It is an open source, easy-to-install high performance Python and R distribution, with the conda package and environment manager and collection of 1,000+ open source packages with free community support.

Installing Python in a terminal is no joy. Many scientific packages require a specific version of Python to run, and it’s difficult to keep them from interacting with each other. It is even harder to keep them updated. Anaconda Distribution makes getting and maintaining these packages quick and easy.

It’s Anaconda Distribution without the collection of 1,000+ open source packages. 
With Miniconda you install only the packages you want with the conda command,

Anaconda Navigator is an easy way to use graphical Python programs without having to use command line commands.

conda is the package management tool for Anaconda Python installations. Anaconda Python is a distribution from Continuum Analytics specifically aimed at the scientific community, and in particular on Windows where the installation of binary extensions is often difficult.
Conda is a completely separate tool to pip, virtualenv and wheel, but provides many of their combined features in terms of package management, virtual environment management and deployment of binary extensions.
Conda does not install packages from PyPI and can install only from the official Continuum repositories, or anaconda.org (a place for user-contributed conda packages), or a local (e.g. intranet) package server. However, note that pip can be installed into, and work side-by-side with conda for managing distributions from PyPI.

If you quit from the Python interpreter and enter it again, the definitions you have made (functions and variables) are lost. Therefore, if you want to write a somewhat longer program, you are better off using a text editor to prepare the input for the interpreter and running it with that file as input instead. This is known as creating a script. As your program gets longer, you may want to split it into several files for easier maintenance. You may also want to use a handy function that you’ve written in several programs without copying its definition into each program.
To support this, Python has a way to put definitions in a file and use them in a script or in an interactive instance of the interpreter. Such a file is called a module; definitions from a module can be imported into other modules or into the main module (the collection of variables that you have access to in a script executed at the top level and in calculator mode).

Packages are a way of structuring Python’s module namespace by using “dotted module names”. For example, the module name A.B designates a submodule named B in a package named A. Just like the use of modules saves the authors of different modules from having to worry about each other’s global variable names, the use of dotted module names saves the authors of multi-module packages like NumPy or the Python Imaging Library from having to worry about each other’s module names.