Introduction

Don’t get me wrong — we always want to improve our productivity — with the same amount of time, we can get more work done. It’s true to data science researchers too. After you have set up your hardware, it’s time to think about how you should pick the software that you need to start your data science projects. The problem is that there are too many choices on the market, and for learning purposes, you may have already tried different tools. In other words, your shopping list is too long and you’re probably lost where you should get started.

In this article, I’d like to share the combination that I’ve found to be suitable to my needs for my data science projects. Certainly, it won’t be a one-size-fits-all solution for all of you. But probably it’s something that you can try first if you have no ideas about your configurations.

Specifically, we’ll use three tools: PyCharm, Anaconda, and JupyterLab. I’ll first introduce the installation and then discuss the role of each tool. I’ll try my best to be concise, because it’ll be overwhelming for beginners if I pour too much information.

Installations

PyCharm

To install PyCharm, you can go to the PyCharm website: https://www.jetbrains.com/pycharm/download/#section=windows. Depending on your OS, you need to download the correct version. I work for a non-profit educational institute, so I have access to the Professional version. You can take advantage of this benefit, if you’re in a similar situation. However, the Community version should work just fine if you mostly do Python development.

Once it’s downloaded, just follow the prompts. Nothing is special.

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