1. Introduction
  2. Jupyter Notebook
  3. PyCharm
  4. Summary
  5. References

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. Of course, using multiple tools or platforms is beneficial, but there is a time and place for specific ones. Two beneficial and important tools that many data scientists use are Jupyter Notebook and PyCharm. 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). With that being said, I want to highlight the benefits of both and when to use one over the other below.

NBextensions tab (this is an add-on that is very useful). Screenshot by Author [2].

This tool is incredibly useful for data scientists in an educational setting, as well as a professional setting. 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.

When starting a data science project, you can use Jupyter Notebook [3] 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. You can even do most of the ‘end-to-end’ data science process in your notebook, except for one major step (however, there are platforms that are incorporating notebooks with more machine learning operation steps), which is the deployment, which you will usually do in conjunction with another platform like AWS for example.

In addition to model deployment, you may want to do these main data science steps in the next tool we will discuss below, but when you are first starting off, I think it is easier to preprocess and train data in your notebook, without having to worry about production parts.

To make these points more clear, here is when you can and should use Jupyter Notebook:

  • Prototyping
  • Data ingestion
  • Exploratory data analysis
  • Feature engineering
  • Model comparison
  • Final model

The reason these steps are preferred in the researching step of data science is that it is simply just easier — however, this statement may not be true for everyone, since it is ultimately up to preference.

With that being said, let’s highlight the benefits of the Jupyter Notebook:

  • Free
  • Easy start-up, just type juptyer notebook in your terminal
  • Visualization…

Continue reading: https://towardsdatascience.com/jupyter-notebook-vs-pycharm-7301743a378?source=rss—-7f60cf5620c9—4

Source: towardsdatascience.com