Welcome!
In this video we'll introduce you to Jupyter
Notebooks.
So what is Jupyter and what are Jupyter Notebooks?
Simply put, Jupyter is the evolution of IPython
notebooks.
It's basically open-source and interactive
data science and scientific programming.
Jupyter has replaced IPython notebooks, which
provided a rich architecture for interactive
computing with features such as.
A powerful interactive shell.
A kernel for Jupyter.
Support for interactive data visualization
and use of GUI toolkits.
Flexible, embeddable interpreters to
load into your own projects.
Easy to use, high performance tools for parallel
computing.
In a nutshell, a Jupyter Notebook is a web
application that allows you to create and
share documents that contain live code, equations,
visualizations and explanatory text.
Uses include: data cleaning and transformation,
numerical simulation, statistical modeling,
machine learning and much more.
The Jupyter Notebook on Data Scientist Workbench
supports popular programming languages in
Data Science such as Python, R, and Scala.
Notebooks can be shared with others using
email, Dropbox, GitHub and the Jupyter Notebook
Viewer.
Code can produce rich output such as images,
videos, and JavaScript.
And, Interactive widgets can be used to manipulate
and visualize data in real-time.
The notebook leverages big data tools, such
as Apache Spark, from Python, R and Scala.
So you can see that, Jupyter notebooks are
indeed a great environment where you can write
your code in Python, Scala or R, see your
output, and add documentation with stylized
text and HTML, all in a single document called
a "notebook".
Notebooks are used to help you - and others
- understand what's going on in your code,
which makes it ideal for collaboration.
This brings us to the end of this video.
Watch the other videos in this course to learn
more about Jupyter and other notebooks.
Thanks for watching!
