Meet the Social Media Coordinators: Ronica Gupta & Evie Klaassen
Hi everyone! We are Ronica Gupta and Evie Klaassen, and we are the Social Media Coordinators for Cohort 10 of the Masters in Data Science program at the University of San Francisco (USF). We’ve interviewed a number of students so far — with more to come — and we wanted to share a bit about ourselves as well.
Our Backgrounds
Evie: I grew up in Sonoma County, which is about an hour north of San Francisco, so I came to the city a lot as a kid. I went to UCLA for undergrad and I got my Bachelor’s in Psychobiology and Education Studies, which is pretty much a mix of psychology, evolution, and education. While I was in college I did a lot of research, which is how I got exposed to working with data. In the UCLA Psychology department they emphasize learning statistics, so I had a good introduction there as well. After that, I worked at a psychological assessment publishing company where we published screening tests for various learning and intellectual disabilities. There, I was a part of the research and development team, so I was working with more data in that context. I knew that I liked working with data, but I didn’t want to go down the route of getting a PhD and going into research and academia, so data science seemed like the best fit for me.
Ronica: I grew up in India and I did my undergrad in electrical engineering. As a kid, I have lived in different places. I’ve lived in the UK and Australia, but I always moved back to India because that’s where my family felt I should do my undergraduate education, and so I did an electrical engineering with a minor in computer science. After that, I worked as an analyst for about a year, all during the pandemic. During my second year of undergrad, I was introduced to robotics in electrical engineering and that’s what kind of moved my focus towards machine learning.
Evie: How long did you live in each country?
Ronica: I lived in the UK for about a year, then in Australia for about two and a half years.
Evie: Wow, that’s so cool. I just grew up in one place — I was born in the Netherlands, but I moved here when I was about two years old, so I don’t even remember it.
Why Data Science?
Evie: I think the pandemic really shaped my push towards data science. During the pandemic, I saw how many people either lost their jobs, couldn’t go to work, or had a hard time working from home, and I wanted to make sure I was going into a career that was going to be really resilient to all of those things. During the pandemic was also when I started taking online coding classes. I had never learned how to code before, so I just started playing around with it and I really liked it. Originally, I wanted to go into clinical psychology and be a therapist, and I wanted to do lots of psychology and education related things, but all of that seemed like it wasn’t really meant for me. I enjoyed the subject matter, but the actual work of sitting down with clients didn’t really seem like it was for me and I liked how data science could be used in so many different ways. I knew that I would be able to use it in a way that would help the people that I originally wanted to work with in the first place.
Ronica: Yeah that’s true, I think the thing about data science is that you can apply it to whatever field you like, so your previous experiences are never going to waste. For me, during my undergrad as an electrical engineer, we had a lot of courses that lead to robotics and build on concepts of artificial intelligence, so that kind of got me interested and intrigued in that area. Then, while working as an analyst, I got a more hands-on experience with data but I wanted to explore more of the modeling side of the work, so I think that’s what brought me to data science.
Why MSDS at USF?
Ronica: I think the experience that USF gives you is something that no other university can provide. The location, the one year program — it’s all very targeted and that is so important (especially as an international student!). When you’re moving to another country, you want to know if the program will help you in your career in the way you want it to. I think that USF completely nailed that, considering the timeline and the opportunities they provide both in the classroom and with the practicum. It’s not easy for a recent graduate to get a job, especially in a field that is very technical and competitive, so to come out of the program with real experience where you can say that this is where you helped somebody build something because of your data science knowledge, I think that was one of the main selling points for me.
Evie: Yeah, same here. I think a lot of people have shared the same answer in our student spotlights as well. When I was looking at programs to apply to, I didn’t really see anything else like USF’s program, especially with the practicum component. I also really liked how this program seemed more welcoming to people coming from various educational backgrounds. I had such a hard time thinking about how I would break into data science and how I would make myself a competitive applicant for grad schools because my degree is in something very different. I had worked with data, but it was never anything anywhere near data science. I didn’t have a computer science background or a strong math background, really. But I appreciated that they see how different people’s backgrounds can be useful and can be beneficial.
Ronica: For sure, I agree. I think with boot camp they did really well bringing us all up to the same level so you don’t feel lost in the program and you are confident knowing that you can do this on your own. I think a lot of other universities would just be like, “These are the prerequisites and you need to get these done” and don’t really pay attention to what a person actually knows. With USF’s boot camp, they make sure that we quickly learn the most important data science concepts that we should know.
Favorite Class & Favorite Project
Evie: Everyone I’ve spoken to has said this, but for me it’s data acquisition. I think it’s just because we did really cool stuff and it was really helpful. Even for the distributed computing project there was one piece of data that we needed, and I was able to scrape it from a website in five minutes because of a notebook I had from data acquisition. For my favorite project, I would probably say the article recommendation engine. I thought that being able to launch a server and actually have this finished product was so cool, and I loved being able to use the articles and word vectors. It just felt so complete when I finished the project and I felt really proud of myself when I finished.
Ronica: For my favorite project, I’d say the tweet sentiment analysis project. Again, just completing the whole project from scratch, getting the data, seeing what you need to do, and creating a webpage on a server with the colors of the sentiment, links to the tweets, and how it could be used for any person on Twitter — I thought that was amazing. I would say for my favorite class, I liked linear regression. I had a basic understanding but I had never studied it in this much detail, as in what is really going on behind it. I also enjoyed the machine learning lab. It was really interesting to learn various methods of how to play around with data and getting it into a model — that’s also exciting. This was the stuff I was really excited about before coming into the program, so now getting the chance to learn about it all, it feels like, “Okay, wow — this is what I came for.”
What area of data science are you most interested in?
Ronica: I’ve always envisioned myself working for the customer or the consumer. I would personally really enjoy the domains of e-commerce and retail because those are my personal interests. I really like fashion and shopping, and again, data science is everywhere, so I think that’s something I really envisioned myself doing. Anything to make our lives easier as consumers is what I envision myself doing. Of course, I don’t want to be too fixated on anything because life could always have something else planned out for you, but those are my interests now.
Evie: I’d say I have pretty split interests when it comes to data science. On one hand, I really like the idea of working with data in the context of public policy and creating data-driven policies, which is related to my practicum work. On the other hand, I would really love to create products — I think that’s a big reason that I love projects like the recommendation engine and sentiment analysis. I love having a product at the end that I can point to and know exactly what I did and what I contributed. So yeah, I could see myself working in a setting where I’m helping optimize a product’s recommendation engine or building a better search engine algorithm within a product. For my practicum, I ended up leaning more towards my public policy interest.
Ronica: That’s exciting! I’m interested in how data within public policy works, that’s something I’m not very familiar with. What are the applications of data science with public policy that you’ve learned about so far?
Evie: From my experience so far, it’s a lot of data engineering and database management so that policymakers can easily find the data relating to the issue and population that they are interested in. Well-engineered data and properly maintaining datasets is really important. For example, if that data is coming from the census, we want to make sure that any engineered features are engineered properly.
Practicum
Evie: I’m doing my practicum with a nonprofit organization called California Forward that focuses on economic policy reform in California. Specifically, I’m working on creating a job quality index. A lot of the time, you’ll hear policymakers and politicians talking about how many jobs have been created, but we are interested in knowing if these are high quality jobs; there’s a big difference between creating 100 jobs versus creating 100 good jobs. There are many things that go into what causes a job be considered a high quality job, but right now I’m looking at wages and the respective cost of living for where those jobs are located. The goal is to eventually incorporate this into an existing tool called the California Dream Index. That tool is used by a lot of different policymakers — it currently serves as a big source of information in their work.
Ronica: I’m doing my practicum at First Republic Bank, and my project is basically around bond prospectuses and how we can identify the payment holder of the bonds. We use techniques like NLP for these bond prospectuses because these are really large text files about 200 pages long. To get meaningful information out of that manually is really painstaking, so creating a mechanism that is efficient and accurate is so important.
Personal Interests
Evie: I hang out with my roommates a lot. I have three roommates and they’re three of my best friends, so I feel like we spend a lot of time either hanging out at home or enjoying whatever is around us at the moment. I live really close to Golden Gate Park so there’s a lot of great food and great scenery around us. In general, ever since I was a kid I wanted to live in San Francisco, so now that I’m here, I want to explore as much as I can. I also love finding new music, listening to music, finding new artists, and sharing what I find with other people — that’s definitely one of my favorite things to do.
Ronica: I live right by the Embarcadero. I think it’s a great place to go for a run or a walk, and that’s something I really like doing. Apart from that, I’ve started to cook a lot of food, so I’ve been experimenting a lot with cooking and going out for food, just to hang out. Most weekends are movie weekends for me and my roommates, we’ll get together and binge a bunch of movies. I definitely want to visit more places in and around SF and explore San Francisco a lot more.
