Every month, I look at dozens of data science and machine learning resumes, and I keep seeing the same projects over and over. The datasets most people use are great when you’re just starting out, but they won’t help you get hired. They’re too simple, too small, and don’t reflect real-world AI challenges. In this article, I’ll share five of the best free datasets you can use to build real, impressive AI projects.
Free Datasets for Real AI Projects
Here are five powerful, industry-level datasets you can use to create impressive AI projects for your resume.
1. The Stack Dataset
If you’ve ever wondered what powers coding assistants like GitHub Copilot, it’s datasets like this one. The Stack is huge, with over 6 terabytes of open-source code in hundreds of programming languages. The BigCode project created it to offer open and transparent training data for Large Language Models (LLMs).
Unlike most NLP datasets that use everyday English, source code is very structured, follows strict rules, and depends heavily on context, such as variable definitions that might be in different files.
Don’t try to train a large language model from scratch, since that usually requires a big budget. Instead, use a smaller part of the dataset, like just the Python and Rust code. You can fine-tune a smaller open-source model, such as Llama 3 or Mistral, to translate code between languages or to create a smart code-comment generator.
2. OpenAlex
OpenAlex is an open catalog of the global research system. It replaces the now-retired Microsoft Academic Graph and contains hundreds of millions of interconnected entities like scientific papers, authors, institutions, and citations.
OpenAlex is basically a huge Knowledge Graph. Rather than just showing separate rows in a CSV file, it connects different pieces of information to show how they relate.
With this dataset, you can build a smart recommendation engine. For example, you can use the abstracts to create document embeddings with sentence-transformers, then combine them with the citation graph to suggest papers to users based on what they’re reading.
3. LAION-5B
LAION-5B is a dataset that changed the world of generative AI. It has 5.85 billion image-text pairs, which means billions of internet images matched with their alt-text captions. This kind of data is what’s used to train text-to-image models like Stable Diffusion.
LAION was created to make research on large multimodal models more accessible. But since it’s collected from the open web, the data can be messy, with broken links, mislabeled images, and different image qualities.
Try downloading a small part of the dataset and build a reverse image search engine. Use a pre-trained CLIP model to get image embeddings, store them efficiently, and create a script so users can upload a picture and quickly find similar images in your dataset.
4. MIMIC-IV
MIMIC-IV is a big, free database with de-identified health data from thousands of ICU admissions. It includes information like patient demographics, vital signs, lab results, medications, and more.
Since healthcare data is sensitive, you can’t just download MIMIC-IV right away. You’ll need to take a free course on human research ethics through PhysioNet before you can access it.
With this dataset, you could build an early-warning model. For example, you can use patient vitals and lab results from their first 24 hours in the ICU to predict the risk of complications like sepsis.
5. NYC Taxi Dataset
The NYC Taxi and Limousine Commission (TLC) maintains this dataset, which has records of millions of taxi trips in New York City going back more than ten years. It includes pickup and drop-off times, locations, trip distances, and fare details.
Even though it might seem simpler than the other datasets, its huge size makes it a great way to practice Data Engineering and Machine Learning Operations (MLOps).
Instead of just building a model in Google Colab, try creating a full automated pipeline. Use Apache Airflow to download the data each month, process it with Polars or Dask, train an XGBoost model to predict busy taxi zones, and deploy your model with FastAPI.
Closing Thoughts
Those are my top five industry-level datasets you can use to build impressive AI projects for your resume.
Choose one of these datasets and spend a month really exploring it. Gaining deep understanding from a single, complex project will help your career much more than doing lots of basic tutorials.
If you found this article helpful, you can follow me on Instagram for daily AI tips and practical resources. You may also be interested in my latest book, Hands-On GenAI, LLMs & AI Agents, a step-by-step guide to prepare you for careers in today’s AI industry.
