What is this?
A free, open-source initiative that mentors anyone into Data Science: instead of drowning in a chaotic jungle of tutorials, you follow structured learning paths it has put together.
A free, open-source initiative that mentors anyone into Data Science: instead of drowning in a chaotic jungle of tutorials, you follow structured learning paths it has put together.Is this one course, or a set of separate guides?A set of organized study paths rather than a single course — the paths span Data Science topics like Python, statistics, machine learning, NLP, computer vision and business intelligence.
A set of organized study paths rather than a single course — the paths span Data Science topics like Python, statistics, machine learning, NLP, computer vision and business intelligence.How do I use it — do I need to install anything?Nothing to install: it is a reading guide, so you open the web version linked in the README and follow the paths in your browser. It is completely free.
Nothing to install: it is a reading guide, so you open the web version linked in the README and follow the paths in your browser. It is completely free.Is it just a pile of Jupyter notebooks to click through?No — it explicitly calls out the false confidence of just clicking Run on other people's notebooks, and instead pushes you out of that comfort zone to actually learn the material.
No — it explicitly calls out the false confidence of just clicking Run on other people's notebooks, and instead pushes you out of that comfort zone to actually learn the material.Where should a beginner start?With the fundamentals paths like Python and statistics, then move on to applied topics — the whole point is helping anyone get self-started as a Data Science practitioner.
With the fundamentals paths like Python and statistics, then move on to applied topics — the whole point is helping anyone get self-started as a Data Science practitioner.Can I reuse the material for my own course, or commercially?Content is under Creative Commons BY-NC-SA 4.0: you can share and adapt it with attribution, but not commercially. The project's code itself is MIT.
Content is under Creative Commons BY-NC-SA 4.0: you can share and adapt it with attribution, but not commercially. The project's code itself is MIT.What tools and topics do the paths cover?Alongside fundamentals like Python and statistics, the topics reach into machine learning with scikit-learn and TensorFlow, NLP, computer vision and business intelligence.
Alongside fundamentals like Python and statistics, the topics reach into machine learning with scikit-learn and TensorFlow, NLP, computer vision and business intelligence.Why does the README start with a GenAI framework promo?That banner is the author announcing their own separate project — it is not part of Virgilio, which remains a Data Science learning guide.
That banner is the author announcing their own separate project — it is not part of Virgilio, which remains a Data Science learning guide.What are similar projects?ailearning, 100-Days-Of-ML-Code, Ai-Learn, mlcourse.ai, PythonDataScienceHandbook, machine-learning-zoomcamp, data-science-ipython-notebooks, cheatsheets-ai
ailearning, 100-Days-Of-ML-Code, Ai-Learn, mlcourse.ai, PythonDataScienceHandbook, machine-learning-zoomcamp, data-science-ipython-notebooks, cheatsheets-aiWhich topics is it in?datascience, guidelines, business-intelligence, study, science, scikit-learn, pandas, statistics, data-analysis, numpy, tensorflow, jupyter notebook
datascience, guidelines, business-intelligence, study, science, scikit-learn, pandas, statistics, data-analysis, numpy, tensorflow, jupyter notebook