GitHub Mistakes That Make Data Analysts Look Amateur

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Your GitHub profile is a first impression. And you might be blowing it.

I've reviewed hundreds of data analyst portfolios. The same mistakes appear over and over. Profiles that could showcase genuine skill instead scream "beginner who doesn't know better."

The frustrating part? These are easy fixes. Yet most analysts never realize they're making these errors because nobody tells them.

Consider this your wake-up call.

Mistake #1: The Graveyard of Abandoned Projects

Your profile shows 23 repositories. Impressive? Not when 20 of them are half-finished.

Abandoned projects tell a story—just not the one you want. They suggest:

• You start things but don't finish them
• Your judgment about what's worth pursuing is poor
• You lack the discipline to see work through

The fix: Make repositories private or delete them. Keep only projects you'd be proud to discuss in an interview. Five polished repos beat fifty embarrassing ones.

Mistake #2: README Files That Say Nothing

The README is your project's front door. Most data analyst READMEs look like this:

# Project 1

Analysis of sales data.

That's it. No context.

No methodology. No results. No evidence that anything valuable exists inside.

Would you walk through that door? Neither would a hiring manager.

The fix: Every README needs:

• What problem you solved
• What data you used
• What techniques you applied
• What you found (with visuals!)
• How to run the code

If writing READMEs feels tedious, you're thinking about it wrong. The README is the project. The code is just the supporting evidence.

Mistake #3: Notebook Crimes

Jupyter notebooks are notorious for revealing amateur habits.

Crime 1: Execution disorder. Cells numbered [1], [7], [3], [15]. You ran things out of order, didn't restart, and committed the chaos.

Crime 2: No narrative. Sixty code cells with zero markdown. No explanations, no section headers, no interpretation of results.

Crime 3: Debug leftovers. print(df.head()) appearing seventeen times. Commented-out experiments that "might be useful later."

Crime 4: Missing outputs. You cleared the outputs and forgot to re-run before committing. Now viewers see code that produces nothing.

The fix: Before any commit, do Kernel → Restart & Run All. If it doesn't execute cleanly top-to-bottom, it's not ready to publish.

Mistake #4: Repository Names That Mean Nothing

I've seen:

• project1
• Untitled
• analysis_final_v2_FINAL
• asdf
• test123

These names communicate nothing except that you didn't care enough to think for five seconds.

The fix: Descriptive, lowercase, hyphenated names. customer-churn-analysis. sales-forecasting-model. covid-dashboard-project. Names that tell visitors what they'll find.

Mistake #5: Committing Credentials

This one can actually hurt you.

API keys. Database passwords.

AWS credentials. Personal tokens. I've seen all of these in public repositories.

"But I removed it in the next commit!"

Doesn't matter. Git remembers everything. Anyone who clones your repo can dig through history and find that deleted secret. Bots actively scan GitHub for exposed credentials.

The fix:

• Use environment variables
• Add .env to .gitignore
• Use git-secrets or similar tools
• If you've ever committed a credential, rotate it immediately

Mistake #6: One Giant Commit

Your entire project uploaded in a single commit: "Initial commit" or worse, "."

This looks like you built everything locally and uploaded it at the last minute. Maybe for a deadline. Maybe because you just learned Git exists.

What it doesn't look like: professional development practices.

The fix: Commit incrementally as you work. Even on personal projects, practicing good commit hygiene builds habits that matter in collaborative environments.

Mistake #7: No .gitignore

Your repository contains:

• .ipynb_checkpoints/
• __pycache__/
• .DS_Store
• Thumbs.db
• venv/ (3,000 files)
• data/huge_file.csv (500MB)

These shouldn't exist in your repo. They pollute the project and make you look careless.

The fix: Add a proper .gitignore before your first commit. GitHub offers templates for Python projects. Use them.

Mistake #8: Copy-Paste Course Projects

Tutorials are for learning. They're not portfolio pieces.

When your repo is obviously copied from a Coursera course or YouTube tutorial—same dataset, same analysis, same variable names—it shows nothing except that you can follow instructions.

Hiring managers have seen these projects before. Many times.

The fix: If you want to showcase skills learned from a course, apply them to different data. Add original analysis. Extend the project beyond what was taught. Make it yours.

Mistake #9: The Empty Profile

No bio. No profile picture (or worse, the default identicon). No pinned repositories. No README profile.

You're asking people to invest time exploring your work while investing none in presenting yourself.

The fix: Five minutes of effort:

• Add a real photo
• Write a one-line bio
• Pin your best repositories
• Create a profile README

This is the minimum viable professionalism.

Mistake #10: Data Without Context

You've uploaded a dataset and some code. But:

• Where did the data come from?
• What do the columns mean?
• What question were you trying to answer?
• What did you conclude?

Without context, your analysis is just code that runs. It doesn't demonstrate thinking.

The fix: Document your data sources. Explain your methodology. Interpret your results. Show that you understand the "why," not just the "how."

Mistake #11: Ignoring the Contribution Graph

Your profile shows a contribution graph—those green squares indicating activity. And yours is almost entirely white.

Empty graphs suggest:

• You don't code regularly
• You don't maintain your projects
• You created everything in a burst and then abandoned it

The fix: Commit regularly, even small updates. Fix a typo. Update a README. Improve documentation. Activity matters psychologically, even if the changes are minor.

Mistake #12: Not Testing Your Own Repos

Have you ever cloned your own repository to a fresh environment and tried to run it?

Most analysts haven't. Which is why their repos are full of:

• Missing dependencies
• Hardcoded paths that only work on their machine
• Instructions that don't actually work
• Broken links to data files

The fix: Clone it. Follow your own instructions. If you can't run your project, neither can anyone else.

The Meta-Mistake: Treating GitHub as Storage

The underlying error behind all these mistakes is thinking of GitHub as a place to store code.

GitHub is a place to present work.

Storage is private. Presentation is public. The standards are different.

When you push to a public repository, you're publishing. You're saying "this represents me professionally." Treat it with that seriousness.

A Quick Audit

Open your GitHub profile right now. Check:

• [ ] Profile photo is professional
• [ ] Bio exists and describes what you do
• [ ] At least 3 repositories are pinned
• [ ] Pinned repos have meaningful names
• [ ] Each repo has a substantive README
• [ ] No exposed credentials in history
• [ ] Notebooks execute cleanly top-to-bottom
• [ ] .gitignore excludes junk files
• [ ] Contribution graph shows some activity

Failed any of these? You now know what to fix.

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Frequently Asked Questions

Should I delete old bad projects or fix them?
Depends on effort required. Quick fixes are worth it. Major rewrites—just delete and start fresh.

How many repositories should I have public?
Quality over quantity. Five excellent repos are better than twenty mediocre ones.

What if my best work is at my job and proprietary?
Create similar analyses with public data. Demonstrate the same skills without revealing proprietary information.

Is it too late to fix my profile?
Never. GitHub history matters less than current presentation. Clean it up today.

Should I delete embarrassing old commits?
Rewriting history is complex and can break things. Usually easier to archive the repo and create a fresh one.

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Conclusion

Every mistake on this list is fixable in a weekend. Most take minutes.

The question is whether you care enough to fix them.

Your GitHub profile is working for or against you 24/7. Recruiters see it. Hiring managers check it. Future colleagues browse it.

Make it work for you.

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This article was refined with the help of AI tools to improve clarity and readability.