ujjwalkarn/datasciencepython

This repository is a curated collection of links to tutorials, articles, courses, and reference materials for learning data science and machine learning using Python. There is no runnable code in the repository itself. It is an organized reading list pointing outward to other resources on the web. The list is organized by topic. It starts with Python language fundamentals, pointing to beginner guides, style references, and common questions from Stack Overflow. It then moves into data science topics, covering libraries used frequently in that field: Pandas for working with tables of data, NumPy and SciPy for numerical calculations, Matplotlib and Seaborn for making charts, and scikit-learn for building machine learning models. There are sections on natural language processing (teaching computers to work with text), neural networks, web scraping (pulling data from websites automatically), and SQL databases. Each section is a list of links, usually with short descriptions, pointing to blog posts, video series, Jupyter notebooks, and online courses from places like MIT, Coursera, and DataCamp. The repository also links to a companion list for people who prefer to do similar work in the R programming language, and to a broader machine learning tutorials list maintained by the same author. Someone completely new to data science could use this repository as a map, working through the linked materials roughly in order, from the Python language basics through to building and evaluating machine learning models. The README is the entire content of the repository. There are no scripts, notebooks, or packages included here, just a structured index of external learning material.