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Top Options to Create a Data Science Environment

Top Options to Create a Data Science Environment


When you have taken a course in Python, R programming language, Git, and Unix Shell, you might think of creating a data science environment yourself. And why not? You must use the skills somewhere to increase your relevance in the team. In this article at DataCamp, Michael Galarnyk shares how you can set up a data science environment using these top options.

Creating a Data Science Environment

Here, we will be discussing the benefits of each of the options. Here are the top choices for your data science environment:

Anaconda Python

For a data science environment, developers mostly use Anaconda Python Distribution. Numpy, scikit-learn, scipy, and pandas are already installed in it. The package manager helps install Jupyter Notebooks. For additional packages, you can easily use the compatible conda or pip. Anaconda has Spyder tool that enables you to write code, test, and even fix bugs. You can integrate the package manager with other Python Integrated Development Environments like PyCharm and Atom.

R Programming Language

Developers believe RStudio is the most compatible IDE for R programming language. Once you install RStudio, you have the R language’s functions and objects. You can use that with an R interpreter for builds and commands. The RStudio panes include a text editor, dashboard, R Interpreter, and help window.

Unix Shell

With this option, you can navigate directories, copy files, leverage virtual systems in the data science environment. You need Unix Shell skills for several cloud computing platforms like Google Cloud. You will also find shell commands in Jupyter Notebooks apart from Python. Mac comes with Unix shell, unlike Windows. You can install Git, Gnu, and Cygwin to have Unix commands for your data science environment in a Windows system.


The version control system saves all the versions so that you can go back to the previous versions if needed. With Git, you cannot overwrite because it detects if someone else is also working on the same code lines. Several people can work on the same file from different machines using Git.

All these options can be integrated into your data science environment. If you want to create one specifically for your local computer, go ahead and do it.

To view the original article in full, visit the following link:

The post Top Options to Create a Data Science Environment appeared first on AITS CAI’s Accelerating IT Success.

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