Here are the best platforms and tools for doing hands-on data-science projects, grouped by purpose so you can pick what fits your goals (learning, competitions, compute, datasets, portfolio, or deployment).

Learning & guided projects

• Kaggle Learn / Kaggle Projects — short, practical micro-courses and end-to-end kernels; great for reproducible notebooks and building a public portfolio.
• DataCamp Projects — guided, interactive projects with automated feedback (paid tiers for more content).
• Coursera / edX capstone projects — structured, course-linked projects that often include peer review and certificates.
• IBM Skills Network / Microsoft Learn labs — hands-on labs focused on real tools and cloud services.

Competitions & real-world problems

• Kaggle Competitions — biggest community, range from beginner to expert, excellent for model-building and reproducible notebooks.
• DrivenData — social-impact oriented problems (public health, policy, conservation).
• Zindi — Africa-focused problems, varied difficulty and good for diverse, real-world datasets.
• AICrowd — research-oriented, RL/ML challenges and reproducible evaluation pipelines.

Compute & interactive notebooks

• Google Colab — free (with paid Pro tiers) GPU/TPU access; great for quick experiments and sharing notebooks.
• Kaggle Notebooks — integrated with Kaggle datasets and competitions; free GPU.
• Binder — turn Git repos into shareable executable notebooks (good for demos).
• Paperspace Gradient / Saturn Cloud / Gradient Notebooks — managed cloud notebooks with more compute options (paid).

Datasets & data sources

• Kaggle Datasets — thousands of user-uploaded datasets with descriptions and starter notebooks.
• UCI Machine Learning Repository — classic curated datasets for experimentation.
• Google Dataset Search — search engine for datasets across the web.
• AWS Open Data / Google Cloud Public Datasets — large-scale, cloud-hosted datasets (satellite imagery, genomics, etc.).

Code hosting, collaboration & versioning

• GitHub (with GitHub Actions) — essential for version control, issue tracking, and CI/CD for data workflows.
• GitLab — similar to GitHub with integrated CI.
• DVC (Data Version Control) — manage dataset and model versioning alongside Git.

Model serving & building interactive apps (portfolio)

• Streamlit — easiest way to build interactive data apps and dashboards from Python scripts; great for portfolio demos.
• Hugging Face Spaces — host small ML demos (Streamlit or Gradio) for free on Hugging Face.
• Dash (Plotly) — production-grade interactive dashboards.
• FastAPI / Flask — for building APIs around models for deployment.

End-to-end MLOps / managed platforms

• MLflow — experiment tracking and model registry.
• Weights & Biases — experiment tracking, dataset and model monitoring (popular in industry).
• Vertex AI / AWS SageMaker / Azure ML — cloud-managed pipelines, training, and deployment for production workloads.

Practice & interview prep (algorithms, pipelines)

• Kaggle (for kernels + discussions) and HackerRank/LeetCode (for coding & algorithm practice) — combine to cover both ML and software skills.
• Project-based curricula (Udacity Nanodegrees, DataCamp) — useful for filling gaps in MLOps, deployment, and full-stack skills.

Where to start (recommended path)

1. Do a few guided projects (Kaggle Learn, DataCamp) to learn core tools (pandas, scikit-learn, basic modeling).
2. Reproduce a Kaggle notebook from a competition or public dataset and add your own twist.
3. Host code and notebooks on GitHub and add a README + short project write-up.
4. Build a small interactive demo with Streamlit or Gradio and host it on Hugging Face Spaces or GitHub Pages.
5. Optionally enter a beginner-friendly Kaggle/DrivenData/Zindi challenge to practice deadlines, feature engineering, and collaboration.
6. Learn basic MLOps: use DVC or W&B for tracking and practice deploying a small model with FastAPI or Streamlit.

If you want, I can:

• Recommend 3 project ideas and the best platforms/tools for each, or
• Pick platforms tailored to your current level (beginner / intermediate / advanced) and career goals.