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 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 and Matplotlib and solving the various dependencies, I learned about the Anaconda Python distribution.
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 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, a desktop graphical user interface (GUI) system that includes links to all the applications included with the distribution including RStudio, iPython, Jupyter Notebook, JupyterLab, Spyder, Glue, and Orange. The default environment is Python 3.6, but you can also easily install Python 3.5, Python 2.7, or R. The documentation is incredibly detailed and there is an excellent community of users for additional support.