{"id":13665639,"url":"https://github.com/rebecca-vickery/data-science-learning-resources","last_synced_at":"2025-04-26T08:32:32.653Z","repository":{"id":218059887,"uuid":"268092562","full_name":"rebecca-vickery/data-science-learning-resources","owner":"rebecca-vickery","description":"A comprehensive list of free resources for learning data 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Data Science Learning Resources\n\nA comprehensive list of free resources for learning data science.\n\n## Python\n\n### Courses/Tutorials\n\n* **[Datacamp](https://learn.datacamp.com/courses)** selected free courses. \n* **[Dataquest](https://www.dataquest.io/)** free trial. \n* A really good **[tutorial on OOP for data science](https://opendatascience.com/an-introduction-to-object-oriented-data-science-in-python/)**.\n* **[CS50x Harvard Introduction to Computer Science](https://cs50.harvard.edu/x/2020/)**.\n* **[https://realpython.com/](https://cs50.harvard.edu/x/2020/)**.\n* **[Pandas basics](https://pandasguide.readthedocs.io/en/latest/Pandas/basic.html)**.\n\n### Books\n\n* **[The Hitchhiker's Guide to Python](https://docs.python-guide.org)**. \n* **[Automate the Boring Stuff with Python](https://automatetheboringstuff.com/2e/chapter1/)**. \n* **[Python for Everybody](https://www.py4e.com/book.php)**. \n* **[Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/)**.\n* **[Python for Data Analysis](https://bedford-computing.co.uk/learning/wp-content/uploads/2015/10/Python-for-Data-Analysis.pdf)**. \n\n## Data science general\n\n### Courses/Tutorials\n\n* **[Microsoft course Data-science-for-beginners](https://github.com/microsoft/Data-Science-For-Beginners)**.\n\n## Machine Learning\n\n### Courses/Tutorials\n\n* **[Google's machine learning crash course](https://developers.google.com/machine-learning/crash-course/ml-intro)**. \n* **[Scikit-learn workshop](https://github.com/amueller/ml-workshop-1-of-4)** material by Andreas Mueller, core contributor to Scikit-learn.\n* **[Applied machine Learning](https://github.com/amueller/COMS4995-s19)** material from Columbia University. \n* **[Machine learning with python](https://github.com/tirthajyoti/Machine-Learning-with-Python)** github repo with numerous tutorials. \n* **[Notes on data science \u0026 machine learning](https://chrisalbon.com)** by Chris Albon.\n* **[Machine learning (theory) flashcards](https://github.com/gmaclenn/ml-flashcards-python/tree/master/flashcards)** by Chris Albon. \n* **[Introduction to Machine Learning with Scikit-learn](https://courses.dataschool.io/introduction-to-machine-learning-with-scikit-learn)**.\n* **[Kaggle Machine Learning Explainability](https://www.kaggle.com/learn/machine-learning-explainability)**.\n* **[Scikit-learn Course](https://inria.github.io/scikit-learn-mooc/ml_concepts/slides.html)**.\n* **[Microsoft course ML-for-beginners](https://github.com/microsoft/ML-For-Beginners)**.\n\n\n### Books\n* **[Natural Language Processing with Python](http://www.nltk.org/book_1ed/)**. \n* **[Hands on Machine Learning with Scikit-learn and Tensorflow](http://index-of.es/Varios-2/Hands%20on%20Machine%20Learning%20with%20Scikit%20Learn%20and%20Tensorflow.pdf)**.\n* **[A curated list of widely cited papers on machine learning](https://github.com/tirthajyoti/Papers-Literature-ML-DL-RL-AI)**.\n* **[Introduction to Machine Learning with Python](http://noracook.io/Books/Python/introductiontomachinelearningwithpython.pdf)**. \n\n## Natural Language Processing\n\n### Courses/Tutorials\n\n* **[Introduction to Natural Language processing](https://courses.analyticsvidhya.com/courses/Intro-to-NLP)**.\n* **[Awesome NLP](https://github.com/keon/awesome-nlp)** curated list of tutorials and articles.\n\n### Books\n\n* **[Introduction to Natural Language Processing](https://london.ac.uk/sites/default/files/study-guides/introduction-to-natural-language-processing.pdf)**\n* **[Natural Language Processing with Python](https://www.nltk.org/book/)**\n\n\n## Deep Learning\n\n### Courses/Tutorials\n\n* **[FastAI](https://course.fast.ai)** practical deep learning for coders.\n* **[Scaler Topics](https://www.scaler.com/topics/what-is-deep-learning/)** Deep learning.\n\n### Books\n\n* **[Deep Learning](https://www.deeplearningbook.org)**.\n\n## Maths \u0026 Statistics\n\n### Courses/Tutorials\n\n* **[From 0 to Research Scientist Resource Guide](https://github.com/ahmedbahaaeldin/From-0-to-Research-Scientist-resources-guide)**.\n* **[Khan Academy Statistics and Probability](https://www.khanacademy.org/math/statistics-probability)**.\n* **[Khan Academy Linear Algebra](https://www.khanacademy.org/math/linear-algebra)**.\n* **[Khan Academy Calculus](https://www.khanacademy.org/math/calculus-1)**.\n* \n\n### Books\n\n* **[Practical Statistics for Data Scientists](https://github.com/Chandra0505/Data-Science-Resources/blob/master/machine-learning/Practical%20Statistics%20for%20Data%20Scientists.pdf)**. \n* **[Think Stats](https://greenteapress.com/thinkstats/)**. \n* **[Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)**. \n* **[Statistics in Plain English](https://www.book2look.com/embed/9781317526988)**. \n* **[Computer Age Statistical Inference](https://web.stanford.edu/~hastie/CASI_files/PDF/casi.pdf)**.\n\n## Data Engineering\n\n* **[Machine learning system design - data engineering](https://docs.google.com/document/d/1b9iuZiDEGVLHyMmnf6w2y1aN6yWQhAyqk3GHlpI9q6M/edit#heading=h.a8w2b79yy875)**, Stanford lecture notes by Chip Huyen.\n\n## Data Science Libraries\n\n* **[Curated list of Python libraries for data science](https://github.com/krzjoa/awesome-python-data-science)**.\n\n## Code Helpers\n\n* **[Quickly find commonly used code snippets with codegrepper](https://www.codegrepper.com/code-examples/python)**.\n\n## Code Practice\n\n* **[Leetcode](https://leetcode.com/)**.\n\n# Misc\n\n* **[Mac setup guide](https://sourabhbajaj.com/mac-setup/)**.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frebecca-vickery%2Fdata-science-learning-resources","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frebecca-vickery%2Fdata-science-learning-resources","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frebecca-vickery%2Fdata-science-learning-resources/lists"}