the first time you saw that you had to do a team project, your attitude was completely different than what your attitude is. Now it's like, you kind of learn the ropes around this one you can deal with anybody from any background from any sort of different set of assumptions about how measurement works is that the because at this point, you all have this the share mindset, the sense of what the expectations are, and how it is you're supposed to work with anybody you're thrown together with simply because the first letter of your last name is close to the first letter of their last name, right? It's not like the fates are decreeing these things. It's quite literally like this is who you're working with, and nobody gave it any thought, make it work.

Dennise Cardona  18:20  
What would you say Chuck is the biggest takeaway that you got from studying at UMBC in this program,

Chuck Rainville  18:26  
the biggest takeaway for me is that there are two different paths, a handful of us were there to improve their on the job skills and to be to become better assets to their institutions. So the biggest takeaway from me, as somebody who desired to be more informed about what I do on a day to day basis was that it's not intimidating, to learn new things, to learn new technical things that can be done. If you have a sense of how to go about it, what attitude you take towards it, getting code that doesn't run can be frustrated, being unable to put together to design measurement in a way that answers a question can be frustrating. The program teaches you that to be frustrated is a good thing. Because you are right at the threshold of making a discovery, if you're feeling that, and I felt it a million times every homework was an exercise in frustration. And then you make the leap. And you add that to your toolkit, whatever you just figured out. You now understand it. And you understand it because you felt that frustration is like not there anymore. That unquiet state where you can't get something to work because just the nature of the way things have to be. You're going to get past it. You get past it every time. It's just a matter. Have sincere intent to stick with it and get past it. So I'm used to it, I just kind of like, every time I'm flummoxed or feel like I can't get to something or can't get past it, I'm like, we you know, getting close. It's like, you know, just, I'm on the, I don't know anything about golf, but I'm on that middle part. And soon I'll be on the green and you know, that type of thing. Getting closer. Now, I think the biggest thing that people who are looking for work, and not just to understand their their role in what they do, it may be, it may be a different set of benefits that they get, well, first of all, this is going to be the foundation of all of their skills, right is going to be added to the foundation of their skills, it's not augmenting an initial set of skills, to get a layout of the employment landscape, or what what things are valued, they probably listened more closely to that than I did, because I wasn't looking for a different pasture or anything like that I was looking for something else. And the applied focus was a benefit to both types of persons. For the person who's looking to get get their foot in the door have a set of skills to get an initial position or sort of entry level position or mid level position? Yes, they'll have a portfolio, they'll show what they can do. And there'll be hot shots. But somebody, you know, some little longer than the tooth like me. If I try to be hot shot, they'll just be like, right, that would be bad. Right? Because, you know, just be like, not impressed. 

Dennise Cardona  21:33  
AW Chuck!

Chuck Rainville  21:34  
Yeah. Right. Have you been, you know, ready to get into, for me, they want to see theme and variation. I was running out of variations at a certain point, right, I knew what I knew, and kind of hit a impasse in terms of Well, I'm going to need to do something unexpected. Let me make a big move, and see what comes of it, you got to play to win. And I think I won in terms of what I wanted to get out of it. The fact that I am applying it, I'm trying to pull one of my colleagues who went through the program, we're still going through the program, trying to pull them over into it. So we can talk that type of shop more, use it or lose it. I think the institution that I work for benefits now, because of the connections that wouldn't, would not have otherwise been made. We have programs that are opening up into different areas, we're going to do more text and analysis. Now. It wasn't like a one person reboot, but I certainly kept up. Or if I didn't catalyze where things were going, I certainly kept up with where things were going. I have suspect that might have catalyzed some of it, but they'll never tell you.

Chuck Rainville  22:39  
They don't want you to think you're good. Most people suffer the delusion that they're good. Right? And so Oh, yeah, I know how to do that.

Unknown Speaker  22:53  
Real? Yeah.

Dennise Cardona  22:56  
Chuck, in terms of Alright, so a student coming into this program, in your opinion, because you've been through it now. In your opinion, what do you think would make a student very successful in a program like this? What kind of attitude do they need to come in? With? What kind of skill sets Do you think they need to have in order to really be successful in a program like this?

Chuck Rainville  23:18  
I would say that requires the baseline of some some technical background to begin with. So the one thing that I think I've benefited from and, and I would hope some of the other students that had some background in the understanding statistics, initially, and not necessarily social science, statistics, but like, you know, business or econometrics, any of that, to walk in the door and to know what a mean, and a standard deviation is, and you know, what linear regression is, would be exceedingly helpful, because a lot of the algorithms, a lot of the a lot of the estimation, and a lot of the predictive analytics methods, there were a lot of them. So the fundamentals of statistics, you weren't going to get a whole set of lectures about, you know, violation of assumptions, and you know, how to draw a line through a least squares scatter and all this stuff, you know, all the stuff you do in like a research conduct course, or something like that. So having that under their belt, which I imagine most people would have, getting into this program. A sense of the imperatives of businesses, helps, there's different types of sectors, right? what's expected in a place that's doing research and development, what's engineering, what's expected? What are the norms of, you know, what are the businesses? What's the room for error and estimation, what you know, what are the basic types? What type of person is going to look at your work? Is this the type of person who looks at things and asks, on what timeframe? Is this information useful to me like, some people look at work output, like if someone in business is this somebody who looks at it and their first question is like, how does this make me money? Or is this somebody who looks at something's like how does this work in the mid range? Or is this somebody who looks at this? It's like, yeah, I see the beauty of this idea. It's going to take some time. So to understand what the imperatives are, if you're looking for your first job to understand the types of institutional imperatives that there are, like in government, there's different sets of institutional imperatives than there are nonprofit, where I am, versus how do I make money with this now approach like for startups, and so these people have gone to all different types of places like social science, research, econometrics, I'm always kind of leaning to the idea of like, maybe a business background would help. Or at least an understanding of industry, in the region that people are working or DC lay of the land. So some type of practical experience, or some sort of practical sense of how to apply these and what those settings are like, and what people are looking for in those settings. And definitely a little bit of a background in statistics, at least, at least matrix algebra and some of the some of the basic statistical tests.

Dennise Cardona  26:05  
So that's helpful, because I think a lot of times students that can read the website, they can see what's required. But to hear it from somebody who's actually lived and breathed the program. To me that just is more social proof that okay, this is this is really what I need to focus on it, I probably should brush up on these skills, or I'm set for it now. Okay, great. This is the perfect time to enroll. Yeah. One question I wanted to ask you, what did you want to get out of this program? And did you get it?

Chuck Rainville  26:36  
What I wanted to get out of this program initially, was to be able to zip around in Python. And to leap over from the statistical software I was using to Python and to use all of those packages, and then realized that was not really that was not it. I didn't really know that in and of itself was not a useful goal. So the question is, whatever you're using, whether it's Python, whether it's our, whatever language you're looking at, it's the appropriate language for the question you're trying to answer will determine what package what language what what environment you're working in. So I really had this literal, like, well, I can do what I always do, just but I can do it in a new language. That's not really an improvement. That's just like, doing what you're always do. And I figured out quickly, that it would be good if I didn't get what I wanted from the program. Because the way I initially conceived of it was all wrong. So I ended up working in all types of different environments. I'm like, Well, I want to learn Python, but here I am zipping around in a language called Scala. Well, you know, it seems to be working. And I had never heard of half of these things. And, but they were the appropriate languages for the environment. And I learned that Python is a popular language, but it doesn't fit everything. Right. So it's more about the fit of the environment, the language, the the certainly the the algorithms, certainly the tests in the end that you're using. It was basically saying that, yeah, you know, a language is not a toolkit, right? a toolkit is something that probably has dribs and drabs from all different types of languages. So I got more than I actually desired. I didn't realize I'd set my ambitions kind of low. Because I just didn't I just didn't imagine there was that much. I didn't know. But could get in fairly quickly. Once you get over the mindset of like, Well, you know, that Python is not that important, actually. I mean, that code is code. You can find it in any language or you know, but it certainly wasn't there. They didn't sit you down and and say, you know, you're going to pip install this package, hit, hit run. Okay.

Dennise Cardona  28:59  
Right. Right. One by one step by step. Yes. Yeah. Make sure you go through it. Don't skip a step.

Chuck Rainville  29:04  
Yeah. Well, it disabused me of a lot of linear thinking, too. Because for statistical analysis, it's so linear. I mean, it's just like, Yeah,

Dennise Cardona  29:12  
yes. So programs to be to have that linear mindset.

Chuck Rainville  29:15  
Yeah. I think my mindset now is in terms of like, the how things come together. It's not a lot of step by step, clean documentation. This that the other, there's a Well, once you have the code, you can always come back to it. You can always look at, what did you use defaults? Well, here's what you want to do. You might want to spell it, right. You cannot. It's iterative. And it's, it's like I played my whole golf analogy. It's just getting there. It's getting there. It's getting there. And once you have like your 93%, hit rate, stop. 93% great. Because what you knew before your hit rate was a lot lower and you had no way of knowing it. You didn't have the code right there. You just you brute forced it yet. turned it in. But with this, it's documented, it's there, I go back to some of my old Jupyter Notebooks from the program just to see, you know, some of them are homeworks. But some of them, you know, but it's all there. It's always like, Oh, you know what, this makes things a little more efficient, I can just pluck this from this. And you know, and so, each notebook is kind of like evolving independently, all on its own.

Dennise Cardona  30:24  
And it's like, kind of like a recipe, you're pulling pieces from this recipe. And maybe if I add this ingredient in here, it'll it'll spice it up a little bit more and be more robust and do

Chuck Rainville  30:34  
Yeah, yeah. And I think, and I think it works towards this idea of automation. It's like, Well, here, you kind of like actually did this and you thought through any data, but at the point where you've done it once, can you just adapt it and kind of just build this into a front end process, and spend more time actually thinking, right, so that's the thing about a lot of the initial course coursework is really basically pushing towards automation, pushing towards the idea of like, there's some parts of a project you don't really want to spend that much time on, there's there's no lift, cleaning, and stuff like that. So that's what the I have those notebooks and, you know, nobody can take them away from me. And, yeah, bit by bit, pull stuff from a project that where you're, you know, you're just trying to figure out how much energy is being used hourly, but you're like, Oh, you know, what this might actually some of this code might actually work in this text thing I'm doing, they're not related. But you know,

Dennise Cardona  31:29  
there's valuable stuff in the past that you can buy to your, to your current state to your present, that you'll eventually be able to also apply to your future. And so yeah, I think that from what it sounds like, what your experience has taught you is that versatility, adaptability, flexibility, that will put important components to your your growth as a professional, and the more that you can adapt, and the more flexible you can be. And the more open you are to being able to use different schools of thought from your past, present and future, the more successful you're going to be

Chuck Rainville  32:03  
and to be disabused of a linear mindset, those notebooks now that I kind of evolving independently, and I have papers that I'm supposed to get to, and I have this, that the other, you know what they're all doing their thing, like, when I get to them, they'll they'll all move forward. But the whole idea of like project as a beginning, middle, and with no other projects being at any other stage at any of your time, that goes away. And so with data science, because, you know, a lot of times and homework after homework after homework with with a group project going on in the background, speed kills, if you never hit a wall, and you never slow down, you have no idea how fast you're going. So you know, so I kind of, I get pushed out of my comfort zone. And it was a good thing. I just had no idea how I was technically not going that fast, and I thought I was slipping along. So this really kind of like, sort of turbocharged, the way I look at analysis, and it disabuse me of that linear mindset, which I just gotten kind of not smug, but kind of comfortable with, I kind of assumed that's all there was. So I'm really glad I did it. Because I like a fish doesn't know about water. I didn't know that I I was in an environment that other things were going on. And I wouldn't necessarily, I wouldn't have figured it out. And if I hadn't figured it out, I probably would have felt exposed or insecure over time, or if it's not like this fear of obsolescence, but just the idea that the extent to which the be a whole class of people doing what I'm trying to do. And they'll continue to do that, for the most part. I think I want it to be you know, in that class, but also have things that I can kind of layer on top of it, see that as kind of a strong, fundamental foundation for it, but be able to do some kind of fancy stuff on top of it, razzmatazz.

Dennise Cardona  34:04  
That's a great a word to close out our conversation. For this particular interview. Yes, really has been a really wonderful interview. And I've really enjoyed the conversation because I've learned a little bit more about data science. And I love the idea that you just be brought out in the linear mindset, and how it can be limiting if you just go that whole route the whole time. So thank you for opening my eyes with that, because it's really important. And I will be very aware of that. As I event myself when I go through certain projects that I'm doing with work and with school. And with life. I tend to be very linear in some in some respects. And I think we can get more creative juice out of ourselves when we may not be so linear. So thank you for thank you for bringing attention to that. Thank you for sharing your experiences that you had at UBC and for your experience within the data science program. I'm really happy that That you got what you wanted out of it. In the end it sounded like it sounds like you did so, really happy for you for that. Okay. Alright. Well thank you so much, Chuck for your time. This has been really great. I really appreciate it. Thank you for taking time to listen to this episode of you mbcs miked up, we hope you enjoyed it. If you'd like to learn more about UMBC's graduate programs in data science, please visit us at datascience.umbc.edu

Transcribed by https://otter.ai