- With the increasing amounts of data available,
the need for data scientists is at an all-time high.
This field has really embraced Python
and helped push the popularity of Python.
So if you're a data scientist or interested in data science,
you can't go wrong learning Python.
In addition to the tools
included with the Python standard library,
there are a plethora of tools available
for all your data science needs,
whether that's data analysis,
data visualization, machine learning, or data storage.
Anaconda is a specialized distribution of Python
that comes pre-bundled with popular software
for data science,
including Pandas, NumPy, Jupyter, and SciPy.
It also makes managing multiple project environments easy
and has its own system for installing
any additional third-party libraries you might need.
JavaScript is not as popular for data science as Python.
However, depending on your needs,
you may still find it to be a valuable skill.
Although the availability of data science functionality
and third-party libraries is not as robust as Python's,
that is changing every day.
TensorFlow, a popular open-source machine learning platform,
released JavaScript support in late 2018
and some of the most popular data visualization tools
like D3.js are built with JavaScript.
If your goal is to become a data scientist,
you really can't go wrong with learning Python,
but you can really up your data visualization game
with a little JavaScript.