If I could go back and sit down with my freshman self, I don’t think I’d bring a checklist or a master plan. I think I’d just try to reframe how I was looking at everything. Because the problem was never that I wasn’t capable, it was that I thought I was supposed to already be.
I’ve thought about that time more than I probably should, not in a regretful way, but in a “I really had no idea what I was doing” kind of way. Starting out in a data-related field felt overwhelming in a very specific sense. It wasn’t just that things were hard, it was that they were unclear. You’re handed tools, languages, datasets, and expectations, and it somehow feels like you’re supposed to already understand all of it. Like you should know how to code, how to think analytically, how to ask the “right” questions, and already have a direction.
I didn’t. And if I’m being honest, most other people didn’t either. They were just better at hiding it.
If I could say one thing to that version of myself, it would be this: not knowing what you’re doing at the beginning isn’t a weakness, it’s the entire point. Data as a field is built on uncertainty. You’re constantly working with incomplete information, messy datasets, and questions that aren’t fully formed yet. Feeling unsure doesn’t mean you’re behind. It means you’re actually engaging with the work the way you’re supposed to.
If anything, I wish I had leaned into that uncertainty more instead of trying to avoid it.
One of the biggest shifts for me came from something a professor once said: he assumed you weren’t really thinking until you asked a question, because asking a question proved that you were. That stuck with me. I had spent so much time trying to look like I understood everything that I avoided doing the one thing that would have actually helped me understand anything.
In a data-driven field, questions are everything. Before any code runs, before any dashboard gets built, before any “insight” exists, there’s a question. And if that question is vague, rushed, or unexamined, everything built on top of it will be too. Learning to ask better questions isn’t just a skill, it’s the foundation of doing meaningful work.
And those questions don’t need to be perfect, they just need to be honest. Why does this look different than I expected? What am I missing here? Does this actually mean what I think it means?
That kind of thinking is what actually moves you forward. Over time, you realize that the people who grow the most aren’t the ones who always have answers, they’re the ones willing to dig deeper.
I’d also tell myself to start asking those questions sooner and out loud. I used to hesitate because I thought asking for help would make me look unprepared. But in reality, it’s the opposite. The world isn’t sitting there waiting for you to fail. It actually wants to help you figure things out. Professors, classmates, coworkers, even people online are usually more than willing to give you a hand. But you have to meet them halfway. You have to be willing to say, “I don’t get this yet.”
That shift, from trying to prove yourself to trying to learn, changes everything. I’d also remind myself that I didn’t have to figure everything out alone. For a long time, I treated independence like it was the goal, like needing help meant I wasn’t capable. But this field is collaborative by nature. People share ideas, troubleshoot together, and build off each other’s thinking. Letting yourself be part of that process doesn’t make you weaker, it makes you better.
Another thing I wish I understood earlier is that you don’t have to be completely consumed by something to be good at it. There’s this idea that you need to “live and breathe” data to belong in the space, and I don’t think that’s true. I used to think I needed to find that one thing I was endlessly passionate about. When I didn’t feel that way, I questioned whether I was in the right place.
But being good at something doesn’t always start with passion. A lot of the time, it starts with consistency. With showing up, putting in the effort, getting a little better each time, and slowly building confidence through competence. You can grow into something. You can learn to enjoy it because you understand it, not just the other way around.
At the same time, striving to improve doesn’t mean becoming someone else in the process. You can push for better: better skills, better understanding, better work, while still staying grounded in who you are. That balance matters, especially in a field that can feel technical and rigid.
For me, staying grounded also means bringing my relationship with God into what I do. Not as something separate from school or work, but as something that actively shapes how I think and grow. When I trust that He can guide my mind, open my thoughts, and support me in what I’m working toward, I approach challenges differently. It doesn’t replace effort, but it gives that effort direction and purpose.
And through all of this, I think the simplest thing I’d say, the one that took the longest to fully understand, is to just be myself. Not a version of myself that fits what I think the field expects, not a version that blends in better, but the one that actually thinks the way I think.
Because in the end, data isn’t just about numbers, it’s about interpretation. It’s about perspective. The way you approach problems, the way you ask questions, the way you connect ideas. That’s where your value is.
Allowing yourself to be yourself doesn’t mean you settle where you are. It just means your growth is actually yours. It’s not forced, it’s not performative, and it’s not based on who you think you’re supposed to be.
Looking back, I wouldn’t hand my freshman self a perfect plan. I’d give a little more clarity instead: you’re allowed to not know, you’re expected to ask, you’re not doing this alone, and you’re capable of growing into this.
So keep asking, keep trying, and stay grounded in what matters to you. You don’t need to have everything figured out right now, you just need to keep moving forward and trust that clarity comes with time.