Your First Real Data Project Is Allowed to Be Ugly

Proof over polish. My first project was a mess, and it did more for me than any certificate ever did.

My first real data project was ugly.

Not “humble brag, it was fine actually” ugly. I mean the column names were a mess, half my notebook was commented-out code I was too scared to delete, and my big finding was something most people would call obvious. I almost did not publish it. I sat with my cursor over the button longer than I want to admit, certain that someone would look at it and know I did not belong.

I published it anyway. And that ugly little project did more for my career than the stack of certificates I had collected before it.

I want to tell you why, because I think you are sitting on a project of your own right now, talking yourself out of shipping it.

The certificate trap

Here is the thing nobody says out loud when you are starting.

Certificates feel like progress. You finish a course, you get a badge, you feel one step closer. So you collect another one. And another. And the whole time, you are building a resume that says “I can follow instructions” when the job needs to know something else entirely.

Roughly two in three hiring managers say they would rather see portfolio evidence than certifications alone. (That is the gist of recent reporting, citing Burtch Works data, so I will not pretend it is a precise law of nature, but the direction is clear and it has been clear for a while.) The current guidance lands in the same place: a few honest end-to-end projects beat a wall of badges.

A certificate proves you sat through something. A project proves you can do the thing.

What an ugly project actually proves

We tell ourselves the portfolio is there to impress people. It is not, really.

The portfolio’s job is to prove you can take messy, real-world data and do something useful with it. Not perfect data. Messy data. The kind with missing values and weird formatting and a column someone clearly named at 4pm on a Friday.

Your perfect result is not what convinces anyone. Your thinking is. How you noticed the data was dirty, how you decided to handle it, what you did when the first approach did not work. That is the whole game. And here is the quiet truth underneath it: a finished ugly project proves you are a person who ships. That is the thing no certificate can say for you, and it is exactly the thing employers are scanning for.

If this is landing, restack it so it reaches someone who needs it today.

Perfectionism is fear in a nicer outfit

Let me say the part you might not want to hear.

The reason your project is still sitting unpublished is probably not that it is not ready. It is that you are scared. Scared someone will look at your GitHub and tear it apart. Scared “simple” means “not good enough.” Scared that if you ship something imperfect, it confirms what the impostor voice has been whispering.

Perfectionism feels like high standards. For most career changers, it is fear wearing a nicer outfit. And the cruel trick is that polishing forever feels productive while it quietly keeps you exactly where you started, with nothing to show.

The people who break in are not the ones with the prettiest projects. They are the ones who finished one.

So go make something ugly

Here is your assignment, and I mean it.

Pick one dataset this week.

Not the perfect one.

Just one.

Kaggle has free public datasets and beginner-friendly competition data to practice on. You can find it at kaggle.com. Data.gov has hundreds of thousands of genuinely messy, real-world datasets that will force you to do actual cleaning, which is the skill that matters. It is at data.gov. Dataquest keeps a project menu with deliberately messy data if you want a “pick one and ship it” starting point.

Choose one. Ask one question. Clean the data, badly is fine, then better. Write up what you found and what tripped you up. Publish it before it feels done.

Your first project is allowed to be a mess. The only project that hurts your career is the one you never ship.

This is what Inside Data Science is about: honest notes from inside the shift. If you want one in your inbox each week, subscribe below.

P.S. Reply and tell me what you are going to build this week. Send me the link when it is up, ugly and all.

About the author

I didn’t start my career in tech.

For 20 years, I taught high school math. In my 40s, I made the decision to start over and transition into data science.

My first data science interview ended in rejection. The hiring manager asked if I knew BERT. I didn’t. Instead of giving up, I learned what I was missing, followed up with the same hiring manager, and eventually earned the job I was originally rejected from.

That moment changed how I approach this field.

Today I work as a Data Scientist and AI Developer, building applied AI systems and working on real-world machine learning projects.

Along the way, I’ve been recognized as a LinkedIn Top Voice (2024-2025), a LinkedIn Instructor, named one of the Amazing People at LexisNexis, and my transition story has been featured by Udemy and KDnuggets.

I now share the lessons I learned the hard way to help aspiring data scientists, students, and career changers better understand how this field actually works.

If you’re trying to break into data science or understand how the job market really works today, you’ll probably find my newsletter useful.

Inside Data Science with Data Sistah

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https://www.linkedin.com/in/tiffany-teasley/

Data Sistah