What I Wish I Knew Before My First Data Science Internship
Lessons that go beyond code, algorithms, and Jupyter notebooks
Starting my first data science internship was exciting—and nerve-wracking. I had just enough Python experience, had completed a few personal projects, and watched enough YouTube tutorials to feel like I kind of knew what I was doing. But once I actually stepped into the internship (virtually, in my case), reality hit.
It wasn’t just about writing perfect code or building flashy models. There were soft skills, real-world constraints, messy data, and unexpected challenges that I simply hadn’t prepared for. Looking back, here are some things I really wish someone had told me before I started.
1. You’re Not Expected to Know Everything
Let’s start here. The imposter syndrome? Very real. The first week, I found myself constantly wondering if I belonged on the team. Everyone seemed smarter, faster, and more confident.
But here’s the truth: internships are meant for learning. No one expected me to be a machine learning expert on day one. My job was to be curious, ask questions, and show a willingness to learn. Once I stopped pretending to “know it all,” I actually started to learn much more effectively.
2. Business Context Matters More Than You Think
In school or Kaggle competitions, you usually get a clean problem: “predict X from Y.” But in the real world? Not so much. I was given a vague question like “can we identify users likely to churn?” and I had to frame it as a problem before even thinking about modeling it.
Understanding the business context—who the stakeholders are, why this problem matters, and what decisions will be made based on your analysis—is key. Data science doesn’t exist in a vacuum. It's not just about model accuracy; it's about solving the right problem.
3. Cleaning Data Will Take More Time Than You Want to Admit
Everyone says it, but you don’t really believe it until you’ve done it: most of your time will be spent cleaning and wrangling data. My first dataset had missing values, inconsistent naming conventions, weird outliers, and columns that made no sense.
I spent days just figuring out what each column meant and whether it was reliable. I learned how to merge datasets carefully, write reusable preprocessing functions, and keep a log of all the cleaning decisions I made—because yes, you will forget why you dropped that column two weeks later.
4. Communication Is a Superpower
You can build the best model in the world, but if you can’t explain what it does and why it matters, it won’t go anywhere.
One of the biggest lessons I learned was how to communicate with both technical and non-technical audiences. I had to give regular updates, write summary reports, and explain trade-offs. I learned to avoid jargon, use visuals to tell stories, and focus on the “so what?”—what does this mean for the business?
Data science is 50% technical, 50% communication. Maybe even more.
5. Version Control Is Your Friend
I didn’t know how to use Git properly before my internship. I do now.
Working on a team means you’re not just coding for yourself—you’re writing code that others may review, use, or build upon. I learned how to push code to a repository, create branches, write meaningful commit messages, and resolve merge conflicts (the hard way).
Don’t skip Git. It’s not glamorous, but it’s essential.
6. It’s Okay to Google Things
At first, I felt guilty about Googling error messages or Stack Overflow-ing every little thing. But guess what? Even senior data scientists do it.
Learning how to debug your code, search for documentation, and find helpful resources is part of the job. You’re not failing—you’re just being resourceful. Just make sure you understand what you're copying and pasting.
7. Ask Questions, Even If They Feel “Stupid”
This one took me a while. I used to sit on questions for hours, trying to figure things out alone. I didn’t want to look unprepared.
But asking early—politely and clearly—often saved me hours of frustration. More importantly, it showed that I was engaged and eager to learn. Internships aren’t tests; they’re opportunities. The smartest interns aren’t the ones who know everything—they’re the ones who know how to ask the right questions.
8. Documentation Is Gold
Halfway through my internship, I realized I couldn’t remember how some of my scripts worked—or why I chose certain parameters. That’s when I started documenting everything: what each function did, what assumptions I made, and even little decisions like why I filtered data a certain way.
Good documentation isn’t just for others. It’s for you, too. Your future self will thank you.
9. Be Comfortable With Uncertainty
Unlike textbook problems, real-world data science is messy and ambiguous. You often won’t have a “correct” answer. You’ll try different models, tweak features, and still feel unsure if you’re doing it “right.”
And that’s okay. Learning to be comfortable with uncertainty—and to make decisions based on limited information—is part of becoming a real data scientist.
10. Your Growth Happens Outside Your Comfort Zone
There were days I felt lost, days I messed up, and days I questioned if I belonged. But those were also the days I learned the most.
I learned how to speak up in meetings, write code that others could understand, admit when I was stuck, and push through challenges. Growth isn’t always comfortable, but it’s always worth it.
If you're about to start your first data science internship, take a breath. It’s okay to be nervous. You’ll make mistakes. You’ll feel overwhelmed. You’ll learn a lot.
Don’t put pressure on yourself to be perfect. Focus on learning, communicating, and growing a little each day. And most importantly—be kind to yourself. You're not expected to be an expert yet. You're expected to be a learner.
That’s exactly what you’re there for.