📊 Awesome Data Resources for August 🚀
Here are awesome data resources just for you! 🎉
This is the August 2020 version of the monthly email with great data science resources from Jeff Hale.
If this message was forwarded to you, you can subscribe at dataawesome.com! 😀
Everything below is designed to help you sharpen your data skills and everything has a lot of awesome! Let's get to it! 🚀
Awesome articles 💻
Better Explained is a whole website of awesome articles for understanding math, probability, and statistics. You'll find great explanations of Bayes' theorem, calculus, the natural logarithm, and more! 🤔
How to Understand Things is a thought-provoking piece by Nabeel Qureshi. 🧠
Really not awesome visualization 🖼
😂😂😂😂😂😂😂😂😂😂😂😂😂😂😂😂😂
Image courtesy of Sabah Inbrahim on Twitter.
Awesome books for learning data skills 📖
If you or someone you know is looking to learn data analysis and data science skills, I've got a deal for you. My Data Skills Book Bundle of 4 ebooks is available for a savings of over 60%! For $29 USD you get Memorable Python, Memorable Pandas, Memorable SQL, and Memorable Docker. You'll learn the foundations of Python programming, data manipulation with the pandas library, SQL with PostgreSQL, and how to use Docker containers. 🎉
Awesome keyboard shortcut ⌨️
On a Mac use Command + Control + f to toggle between full screen mode and back again. 👍
Awesome package/extension 📚
Want to be able to use Git inside JupyterLab? Use the jupyterlab-git extension! (I highly recommend making the switch from regular Jupyter notebooks to the JupyterLab interface - lots of benefits!) 😀
Awesome people to follow 📣
Google's François Chollet is the creator of the Keras high-level deep learning API that is now tightly integrated with TensorFlow. He regularly shares thoughtful commentary and updates on the latest deep learning breakthroughs.
Daniela Witten is a Professor of Statistics and Biostatistics at the University of Washington. She's a coauthor of the popular book An Introduction to Statistical Learning . She shares great stats and machine learning content.
What I've been working on 🛠
It's been a busy month! Here are a few resources I made that you might find useful:
How to use the pandas read_html function to quickly scrape HTML tables. 🐼
When to use R² vs RMSE vs MAE for regression evaluation metrics. 👍