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@@ -87,7 +87,7 @@ Data Science is one of the hottest topics on the Computer and Internet farmland
 
 While not strictly necessary, having a programming language is a crucial skill to be effective as a data scientist. Currently, the most popular language is _Python_, closely followed by _R_. Python is a general-purpose scripting language which sees applications in a wide variety of fields. R is a domain-specific language for statistics, which contains a lot of common statistics tools out of the box.
 
-Python is by far the most popular language in science, due in no small part to the ease at which it can be used and the vibrant ecosystem of user-generated packages. To install packages, there are two main methods: Pip (invoked as `pip install`), the package manager that comes bundled with Python, and [Anaconda](https://www.anaconda.com) (invoked as `conda install`), a powerful package manager that can install packages for Python, R, and can download executables like Git. 
+[Python](https://python.org/) is by far the most popular language in science, due in no small part to the ease at which it can be used and the vibrant ecosystem of user-generated packages. To install packages, there are two main methods: Pip (invoked as `pip install`), the package manager that comes bundled with Python, and [Anaconda](https://www.anaconda.com) (invoked as `conda install`), a powerful package manager that can install packages for Python, R, and can download executables like Git. 
 
 Unlike R, Python was not built from the ground up with data science in mind, but there are plenty of third party libraries to make up for this. A much more exhaustive list of packages can be found later in this document, but these four packages are a good set of choices to start your data science journey with: [Scikit-Learn](https://scikit-learn.org/stable/index.html) is a general-purpose data science package which implements the most popular algorithms - it also includes rich documentation, tutorials, and examples of the models it implements. Even if you prefer to write your own implementations, Scikit-Learn is a valuable reference to the nuts-and-bolts behind many of the common algorithms you'll find. With [Pandas](https://pandas.pydata.org/), one can collect and analyze their data into a convenient table format. [Numpy](https://numpy.org/) provides very fast tooling for mathematical operations, with a focus on vectors and matrices. [Seaborn](https://seaborn.pydata.org/), itself based on the [Matplotlib](https://matplotlib.org/) package, is a quick way to generate beautiful visualizations of your data, with many good defaults available out of the box, as well as a gallery showing how to produce many common visualizations of your data.