![]() ![]() When starting a data science project, you can use Jupyter Notebook to import your data, analyze it, choose specific features as well as create new features, create models and compare them, as well as visualize most of the steps as you go. The time to use it is usually at the beginning of the project where your code is not set in stone, and you are focusing on research rather than the end product. This tool is incredibly useful for data scientists in an educational setting, as well as a professional setting. Jupyter Notebook NBextensions tab (this is an add-on that is very useful). With that being said, I want to highlight the benefits of both and when to use one over the other below. Each has its own respective functions, but the end goal can be surprisingly similar, which is to organize and execute code for data science processes ( referring to just data science for the sake of this article). Two beneficial and important tools that many data scientists use are Jupyter Notebook and P圜harm. Of course, using multiple tools or platforms is beneficial, but there is a time and place for specific ones. As a data scientist still learning in an educational setting, you might use one main tool, while you may focus on another, different one as a professional data scientist. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |