In the next project for my company, I wanted to learn about Astrophysics. I always found The Universe to be fascinating. I wanted to know more about the cosmos. I decided this was the perfect moment to start exploring DIY Astronomical Data Science.

The DIY Astronomical Data Science approach truly excites me. From the stars that burn in our galactic neighborhood to the distant reaches of the Milky Way.

I asked myself “What would an astrophysicist do in a typical day?” and I wanted to do that. Obviously, I wouldn’t be able to do quite what they do. I am an independent researcher with no knowledge or skills in math, Python, or how our Solar System really works.

I had my work cut out for me. Still, I wanted the experience of “if I went to work with an Astrophysicist what would I see?” In reality, DIY Astronomical Data Science can offer a unique perspective on learning these skills without formal training.

It was at that moment, I had an idea. I wanted to create a map data of the stars so I would be able to detect anomalies. This way, I also could see what an Astrophysicist would look at when they use Python for data.

The First Query: Navigating Raw XML to Map the Stars

I didn’t just download a spreadsheet; I accessed a VOTable. The international standard for astronomical data.

It is an XML-based map of the Milky Way containing not just the location of these stars, but their temperatures, distances, and velocities. Essentially, I was holding a piece of the Gaia mission’s census of the galaxy right on my laptop.

But, in the spirit of my research here is a fair warning to prove I’m still a rookie: I didn’t think to save my full Python journey yesterday and deleted the workspace after I was finished.

Even when I did have it, I’d been deleting all my errors to keep things tidy. I realized that if I had shared that version, it would have looked too squeaky clean. Almost like I’d just copy-pasted the code rather than doing the work myself.

Lesson learned: documenting the messy process is just as important as the result. From now on, I’m making it part of my mission to show the thought process behind every conclusion.

This way, you can see exactly how I got there. Additionally, DIY Astronomical Data Science has made me appreciate recording every step.

Another important lesson I learned is the how and why behind math. I always hated math and was never good at it; I never realized why until I started this project.

I always thought I had to put the math first and the answers second. I used to think, ‘Oh great, I have to figure out how to do algebra just to get a passing grade or solve for X.’ But that was the wrong way to go about it.

For astrophysics, they have missions, questions, and inquiries. They want to solve a puzzle. The math is simply a way of providing the proof for their answers; it is the how and why they use to address those deeper questions.

When you are excited to learn how a planet spins or how much further it may drift in the next 100 years, the math becomes fun. This is because you want to prove that you figured out what might happen to it in those next 100 years!

Debugging The Universe: Why My Mistakes Were My Best Teachers

While it may be hard to believe I went from absolutely zero coding knowledge to creating a data map of the stars in a week, it certainly was not without many hours of banging my head against a wall.

The most important lesson I learned is that I never gave up. I would take small breaks and come back later, constantly reminding myself that this was for something I genuinely loved. I kept coming back to improve the task at hand.

I even created my own menu labels to track what each number in the charts represented. I didn’t even know what Kelvin was when I started!

That should prove I’m not pretending to be a know-it-all here. However, I take this work seriously; it is more than just a hobby. Out of respect for the field, I feel it is my job to research what a glimpse of this work actually looks like.

Professionals do much more than this, of course; I have simply completed one step of the many that a real astrophysicist performs. My goal is to show the world how admirable their work is and to keep growing my knowledge of the field.

This journey is only the beginning, and I look forward to learning more visual algebra and calculus! As a final thought, DIY Astronomical Data Science can inspire anyone to start exploring the cosmos on their own terms.

Field Research Disclaimer: Content is for entertainment and editorial purposes only. I am not a medical, legal, or professional advisor. Photography is captured via handheld, minimal equipment for independent research in public or authorized spaces—no identifiable subjects are featured. Do not attempt these observations; consult a professional for safety. Findings are independent observations of Comfy Chaos Collective.