UMBC Mic'd Up

Applying an Ethical Lens to Data Science

February 14, 2022 UMBC Mic'd Up with Dennise and Bhavika Chavda Season 2 Episode 23
UMBC Mic'd Up
Applying an Ethical Lens to Data Science
Show Notes Transcript

Bhavika Chavda ’21, M.P.S. Data Science joins us for this episode of UMBC's Mic'd Up and shares her experience with entering into the data science world and working with an ethical lens.

She shared, "As data scientists, we have a lot of responsibility on us. We make or break platforms. We can make people's lives by using the data and we can break their lives by using their data incorrectly. At UMBC, they train us how not to take advantage of the data. ? Data science is not just about the data, but it's also about understanding human emotions along with the data. That's how we can make informed decisions." 

About UMBC's M.P.S. Data Science program
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.

Dennise Cardona  0:00  
Thank you for joining us for this episode of UMBC Mic'd Up podcast. My name is Dennise Cardona from the Office of Professional Programs here at UMBC. I am here today with Bhavika Chavda. She is a recent graduate of our masters and data science. And she is going to share her experience with us and talk about what she's doing now in the world with data science. Welcome, Bhavika. It's so nice to have you here on UMBC Mic'd Up podcast.

Bhavika Chavda  0:25  
Thank you. Thank you so much for having me. I'm excited to be here.

Dennise Cardona  0:28  
Yeah. So as I understand it, you're a recent graduate of the data science program. Is that correct?

Bhavika Chavda  0:34  
That is correct. I graduated in December 2021.

Dennise Cardona  0:39  
Well, congratulations, I'm sure that that is a really great feeling to be able to say that you graduated and that you have this credential behind you. Sure, that is a big relief off your shoulders?

Bhavika Chavda  0:51  
Oh, yes, it is. So basically, I did my bachelor's in India, and I initially came here for my masters. So when I was doing my Masters, it was a little hectic, because first and foremost, the entire place is different, the approach of education is different, everything is very different. So I was not only, you know, trying to adjust with the education path, platform of pattern, but I was also trying to adjust with the change in place and everything. And because I wish I shifted during COVID, it was a little more difficult to be able to just because I had to find resources, without them actually being available readily. So it was difficult, but I think I learned a lot. Now I feel like I can, I can thrive, even if it's challenging. That's a

Dennise Cardona  1:37  
great point. Because you know, it's usually like in those in those really challenging moments that we have to dig deep and get out of our comfort zone. Those are the moments that we really grow the most. And we figure out what we're made out of, we figure out that we have that grit inside of us that hey, if I can do this, I can do anything. Now it's a really good feeling. So can you tell us a little bit about why you chose UMBC status science program.

Bhavika Chavda  2:03  
It was mainly because of the professor's to be very, very, very honest. I was looking a lot of universities and I, I was very specific, I was very particular about the fact that I want to be able to study under the professors who are actual experts in the industry. And because UMBC his master's program was not a typical master's program program. It's actually a Master's of Professional Studies in data science. So it was very, that attracted me like a lot. Our professors were actual industry experts, my favorite professors were like, these are professors and experts that have been working with IBM and toys and Oracle and other amazing industry, sensory companies, organizations that I actually want to be a part of. So that attracted me towards UMBC a lot.

Dennise Cardona  2:53  
I couldn't agree more with you in terms of knowing that the instructors are industry practitioners is such a powerful element to the graduate programs here at UMBC. Because they're there in the field, they see what's happening, they're up on the latest trends. And they can also bring that experience that they're dealing with on an everyday basis into the classroom. Did you experience that with your professors in terms of them, bring in some really, you know, real world issues into the classroom and then having you all sort of work through them and find solutions? 

Bhavika Chavda  3:29  
Yes, actually, at every time that I was talking, sorry, we had our classes, but the professors, the professors were very specific of the fact that the books are okay as guidance. But don't rely on the books alone. Because when you come in the real world, the books don't really help. It's you, your talent, your skill. And I truly believe that because they always asked us to push beyond the books push beyond, what is it that what is it that you see in the data? But what is it that you don't see in the data? What is it that you, you think is happening from data, right? So that is such an important point, because most of the books and everything, they just tell you everything that I've learned so far, whether it be like on rent resources, or other books that I refer to as guides, it's always been like, Oh, this is the data. This is what it's showing. This is how you calculate things. And my and all the professors were like, This is what the computer is showing you. But what is it that you're not seeing? That is not showing you so what is it that you want to be seen? Try and figure that part out? Because end of the day, the data is about real people, real people need real, real life eyes on it. You're the person who's going to be looking at it. So be careful. What is it that you're gauging from it? Because as data scientists, we have a lot of responsibility on us. We make or break platforms, we we can make people's lives by using the data we can break their lives by using their data incorrectly. So it was so refreshing that everything that they were teaching us was so ethically act, you know, it was we were, like, really trained in a good way, because now I know what is it that I want to be using from the data that I'm getting? How not to take advantage of the data that I'm getting? So yeah, that was really good, at least for me, at least, you know, I really learned that from our professors.

Dennise Cardona  5:29  
That's a strong outcome. And you just brought up an amazing point that I think it's really important to highlight again, is that looking for things that you don't see. So you know, in a book, or even in a conversation, sometimes, when we are looking at the surface of things, it's very apparent, maybe what a problem is. But then when you start to dig a little deeper beyond that surface, you go into the data, you go further into the conversation, you go further into your research, that's when I don't want to call it the fun, but maybe it is, in some ways, but that's where the real work is done, is in that really that critical analysis of being able to ask yourself that question of, okay, what is this data not telling me at this point, because that often can be a critical point of further analysis and research that could also uncover some incredible solutions?

Bhavika Chavda  6:19  
Yeah, yeah, definitely. And most of the times, you will not believe this, but a lot of data signs that you see, or data analysis that is done is actually incorrect data analysis. For example, a lot of statistics that you see on TV, it's incorrect. It's actually incorrect as a as a data scientist, now, when I look at it, I can easily make this out. I'm like, Okay, this is wrong, you know, this is incorrect data. This doesn't make sense. But the, but that's what the computer is telling them. So if I was not a person, and if I didn't have the knowledge, I would easily just believe it, you know, but because I have the knowledge now, I know that all computers telling me this, this is incorrect. This cannot be correct. Because there's human and more emotions involved. This person never just takes decisions based on numbers. I will not do that, you know, as, as an individual, I'm taking everyday decisions based on my circumstances, what is it that I'm feeling at that moment, and all of those things. So as a data scientist, data science is not just about the data, but it's also about understanding human emotions along with the data, because then you can make informed decisions, it just

Dennise Cardona  7:27  
brings up a really strong point that AI is wonderful, but it's not going to solve everything for us, we still, as human beings need human being interaction to be able to understand the feelings and the emotions, and the issues that really are underlying things that a machine cannot tell us. Now, what did you expect to get out of the UMBC Data Science Program? Upon entering it? And Did it meet those expectations? 

Bhavika Chavda  7:51  
Okay, I'm gonna be honest with you. So when I came in, all I wanted was to just learn data science. Okay. All I wanted was how to interpret data. And how is it that I can find insights from it. That's it. That's that's all that I actually wanted to learn. But what I learned is so much more than that. And of course, it met my expectations is actually like went exceeded my expectations, to be very honest, like, you, the professors are so friendly. And I did my masters online, it was mostly remote because of COVID. But the professors were so helpful, you know, if I needed help, it's just one email away, and I would get a response the next day, or like the very same day, and I was given the resources, like immediately, like, even if I didn't, I wasn't there, physically, to get the resources from the library. But we were given like an online library with all the resources necessary. We were given everything. So UMBC, definitely, definitely crossed my expectations. We will, we had like different career virtual fairs and Career Guidance fairs. And we had one on one interactions with get guest lectures from outside of our outside of UMBC, which was so good, because I wanted that, right. You attended university, because you want that kind of experience. And I got that from UMBC. So definitely, I expected just like 10%, and I got like, a 90% boost on the 10%.

Dennise Cardona  9:20  
I always love to ask the question of before you enter the program, and now that you've graduated, I'm sure that you can, you can see the experience of growth that you have achieved. And is there anything that surprises you in terms of like, Wow, I did not expect to learn that or I did not that just blew my mind or something along those lines. Because I know as a graduate student myself, when I entered into my graduate studies, I had, you know, I guess surfacey type of expectations. But gosh, it really did go beyond my wildest expectations and some of the things that I learned I'm like, I can't believe I actually learned that and I'm actually applying that in my real life and it just It's a really cool feeling.

Bhavika Chavda  10:02  
The one thing that I would say that I learned was the ethical use of data, or machine learning algorithms, or AI, Facebook or any of the platforms have been in the news for a very long time. And we've seen this time and again, the improper use of data that's been happening. So as data sciences, you know, that you're supposed to be ethical about it, but you just don't know how you don't know, where's the line? You know, because there are no ethical laws that apply on AI yet. AI is growing at a very rapid pace. And the laws have not caught up to it yet. So it comes on individuals to be responsible by yourself and take informed decisions. You know, it's not about just the law, but you need to do that part now. So, like I said, like, our professors kept pushing that point, you know, they were like, Web Scraping is okay. But you need to know, what is it that you're scraping, you don't want to be scraping it sensitive information, and then making a mess out of it. You know, you don't want to expose somebody financial or medical or any of those kinds of information that can information you want to be sensitive about human emotions, what is it ethical, what is not ethical and stuff like that? So I think that was one thing that I learned. And there was something that I think I don't know what that is universities. But I really think that UMBC does, that does a very excellent job of that. So I would definitely recommend anybody and everybody to come here for sure. 

Dennise Cardona  11:39  
You know, you said something that is exciting and terrifying at the same time, in terms of the fact that there are no standards at this point with AI and data, the sharing of data, the the mining of data, the scraping of data. And that's exciting, because if there is new frontier, there's new territories to explore. And there are new standards that can be set. So that's really exciting. But the terrifying part for me is that there really are none of those. Or if there are some there, they're maybe elementary at this point. And there's a lot of work to be done.

Bhavika Chavda  12:15  
So I follow someone called Reed Blackman. And through my current company, we were actually working with Reed Blackman on one on one right now. So this is when I listened to him. He's this, he's, uh, he's very, he's a very well known person in terms of ethical AI. He gives a lot of talks. And he he's, he's writing a book right now, on the same topic. So listening to him talk, I felt like this was something that we were thought in UMBC. Right. So somebody who's been doing ethical, who's been fighting for ethical AI for the last 20 years or something. And he charges like a chunk of money just to give us like one advisor in our company, but we're kind of getting that for free and UMBC, you know, in every class. So it's kind of refreshing to see that. And I always say, I know that part. White professor said that already. So there you go.

Dennise Cardona  13:16  
I love to talk about what you're doing right now. What is your what is your role? Are you working in data science doing that every day?

Bhavika Chavda  13:23  
So I actually did my masters in data science with a minor in project management. And right now I'm working as a data scientist with a company called Data Destler. And we have we have another company called hatch, and B systems and other other different companies that we are targeting and my company, David, slur, we're already on Oracle marketplace. So it's a new company. It's just a startup. It's just a one year young company, but we are going places for sure. My company is really awesome. I love it. And the CEO of my company, Charles Peary, he's he was actually my sixth full professor. And I was studying under him, and he just spoke about his company. He said, Oh, I have this company, blah, blah. And then I just applied to it. I didn't know I'm going to get it or not. And then he was also kind of like, surprised. Oh, you are you part of my copy? I was like, Yeah, I did. And then he took my interview. And he like, obviously, because he was teaching me so he kind of knew my capability and everything. So it was that that's how I got the job. But oh, that's Wow, that is

Dennise Cardona  14:32  
really cool. Were you working in data science when you started the program? Or is this like just a brand new venture into data science at this point?

Bhavika Chavda  14:40  
Oh, no, not at all. i My My background is I was a creative head in the Indian film industry for about nine to 10 years before I got into technology. Then I did my bachelor's in computer applications, which is very different from data science. But then I was always working with data at Even when I was working in the film industry, it's just that it was not called data science. We were always looking at costumes of period. Like for periodical films, we were looking at historical data, what, what stitch did they use blah, blah, blah. So I was always working with data I just didn't know it was called data science. It was only later that I found out that oh, the thing that was doing that state of sign, so let's get into data sites, that that's how it happened. And technology wise, I have experienced like I freelanced before, but I never worked as a data scientist, this was officially my first job.

Dennise Cardona  15:33  
Wow. And do you would you say, then you feel like the graduate program here at UMBC helped give you that sort of leg up in the in the field to be able to now go to this job and really show your value?

Bhavika Chavda  15:48  
Definitely, definitely. So there's a whole lot of times, when, in my day to day tests, I kind of the other I work with a team, right? So sometimes they don't understand a couple of things, or they don't know how to troubleshoot a problem. But because of my experience with the professors who are industry experts, at UMBC, I'm able to troubleshoot those problems in like a matter of seconds. So much so that just as an intern, now I'm actually leading a team. And my experience, as a data scientist is only like seven months. So in like seven months and leading a team, why? Because I'm just applying the same principles that I use, that I learned from the professors from UMBC. So it Yeah, UMBC definitely helped me.

Dennise Cardona  16:36  
What would you say is your biggest takeaway from studying data science?

Bhavika Chavda  16:42  
Takeaway can be defined as so many things. But I mean, when you think about it, what is it about trade when you you have a product or when you have a methodology that you can probably use to advance the human race, and you can advance the human race in a positive manner or a negative, right? And it falls on you, especially when there are no laws? In that specific field that falls on you. That's your responsibility. What do you want to do with it? So I think my biggest takeaway was the same thing that I have data scientists appealed. I have the knowledge now. And I have the application, how to apply it, right? I know the application methodology, how is it that I want to use it, and my takeaway would be the way, that way of doing it, the process of doing it, which is the ethical way of doing it, which is the right way of doing it, just because also one more thing that I learned from UMBC, which is like something that was very weird for me, was there, a lot of times something can be lawful, but just because it's a law does not mean that it's ethical. When you when you look at it, it might still not be ethical, you know? Because like, Okay, so for example, there are like some social media platforms, I'm not gonna be naming them that you actually use for free. And they clearly tell you that, okay, they're going to be using your data, which is fine, that's ethical, they've informed you in terms of conditions that they're going to be using your data. But what's not lawful, is when they say they're going to be selling your data to three other platforms. And when you go to those three other platforms, again, those three platforms are selling your data to six of the platforms. So indirectly, your data is going not just one or two or three people, well, cheap platforms, it's actually going to like 50 other platforms indirectly, you know, so, yeah. So that's, that's something that I learned, which is, just because it's lawful doesn't mean it's right. You got to take that responsibility, you got to do the right thing.

Dennise Cardona  18:44  
Well, it sounds like you really did have a great experience learning, it's been a great learning journey for you. And you sound like you are completely well prepared to be in this industry and be an advocate and a role model for other people in the industry, which it sounds like that is a very important role. And I'm really excited for you that you that you've had that you had this experience now and that you have this new role that you're able to just take it and run with it and really apply the things that you have learned in the classroom. Sounds really exciting.

Bhavika Chavda  19:16  
Thank you so much. I'm really excited to Yeah. Oh, first and foremost, I'm excited that I completed my graduation. Yay. And now I'm excited to actually apply it and move ahead. So one more thing I learned from UMBC. I actually was a because I'm like a global ambassador with Women Tech Network. I was able to talk about my experience at UMBC at my as a speaker at the network. Net conference as there's another conference happening for women in technology. And I will be doing the same thing there. So I'm really excited. I'm a lot of opportunities just because I did this program. So thank you for that.

Dennise Cardona  19:53  
Oh my God, that's fantastic. I'm so happy to hear that. It this has been a really great conversation. And I'm so grateful that you've been here with us today. Thank you so much for being here and sharing your experience with us.

Bhavika Chavda  20:08  
Thank you for watching me.

Dennise Cardona  20:10  
All right, well, keep like keep enjoying yourself enjoying the journey and the process. Keep doing really great work because you just sound like you're in the right spot right now. So,

Bhavika Chavda  20:20  
thank you. Thank you so much. I'm excited.

Dennise Cardona  20:23  
Thanks for tuning in to this episode of UMBC Mic'd Up podcast. I hope that you enjoyed this episode. If you'd like to learn more about the data science program here at UMBC please visit us at