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Greetings! I hope your are well! ❤️ This is the monthly-ish email with great data science resources from Jeff Hale.
If this message was forwarded to you, you can go ahead and subscribe at dataawesome.com - and give a thank you to the sender! 😀
Everything below is designed to help you sharpen your data skills and everything has a lot of awesome!
Let's get right to it! 🚀
Awesome articles 💻
I was already planning to feature Randy Au as an awesome person to follow in this newsletter, and then he wrote this great article on the importance of doing “good enough” work in data science. It's a philosophy I strongly agree with!
Amanda Makulec was also slated as a great person to follow. Then I found her excellent Ten Considerations Before You Create Another Chart About COVID-19. Definitely worth a read if you're thinking about contributing in the space.
Awesome explanatory videos 🖥
3Blue1Brown is a YouTube video channel with awesome explanatory videos on math, physics, and machine learning. Their Essence of Linear Algebra videos are the best narrated visual explanation on the topic I have found!
Awesome visualization and package 🖼
I previously recommended Ken Petrou as a great person to follow. He recently released the Python bar chart race package. It's very cool. You can make a bar chart race with Matplotlib under the hood in just a few lines of code. He's adding support for Plotly now. I used the package to quickly build a bar chart race that you can see in this Kaggle kernel.
Awesome COVID-19 dashboard 📊
The covidexitstrategy.org has the most comprehensive dashboards showing trends in US state-level data for testing, cases, deaths, and resource use that I have seen. Scroll down the page a bit to see a wealth of information. One thing that would be a nice enhancement is the option for per-capita figures. Hat tip to Amanda Makulec for pointing me toward it.
Awesome unsolicited advice 🗣
It's graduation season, so it only seems fitting I should share some awesome unsolicited advice. Kevin Kelly's 68 Bits of Unsolicited Advice has a lot of gems in it. 💎 Hat tip to Conor Dewey for pointing me toward it.
Awesome books for learning data science 📖
If you or someone you know is looking to learn pandas, check out my Memorable Pandas ebook. It's available for free while it's in pre-release, so download the .pdf and .epub versions! If you previously downloaded it, make sure you go grab the latest version. I have just one ask when you read it: please let me know any constructive feedback you have. Thank you! 🎉
Awesome keyboard shortcut ⌨️
If you are brand new to the terminal, the up arrow is your new best friend. 😀 Use it to cycle through the previous commands you have run. ⬆️ This tip might seem very basic if you've been programming for a while, but I often seem my data science students forget to use it to save keystrokes at the terminal.
Awesome people to follow
Randy Au's Counting Stuff newsletter is definitely worth subscribing to! I mentioned one of his articles above, and also highly recommend his articles on speeding up SQL queries, Git learning resources, and why it's okay to use spreadsheets in data science. He's prolific and thoughtful. Randy has the cool, nerdy-sounding job title of Quantitative UX Researcher at Google Cloud Platform. 🤓
Amanda Makulec's name showed up a few times above. She writes and presents great content about data visualization. 🎨 She has a special focus on making sure data visualizations are done responsibly. Amanda is the Senior Data Visualization Lead at Excella consulting. Fun fact: she's also a fellow organizer in the Washington DC MeetUp scene - both Data Vizualization DC and Data Science DC are under the Data Community DC umbrella.
What I've been working on 🛠
It's been a busy month! Here are a few resources I made that you might find useful:
Scikit-learn 0.23 was released on May 12. In this article I highlight the key improvements so you can be in the know. 👍
I wrote a three-part series on the 7 most common classification metrics. Articles 1 and 2 were released recently and article 3 is coming out momentarily. Follow me on Medium so you get notified when it drops.
I released an article on setting up your Python package docs and automating their builds with Read the Docs. The process is a bit of a pain, so hopefully this article will folks a lot of time. ⏱ This is the third article in a series showing you how to create an open source Python package.