In this episode of the UMBC Mic'd Up podcast, join us as we sit down with Vyshnavi Vemuri, a recent graduate from UMBC's data science program, to discuss her journey from graduate school to the professional world of data science. Dennise Cardona from UMBC's Office of Professional Programs leads the conversation, diving into Vemuri's motivations for pursuing data science, the role of collaboration in her academic and professional experiences, and the valuable skills she acquired at UMBC that have helped her succeed in her career.
Throughout the episode, Vemuri shares valuable insights into her time at UMBC, highlighting the benefits of obtaining an advanced degree from UMBC's data science program for prospective students. From the emphasis on real-world applications and hands-on projects to the importance of effective communication and continuous learning, Vemuri's experience offers valuable lessons for aspiring data scientists navigating the transition from graduate school to the professional world.
Whether you're a current student considering enrolling in UMBC's data science program or an aspiring data scientist seeking insights into the field, this episode provides valuable insights and inspiration for your journey. Tune in to learn more about Vyshnavi Vemuri's experience and the benefits of UMBC's data science program.
Learn more about UMBC Data Science Graduate Program: https://professionalprograms.umbc.edu/data-science/
In this episode of the UMBC Mic'd Up podcast, join us as we sit down with Vyshnavi Vemuri, a recent graduate from UMBC's data science program, to discuss her journey from graduate school to the professional world of data science. Dennise Cardona from UMBC's Office of Professional Programs leads the conversation, diving into Vemuri's motivations for pursuing data science, the role of collaboration in her academic and professional experiences, and the valuable skills she acquired at UMBC that have helped her succeed in her career.
Throughout the episode, Vemuri shares valuable insights into her time at UMBC, highlighting the benefits of obtaining an advanced degree from UMBC's data science program for prospective students. From the emphasis on real-world applications and hands-on projects to the importance of effective communication and continuous learning, Vemuri's experience offers valuable lessons for aspiring data scientists navigating the transition from graduate school to the professional world.
Whether you're a current student considering enrolling in UMBC's data science program or an aspiring data scientist seeking insights into the field, this episode provides valuable insights and inspiration for your journey. Tune in to learn more about Vyshnavi Vemuri's experience and the benefits of UMBC's data science program.
Learn more about UMBC Data Science Graduate Program: https://professionalprograms.umbc.edu/data-science/
Dennise Cardona 0:00
Hi, everyone, welcome to the UMBC Mic'd Up podcast. My name is Dennise Cardona from the Office of Professional Programs here at UMBC. Today, we're going to be talking about data science. I'm here with a recent graduate of the program. And I'm super excited to talk with her about her experience at UMBC. Thank you so much for joining me here. It's wonderful.
Vyshnavi Vemuri 0:23
Thank you so much for inviting me.
Dennise Cardona 0:26
It's a pleasure. So let's get right down to it. You graduated May of 2023. Fantastic. From the data science graduate program. And how has life been since then? What are you up to?
Vyshnavi Vemuri 0:38
It's good, no, at present I'm working as a data analyst. And soon Maryland. So I'm working for a company over here. It's called Medical Mutual. And it's nice, it's fantastic. There are no nothing to feel bad about. It's just like after graduating I'm working on. So I know the recession hit really bad. And I'm so thankful for the job and the skills that your Missy provided me to learn into this job. Oh, yeah, it's going pretty good.
Dennise Cardona 1:04
Oh, I'm so happy to hear that. It's such a great feeling. To know that the work you did you know, it's hard work, being in graduate school, putting that effort forth really pays off. In the end, you're just more well equipped to go out there in the world, the real world and apply those skills that you learned in the classroom, no greater feeling than to be able to be of service and give value back to the community that you become a part of. And so it's great to hear that. Now thinking back on your time at UMBC, what motivated you to pursue a career in data science, and how has that initial motivation evolved throughout your journey so far.
Vyshnavi Vemuri 1:46
So, I did manage that in Electronics and Communications, which is a whole different field. So I come from that background in the campus placements, I got into a role that had wide range of opportunities. In the beginning, I started as a manual tester, but later in the different projects in working in different ways. So I evolved as an analyst over there. So I don't want to stop there. And I know my skill set is pretty limited to whatever I'm doing, I want to explore further in the career. I know data science is an ever evolving field, I just want to be a be the knowledgeable, the most knowledgeable person in the field and just learn as much as I can and provide more inputs in terms of providing the business insights or changing the business strategies, whatever it might be. I just want it to be that so I am digging through various colleges, I love to do the research and all of this maybe that is what brought me into this field. So I did a lot of research in terms of universities and rankings everything being an international student, I checked every single thing. And then I came across one of the well known YouTuber called U DJ, interviewed one of the UMBC students. That's where I found the university and I was so thankful I did. And from there, everything is so great at the university is great. The coursework is amazing, uh, talking about the coursework little later. But yeah, the coursework is amazing. And the professors and personally, I feel, masters and MPs, data sciences and MPs program and MPs has a little bit of difference. We get the we get the lectures from the professionals in the industry, we also get to listen about how their day to day life works and how everything happens on the other end. So that's the beauty of MPs program is what I felt and I thought this is the right opportunity for me to learn and grow. And I just took it and I'm so glad I did it.
Dennise Cardona 3:44
Yes, it sounds like you sure did take that opportunity to learn and grow. That of my motto is learn and share but learn and grow is the same the same principle. It's being able to realize one of my, I remember a while back and early in my career here at UMBC, a vice provost once asked everybody we had a meeting and he once asked everybody, what is the value you bring, make sure that you show up every day and bring that value, and you can't go wrong. And that has stuck with me. I think that was probably maybe 12-13 years ago, I heard those words. And that really has stuck with me. And it sounds like that sort of the same tangent the same pathway should say that you take in seeing the value in education and improving yourself and being able to learn all the skills you can to be able to be part of a really dynamic team of data science scientists, it's really important to be able to show up and bring that so, kudos.
Vyshnavi Vemuri 4:40
Thank you. Thank you. And also like the learning growth. That's really interesting, too. Even I feel that's connected with me.
Dennise Cardona 4:48
Yeah, absolutely. Now in the field of data science, collaboration is often vital being part of a team. Can you share an experience where teamwork played a crucial role in the success of a project during your studies or even in the role that you're in right now, any lessons learned on collaborative efforts?
Vyshnavi Vemuri 5:10
I think collaboration brings different perspective to any kind of project. It's just not like a whether it might be in the academics or whether it's in the real day to day work, it brings a different perspective. And we get to see the same project from different angles. So that will give you a better outcome, I would say rather than one person sitting and doing the whole project, if it's like a team effort, and a certain number of people are involved, I would say, it's going to be more diversified in the same time and give the better results and better outcome at the end. So that's something I learned throughout the journey. And there's one project at UMBC, we did a chat body or for a team of four, and we did a project on Chatbot. It's for data science program. It's called data tree world. And it's a huge success. And Doctor, Doctor, organism, charities are extremely happy about the project. And it's, it's just the team collaboration, we are a team of four or three. And then there was people, we just divided the work based on the categories. And everyone is different in some of the other one is good at communication, one is good at writing, and one is good at research. So we started doing the research. And then we dig more into it. And we did the strategic planning. And then we made sure that we had the deadlines and follow them and again, meet up the professor in between so that we are keeping on the track. All of this would not have been possible without a team one person handling everything is a huge thing. And teamwork plays a crucial role in getting the better results and better output at the end, I would say so yeah. And thanks to Tony, the another professor is really amazing. I took two courses under him. Both of them are excellent. And I'm super happy about it, yeah.
Dennise Cardona 7:00
Wow that's fantastic. And you I can't I couldn't agree more teamwork. So I think of I think of it as think of yourself as an individual trying to do everything, trying to be an expert in every capacity, we would just burn out, we wouldn't be able to do that we wouldn't be able to thrive, like we can, when we're in a team, you know, like in a household, even my spouse, and I love to cook, he doesn't, he loves to fold clothes. I don't. And it's everybody has a part everybody has an expertise, especially in the real world working professional. We all have our expertise areas. And I think it makes a stronger individual and a stronger team, when we allow ourselves to take the lens take the view of allowing other people to shine in the areas where they shine, and us focusing on exactly what we are good at. And then everybody bringing those really great things they're good at together. And it's so powerful.
Vyshnavi Vemuri 7:58
Yeah, and also, it's a good learning when you're in teams, so you get to learn from others, which won't happen when we do it individually. We always get the different perspectives. And we tend to learn a lot when we're in a part of team rather than working as an individual person.
Dennise Cardona 8:16
Absolutely. Now, the transition from academia to the professional world can be somewhat challenging for people. How did UMBC's data science program prepare you for the practical aspects of the industry? And what adjustments if any, did you find necessary after you graduated?
Vyshnavi Vemuri 8:37
So I would say in terms of the communication skills, we do have projects in the whole academics. It also focuses on the communication. So we make sure that after completing the project, we present it to the students and the professors, or the guest lecturers, whoever is there. It's something the communication has developed a lot during my academics. And I would say what if I can change or if that's something different in here is the deadlines. The deadlines are completely different from what we do in academics, the real world deadlines are completely different. And what we do is completely changing the whole scenario. So that's something I have to focus more on. And also, while dealing with the tech and non tech audiences is one of the key aspects of data science, which I didn't know during the academics and has to focus more after my academics. So you have to know the audience whom we are talking with and dealing with the right amount of the don't use too many industry jargon. Nobody's going to get you at the end of the day. Just try to keep it simple and whatever you're going to just keep it clear and simple. That's the only way and make sure you take the notes and ask as many questions as you can while doing the analysis before the analysis. And then take the feedback at the end of the project or after you give your Presentation take the feedback from the audience. This is something I learned after after graduating from UMBC. And the skills, the communication and the theoretical and hands on projects are something very valuable during my time in UMBC.
Dennise Cardona 10:15
Yeah, you said a bunch of different golden nuggets there that I'd love to touch on. But the one that really stuck out to me was to keep it simple. And I think that can apply to anything we do in life, the more complicated we make something, the more complicated as it's going to be for ourselves and for everybody around us. And especially when you're dealing with the just the importance of data management, data analysis, finding ways to be able to keep it logical and simple, I would imagine, helps to keep the project in its best light, just being able to focus in on that task at hand. And do it to the best of your ability, by keeping it simple, as simple as it can be with data analytics.
Vyshnavi Vemuri 11:02
Yes, I agree. .
Dennise Cardona 11:03
Absolutely. Data visualization is a vital aspect of communicating findings. Are there specific tools or techniques that you found effective in conveying complex data insights to both technical and maybe non technical stakeholders?
Vyshnavi Vemuri 11:23
I wouldn't say in terms of tools, but if we use the recent tools, rather than sticking to PowerPoint, just go through Tableau or Power BI, the visualizations may be little more in depth and can reach more number of non technical audience. But I would say the techniques are more important than the tools the way you represent the data. And the way you talk about the storytelling is the key part in data analysis. Or in the whole of data science field, the storytelling plays a crucial role. So like I mentioned before, know the audience and do your research before, ask as many number of questions as you can. And just try to take the feedback at the end, these are the things that are going to help in day to day life. So whenever there might be some mistakes, or there might be something that the audience didn't catch, if you take that feedback, that's going to help you for your next presentation, and you are not going to do the same kind of a mistake again. So taking the feedback plays a crucial role of from the tech or non tech audience. And again, don't use jargons. Just keep it simple, as we are discussing. And that's something I follow in my day to day life. So that's it about the tech and non tech audience and communicating it.
Dennise Cardona 12:35
I love what you said about the storytelling aspects. Because especially when you're dealing with a non technical audience, a stakeholder, I think that's really crucial to be able to break it down in language that they will understand and care about. So I look at for an example what I do in marketing. I'll do some data analytics on our social media reports and our blog report. And if I just throw a bunch of facts out to the team, I think they would just maybe, maybe they wouldn't take it to heart the way they do. When I tell a story behind it. What is the story behind this data? What is the story that this data is telling us? And why do we care about it. And I think that's important for anything to be able to for anything that we do is tune into who our audiences and figure out what is the big challenge or problem they're trying to solve and communicate that through storytelling through the data.
Vyshnavi Vemuri 13:32
And also, keep the right visualization is something I would add on to this point. I know there are a lot of new visualization techniques and new charts and graphs coming in recent times. But just keep it simple. And something that is understandable to everyone and not some weird visualization through your data. Just keep it nice and clear weather to attract the more number of audience and they can grasp what you're talking through the visualization. Just keep it clear and simple. Yeah,
Dennise Cardona 14:03
yeah. continuous learning is essential in a rapidly evolving field like data science. What strategies do you use to stay curious? And to expand your skill set outside of the formal academic environment?
Vyshnavi Vemuri 14:19
Yes, I would say I mainly focus on the LinkedIn, I try to be active on LinkedIn as much as I can. Because everything, every company, whatever they do, whatever the small thing that's posted on LinkedIn, so I try to stay active on LinkedIn. Apart from that I read different blogs. There are different articles medium analytics with the that post about the data science stuff. And now there are even more new articles and blogs coming in with the AI being transformed a lot in the past few years. So there are so many AI blogs that are coming in. They just give you daily updates just so read through the articles. Whenever I get some free time. That's one thing I do. I and also try to attend the conferences is what I would say to the current students, which I did lack during my time, I only attended one conference that to at UMBC. It's a DAX conference. So I would say attending conference is the main thing that will play the difference, that will play a crucial difference here. Because in conference, you get to hear from the outside people with different industry people, what they're talking about what their current company is working, and how it's evolving what they did, and what they lack everything. So going to the conference is one key step. And the more number of conferences you attend, the more number of networking happens over there. It's not like LinkedIn, you send it to you send the request to 100 people and you get accepted by 50, whoever 20 people you're going to meet there, all of them are going to be in your LinkedIn, and you know whom to contact when you find a role in the company. So go attend the conferences and do as much as networking as you can. That's the best thing you can do in your whole academics.
Dennise Cardona 16:01
Yeah, that is fantastic advice. I'm a big, big proponent of LinkedIn, and conferences as well, the conferences gives you that face to face, or if it's a virtual conference, at least it gives you that communication, that one to one with particular people. And so that gives you that networking bridge. So when you do when you are on LinkedIn, when you do see a role at a specific company that maybe you met somebody at that company at the conference, it gives you that in to be able to talk with somebody about that. And it Yeah, I agree. I think that's great. And not only that, but even on LinkedIn, I find in my field. I'm also an instructional designer. So I tend to use LinkedIn to be able to join in on conversations within LinkedIn. And that has brought me a lot of just networking opportunities in, I've learned so much too, because there's so much valuable information from people who are out in the industry doing this kind of work for data science, and maybe posting what they you know, something that they've learned something that helped them thrive, or maybe a lesson that they learned. I think it's really a wonderful. I don't want to call it a playground, but I guess I'll call it a playground or a dojo to be able to just try out different ideas and see what other people are doing. Yeah, I agree.
Vyshnavi Vemuri 17:17
And now it is we see a lot of stories and what is happening in the life. It's nice to watch the stories, it's even more engaging them just to post the post about the story, what they did and how it happened. It's even more interesting to learn from real life experiences rather than just the normal way. Yeah.
Dennise Cardona 17:37
Yeah, definitely. As a recent graduate entering the workforce, what surprises or unexpected challenges did you encounter? And how did you navigate them?
Vyshnavi Vemuri 17:49
I would say the one thing as I discussed before, the art of telling story to non tech audience is one thing. And just dive a little more deeper into the topic and just do, I again, did my research, I'm back to the research part, I did my research and learn how to present to the non technical audience, and the deadlines, the work life balance that we come from different we have the past two years, I'm like, just dealing with the studies and just dealing with the part times or whatever I'm doing assignments. But work life is different. It's not like a two hour class you attend. It's like from nine to five. And again, you have to balance your life on the other end. It's a whole different story. So balancing that is little different, difficult for me. And I learned to prioritize the task and set the boundaries and everything. So that's where I think that one thing changed after the graduation. And that's the main aspect. Yeah.
Dennise Cardona 18:46
So looking back, now that you've graduated, what would you say is the greatest takeaway from studying here at UMBC?
Vyshnavi Vemuri 18:55
So you MSC Data Science Program offers a wide range of pathways and also the professor's come from different industries across the US and they try to teach you the real world examples rather than just sticking with the theoretical knowledge. And the pathways will provide you have more opportunities to choose from whether it might be healthcare or cybersecurity, even if you're not sure in the beginning, that is going to help in the later. So that's what excited me about this program particularly.
Dennise Cardona 19:25
That's fantastic. Is there anything that I haven't asked you that you think would lend value to this conversation for maybe somebody listening in or viewing this on YouTube that may be thinking or considering enrolling in the UMBC data science program? Anything that you might want to add?
Vyshnavi Vemuri 19:46
Not anything in terms of university, universities, great, the ranking is good and the alumni are over the place. They're doing amazing work. That's something it speaks a lot about the program and the curriculum it is in the university. When you see the Illumina, you know how the university is doing. So they're doing pretty good job out there. That's how I filter the university too. Yeah, that's something. But in terms of job search, I would say to the current students, or whoever it's listening to this podcast, just try to be consistent, consistency is the key in this difficult world, I would say. The recession is happening and everything, so many things are happening, their values, evolution is happening. With all these things happening around the globe, I would say consistency, and networking are going to help you to achieve your dream, role or achieve whatever you want to be. So just be consistent and be patient, everything is going to happen.
Dennise Cardona 20:43
I don't think I could think of a better way to close out this episode than that statement. That was fantastic. I want to thank you so much for being here with us today. It was really great having this conversation with you.
Vyshnavi Vemuri 20:54
Thank you. Thank you so much, Janice, thank you for inviting me again. I'm honorable to be a part of this wonderful podcast that you're doing. That's nice. Thank you so much.
Dennise Cardona 21:03
Thank you so much, everyone for tuning in and listening to this episode. I hope you enjoyed it. If you'd like to learn more about our offerings, do a search for UMBC data science graduate program, or simply click the link in the description.