The Data-Centric AI Community is the home of all things data 🐍
This repository was created by our community members to build a curated list of awesome resources such books, tutorials, courses, open-source libraries, exercises and other materials that support Pythonistas in the making, and Pythonistas migrating into Data Science!
Check our CONTRIBUTING guide!
💫 You can also find us at our Discord Server to meet other learners, find co-developers or mentors, and engage in small hands-on coding sessions!
If you're serious about starting your journey as a Pythonista, then you need to start with the basics. As a first approach to the language, we suggest that you start with the book "How to Think Like a Computer Scientist: Learning with Python 3" and follow up with the exercises presented in "Python By Example: Learning to Program in 150 Challenges". All exercises in the latter book have solutions, so it could be a nice way for you to start practicing.
If you feel up to it, and to keep yourself in check, you can contribute with exercises and solutions that you come up with to this repository. Just make sure to follow the structure under python-mastery and add your exercise and solution.py, or add a new version of a solution in case the exercise already exists and you think your solution is different from the one(s) presented (e.g. solution-03.py).
- 100 Page Python Tutorial (PDF Version | At Medium.com) - Includes quizes, knowledge checks, + projects.
- Hitchhiker's Guide to Python - Python best practices guidebook, written for humans.
- How to Think Like a Computer Scientist: Learning with Python 3
- Python By Example: Learning to Program in 150 Challenges
- 30-Days-Of-Python - 30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. Nevertheless, this challenge may take more than 100 days, so follow your own pace.
- learn-python - Playground and cheatsheet for learning Python. A collection of Python scripts that are split by topics and contain code examples with explanations!
- python-programming-exercises - 100 Python challenging programming exercises (with solutions!)
Please refer to this folder.
- Create Your Own Data - Making fictional data will showcase what you’ve learned ways that will support your journey towards data science.
- Build a Python Guessing Game - Classic introductory programming challenge.
To learn data science, the CRISP-DM is a good approach:
- Business/Problem Understanding
- 🆕 Data Understanding: Check our EDA Projects in the Exercises section below! 🎉
- 🆕 Data Preparation: Follow the Tutorials below!
- Modelling
- Evaluation
- Deployment
🚧 WIP
- data-engineering-zoomcamp: Free Data Engineering course!
- mlops-zoomcamp: Free MLOps course!
- 01 - Understanding your data with descriptive statistics
- 02 - Understanding your data with visualization
- 03 - Prepare your Data For Machine Learning (👷♀️ coming soon!)
- 00 - Getting Started with Missing Data
- 01 - Introduction to Missing Data 🎉
- 02 - Missing Data Imputation with Statistical Methods 🎉
- 03 - Missing Data Imputation with Machine Learning Methods (👷♀️ coming soon!)
- Olympic 124 Years Dataset: Exploring a dataset of the Olympic Games
- Download the project and try to solve it at your own pace!
- Ask as many questions as you like in our discord channel #🐍ds-projects
- Share your final project by creating a Pull Request! 👏
- 50 Data Analysis Projects with Python - 50 Amazing Data Analysis Projects with Python: solved and explained.
- The Insane App: Data Science: Resources, GitHub repos, free books and cheatsheets on Data Science
- The Insane App: Machine Learning: Resources, GitHub repos, free books and cheatsheets on Machine Learning
We are open to collaboration! If you want to start contributing you only need to create a pull request with relevant resources 🚀 If you found these resources useful, please feel free to join our Discord server. We hope to say "Hi" on the other side! 👋
A special shoutout to all contributors who keep pushing the boundaries of Data Science 👏
Made with contrib.rocks.