Steps to Data Science: The Fear Behind a Red Terminal Feedback

On August 5, 2026, I was officially accepted into Cohort 2 of Dataraflow to dive into Data Science, Machine Learning, and Generative AI. Since that day, it has been an absolute mix of excitement and nervous energy.

We kicked things off with orientation on August 29th, and barely a week into my learning journey, I already had an angry, red terminal staring right back at me.

The primary task this week was setting up our local environments. Everything went smoothly at first, I followed the setup instructions and installed the necessary software without a hitch. But the first wall hit when I tried activating my virtual environment in PowerShell. Out came the bright red text in the terminal.

The error message that had me holding my breathe

My initial instinct was panic; I had no idea what went wrong. But right then, a piece of advice from one of the orientation speakers clicked: read through the error your terminal returns, because that is usually where your solution hides. That is exactly what I did. I slowed down, read the traceback line by line, and realized the fix was right there in the setup overview guide I was following.

With the environment finally running, I stepped into the coursework. I began with the Python crash course modules, learning about core data types, Comparison Operators, if, elif, else Statements, for Loops, while Loops, range(), list comprehension, functions, lambda expressions, map and filter. The initial practice tasks felt manageable with that foundation,

Me feeling like a boss after successfully solving the first few questions in the task

but moving to the assignment section quickly humbled me because the first problem looked entirely like Greek to me.

Instead of freezing up, I pivoted. I skipped the assignment questions and tackled the assessment questions first, solving most of them using residual knowledge and bridging the gaps with research and video walkthroughs on YouTube. Tempting as it was, I made a conscious effort to stay away from generating AI code, not just because we were instructed to, but also shortcuts won’t build real muscle memory.

Once I was done with the assessment, I circled back to the assignment. And genuinely it felt like taking a breath made all the difference: I solved the first two questions with minimal guidance, and for the remaining three, I leaned into cohort discussions and deeper documentation dives.

By the end of week one, I had built small, functional scripts that is including a program that handles items in a shopping cart with their prices and an interactive student directory that accepts, stores and displays user inputs.

Importantly, I learned to demystify the red text in the terminal. Because apparently, it is not a failure signal but a compass pointing towards the fix.

This is just the beginning, but if week one impacted something in me, it is captured best by a line I noted during orientation:

“Start before you are ready, every single time. That’s the job.”