Interviewer: Akanksha Agarwal
I’m Franz, currently a sophomore studying at the Ateneo de Manila University in the Philippines. It has been enjoyable immersing myself in the wide variety of organizations my university provides. Moreover, it’s been exciting to find prospective companies to join, whilst still studying. I’m currently studying computer engineering, but I am trying to shift to management information systems. So it’s a lot of business and data because that’s where I found myself, and it’s the path that I’m going towards. I’m really loving it!
I have developed my passion for Data Science ever since high school because my mom is one of the leaders in tech for women in our country, which I’m really proud of, so I was exposed to the field early on. However, upon going into the course, it wasn’t for me. That’s why I went into a more software engineering kind of start. I have a background in software engineering and web and mobile apps and found out about data science in this tech startup in the Philippines called ‘Eskwelabs‘. Then, I went to study there under their scholarship, and that’s where I trained as a data scientist. Ever since I have been trying to find opportunities to share my learnings.
What setbacks did you face in your Data Science journey?
My journey was not free of setbacks, and the first setback I faced was the sheer expansiveness of the field, which was overwhelming at the start. At first glance, it seemed so simple, but in reality, it was very deep. That was a bit overwhelming at the start, but Eskwelabs really helped me in that regard. Another setback I faced would be the online environment. As a person who really enjoys being in a classroom setting and interacting with people, making the transition to passive, virtual communication was difficult and exhausting. I went through my fair share of bad days, or slow days.
How have you approached tackling the complex challenges of today?
To get a really big problem and try to break it down, you have to define what you want to answer first. I’d always scrutinize a problem statement. It always starts with the problem statement and then gets it to as small as possible. Then asking, what do I really need right now? Thanks to prep, I also have practice and agile methodologies. That helped me put things into perspective and see my progress, and feel I’m getting closer to my goal. Moreover, helping me get closer to solving that problem and then trying to share it with others. It’s going big-picture and then trying to define the scope and the goals of a certain project.
Where do you see yourself in the next ten years?
In the short term, I really want to practice my skills and then expand my reach as well as just master what I’m trying to learn right now. That’s with the goal of trying to create an impact. Specifically, helping people, trying to reach people’s hearts. In five years, I really see myself as a consultant. I love being able to give insights into other problems and helping people with their problems as well. Then given what I know, I also see myself maybe making my own startup. This goal of it is very hard, but that’s something that I really am really gearing towards right now. In 10 years, at the end of my career, I want to see myself really educating others. I always told myself that the last step in my career, I want to be a professor. If then if they use that as well, especially show the world that you can create an impact even as early as sophomore year.
What is the youth spread like in Data Science in the Philippines?
There there are a lot of groups right now, at least that I’ve seen around the Philippines. We have Facebook groups there, even a Slack group available. A lot of organizations right now are very active, even in my university. Many people have been asking me for advice on mentorship. Where do I get started? There are a lot of people who are very interested in this field, but the common thing that I see is that they’re scared of the buzzwords. So I can definitely say that I’ve seen a lot of people, very excited about data science and machine learning all around from university to outside organizations. LinkedIn was really a big factor as well.
What would you advise youth interested in AI, Machine Learning or Data Science?
I’d say that it’s not as scary as it seems, it’s become this buzzword because of whatever we see in games that AI is a bit scary, but as long as you have a firm grasp or as long as you really build on your foundations, it really isn’t scary at all, and it’s very fun. There’s also this preconception that it’s heavy on mathematics, but it’s a great foundation, and it’s also being able to connect with a humanitarian side, really understanding the problem. What are you trying to solve, finding your own path? So the best advice I’d give is to just go for it and explore it to try it as early as possible because data science will be everywhere in a few years.
Can you share your experience with Omdena?
Omdena has been amazing. I’ve learned about it through one of my mentors, one of our instructors in Eskwelabs. I’ve heard a lot of good things before going into it, and then to see that we’re able to work with such big projects that have so much impact and then to be able to meet and connect with like a lot of people that are so big already, they’re so talented and they’re so smart and indigenous they’re like, they’re so willing to guide you and help out. That part is already so amazing and something that’s been inspiring me to keep moving forward. Then the process that is initiated is so interesting. You really won’t see it anywhere else. In my opinion, it’s really about growth and initiative. Learn more about Omdena Projects here.