Mac and Linux users can directly jump to the For All Users section of this post and follow from step #1 using the built-in terminal. Update: You must also have gcloud sdk installed and run gcloud init on your system following instructions from Detailed Steps If you don’t have both of these, then refer to on how to set up a GCP Compute Engine VM and to download and install VSCode on your local computer. Since you are reading this post, I assume you already have a GCP Compute Engine VM running, and you also have VSCode installed on your personal computer. Let’s now get into the details of this post. A simple example is “Rename Symbol,” which allows you to rename a variable just once but automatically updates everywhere the variable appears in your code! Super handy! Pre-Requisites However, provides an excellent overview of the refactoring tools available in VSCode. This is where VSCode shines in the Data Science workflow! The details of using VSCode to refactor code from a Jupyter notebook deserve a standalone post to do it justice. Even if you do have that luxury, it is good practice to refactor your code before passing it to the production team to ensure the product works as expected once deployed. In many organizations, you do not have the luxury of a dedicated production team. The second aspect of a Data Scientist’s job starts once you have a product that you want to run in production (run for thousands or millions of times every day). Jupyter is highly suitable for the kind of iterative, experimental workflow involved in the early phases of a Data Science project. The first aspect is to run experiments for which you use the (in)famous Jupyter notebook. Why VSCode when I have Jupyter?Īs a Data Scientist, why should you use VSCode when you have the convenience of the Jupyter notebook? Well, if you are like me, then your typical day to day Data Science job involves two aspects. Google Trends comparison between VSCode, Sublime, and Atom from Jan 2016 to Nov 2019.
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