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Getting started with Anaconda Python for data science

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Like many others, I've been trying to get involved in the rapidly expanding field of data science. When I took Udemy courses on the R and Python[1] programming languages, I downloaded and installed the applications independently. As I was trying to work through the challenges of installing data science packages like NumPy[2] and Matplotlib[3] and solving the various dependencies, I learned about theĀ Anaconda Python distribution[4].

Anaconda is a complete, open source[5] data science package with a community of over 6 million users. It is easy to download[6] and install, and it is supported on Linux, MacOS, and Windows.

I appreciate that Anaconda eases the frustration of getting started for new users. The distribution comes with more than 1,000 data packages as well as the Conda[7] package and virtual environment manager, so it eliminates the need to learn to install each library independently. As Anaconda's website says, "The Python and R conda packages in the Anaconda Repository are curated and compiled in our secure environment so you get optimized binaries that 'just work' on your system."

I recommend using Anaconda Navigator[8], a desktop graphical user interface (GUI) system that includes links to all the applications included with the distribution including RStudio[9], iPython[10], Jupyter Notebook[11], JupyterLab[12], Spyder[13], Glue[14], and Orange[15]. The default environment is Python 3.6, but you can also easily install Python 3.5, Python 2.7, or R. The documentation[16] is incredibly detailed and there is an excellent community of users for additional support.

Installing Anaconda


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