Every week, I get the same DMs: What project should I build? What will impress recruiters?

The truth is, most people pick random projects with no real thought—and it shows.

They often pick projects because they think they're cool, and given they've been done so many times, they are considering it a safe harbor to landing a job in tech.

Sadly for you, things don't work that way. Long gone are the days where a simple pivot table or a Jupyter notebook could land you a job.

If you want to stand out, here’s how to pick a portfolio project that actually moves the needle.

1. Consider the industries that are viable for you and niche down

Think about where you want to work. If finance interests you, build fraud detection models. If you're into healthcare, explore patient risk predictions.

Niching down gives your portfolio focus. Hiring managers want specialists, not generalists drowning in Titanic datasets. The more aligned your projects are with your career goals, the stronger your application.

2. Search Kaggle for inspiration, but don’t get sucked into the Titanic, COVID-19 loop

Kaggle is a goldmine, but don’t just grab the first dataset you see. Avoid cliché datasets like Titanic, COVID-19, and Iris—they won’t impress anyone.

Instead, look for unique datasets that allow for creative analysis.

For instance, someone once analyzed Airbnb pricing trends to predict the best time to book a rental. That’s the kind of originality that stands out, especially if you want to work in the travel and leisure industry.

3. Read research papers

Cutting-edge research can give you fresh ideas for projects. Browse arXiv, JAIR, or Google Scholar for recent breakthroughs in AI, NLP, or computer vision.

Maybe you find a paper on self-supervised learning and decide to implement a simplified version using open-source data.

Even if your model isn’t state-of-the-art, showing you can bridge research and application is a major plus.

4. Choose something that aligns with your interests

You’re going to spend weeks on this project—make sure you actually care about it.

If you love sports, analyze player performance data. If music excites you, build a recommendation system for emerging artists.

Passion shines through in your work. A project you enjoy is one you’ll complete, polish, and proudly showcase.

5. Research your peers and GitHub for inspiration, not copying

Look at top-rated GitHub repos and portfolios of people already working in data science. What makes their projects engaging?

Maybe they created an interactive dashboard or a live API. Instead of copying, take inspiration and add your twist. A stock market predictor?

Great—but what if you make it explainable so users understand why a stock is flagged? That’s unique.

6. Think End-to-End

Data science isn’t just about models. Employers want to see the full pipeline—data collection, cleaning, feature engineering, modeling, and deployment.

Imagine you analyze customer churn and instead of stopping at an accuracy score, you deploy a Streamlit app where users can test different variables. That makes you stand out from 95% of candidates.

7. Solve a Real Business Problem

Don’t just classify cats and dogs—think like a data consultant. Can you reduce delivery times for an e-commerce company?

Help a restaurant chain optimize pricing? If your project answers a question businesses actually care about, it becomes 10x more valuable.

One student analyzed social media sentiment to predict stock trends—practical, relevant, and marketable.

Picking the right project isn’t just about technical skills—it’s about strategy. What industry would you like to work in?

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