Thread by @PatrickBloniasz

[1/n] For any aspiring scientists hoping to learn Python 🐍, a great #OpenScience resource is "Python for Neuroscience." They cover everything from the basics of object-oriented programming to machine learning. A 🧵 covering some of my favorite chapters: @PhDVoice @ThePhDPlace

[2/n] "The Scientific Stack - Part 1-2" This chapter goes over essential tools for scientific programming, including NumPy, pandas (data management), SciPy (statistical or signal processing), and matplotlib (cornerstone for data visualization in Python). …-python-for-neuroscientists.github.io/textbook-publi…

[3/n]"Testing and Test-Driven Development" This chapter talks about why "unit tests" make your code more transparent. Essentially, a unit test demonstrates that a portion of your code does what it is supposed to do. Helpful for package development
…-python-for-neuroscientists.github.io/textbook-publi…

[4/n] "Git and More Python Libraries" This chapter talks about how to set up and manage a GitHub repository. This is great for newly computational labs or students who learned to code outside a formal setting. …-python-for-neuroscientists.github.io/textbook-publi…

[5/n] "Virtual Environments and Object-oriented Programming (OOP)" This chapter goes over virtual environments (VE) and OOP. Environments create an isolated coding space for your project so you don't need to worry about packages changing. …-python-for-neuroscientists.github.io/textbook-publi…

[6/n] "Introduction to Machine Learning" ML is something everyone in the computational world should be aware of and should know how to implement in trivial settings. I found this to be a good introduction, especially when supplemented with other resources. …-python-for-neuroscientists.github.io/textbook-publi…

[7/n] Let me know if you are interested in forming a learning group to go through any of these chapters (or other resouces)! We could get together a Zoom/slack group. : )