Important Links #2
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Python documentationThe Python community maintains an exceptionally well written documentation. You'll find there everything necessary for your day-to-day Python experience. So, no need to rely on third-party, ad contaminated, badly written, so called tutorial.
The scientific stackThe Python community also provides a large scientific environment, including libraries for most applications and use case. Typically, you'll spend most of the time with the following packages:
NotebooksNotebooks are a very convenient way for interactively working with your data. Project Jupyter provides a popular, language-agnostic notebook interface that runs in your favorite browser. In fact, Jupyter with Python is, the de-facto standard tool for the whole project lifecycle in Data Science and AI. You will typically use JupyterLab for prototyping, data cleaning, model training, evaluation and presentation. You can even prepare portable slides for your next conference gig, that run on every operating system. Go check it out! Note that there are currently two interfaces: JupyterLab and Jupyter Notebook. You should use JupyterLab. Jupyter Notebook is the legacy or classic interface that soon will be fully replaced by JupyterLab. Don't reinvent the wheelMost novice programmers will typically spend many hours doing things that
As a general rule of thumb, before you write anything, take a look at the standard library to see if there is already a solution that suits your needs. If you don't find your desired algorithm, check out the scientific stack; they'll have it in 95% of all cases. If you can't find it there, look again. It really is there, you just didn't frame your problem correctly, because you probably thought to narrowly and specific - go abstract. If that should fail, too, then and only then you'll role out your own algorithm. Have fun! |
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Everyone can share links he or she finds valuable.
Our Zoom Link is:
https://uni-hamburg.zoom.us/j/64686101884?pwd=anl5RS9sTE0wazJ4MmhEalRLK0hIQT09
The passcode is:
95593723
Maybe you found helpful resources for learning python programming or musicology related topics. Feel free to share them.
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