In Tutorials 1 through 5, we covered everything from downloading the Anaconda software to writing simple commands, using more complex packages, and interacting directly with your computer’s operating system. We hope after going through these tutorials you feel more prepared for your future computer science and data science tasks. Another takeaway we would like this project to have is for you to be more comfortable with coding. Coding should not be an intimidating task!
However, there is still much more to learn and we have provided links to further resources below. These links are divided according to the tutorials they are related to.
We wanted our tutorials to be a resource for all students, especially beginner students with no prior computing knowledge. Thus, we chose to offer a basic tutorial of Python within Tutorial 2. However, this python crash course introduction was not meant to be comprehensive and only serves as a vehicle for the user to work within Jupyter Notebook. Here are additional resources on Python including documentation
For more advanced users who care about performance of code, one should look into Magic Commands:
This article provides a more in-depth info about writing symbols and mathematical equations in Markdown.
The tutorial covered plotting (Matplotlib) very thoroughly, but there are more than 200,000 that can do everything you can imagine.
Each package has documentation. We created our tutorial because they are often hard to read. This article" provides information on how to understand documentation.
Below, we list the links to other very commonly used packages in Python. You can always search online for what you need to complete your task.
The tutorial provided an introduction of basic UNIX commands and how to use them, there are a many more commands and techniques to master. The recommend the tutorials below to gain a better understanding of UNIX Commands and the MacOS Terminal.
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