Welcome to Data Scientist Workbench!
On the Data Scientist Workbench home page, you have all the data science tools at your fingertips.
Once you're on the main page of Data Scientist Workbench, you'll find 6 buttons.
Click the "My Data" link for help in managing your data.
Clicking the "Open Refine" link will give you a very powerful way to prepare your data
The remaining four links are aimed at building analytics.
Let's take a closer look at each one of these 4 Building Analytics tools.
Jupyter Notebooks let you write and execute Python, R, or Scala code with a "notebook"
in your web browser.
A notebook is an interactive document that allows you to execute code in smaller chunks
called "cells".
When you execute a cell, the notebook prints any output immediately into the output cell.
Doing so allows you to do a number of things.
For instance, you can write your code to import data, print the data, clean the data, print
the cleaned data, create a model, print the model output, and so on.
And you can change the code in an input cell and re-run the cell as often as you like.
But that's not all.
The notebook also supports rendering markup cells inline, so that you can embed text,
markdown, HTML images, videos, and even interactive widgets, all within a notebook.
On Data Scientist Workbench, with Apache Spark pre-installed, you can also try working with
big data.
It is a very popular tool in data science because it allows you to fine-tune each step
of your project, and it allows others to follow your code with ease.
To collaborate and share your notebooks, simply share your notebook URL with others.
The interactive nature and the ability to render text and media, makes notebooks a powerful
environment for working with data, performing analyses, and documenting results.
Zeppelin Notebooks allow interactive data analytics.
Like Jupyter, you use notebooks to ingest, discover, analyze, visualize, and collaborate
with your data.
You can also make beautiful data-driven, collaborative documents with SQL, code and even more!
The Zeppelin interpreter concept allows any language or data processing backend to be
plugged into Zeppelin.
Currently Apache Zeppelin supports many interpreters such as Apache Spark, Python, JDBC, Markdown,
and Shell.
Also, by using an abstract class , adding a new language-backend is really simple.
Apache Zeppelin provides built-in Apache Spark integration.
So, you don't need to build a separate module, plugin or library for it.
For data visualization, some basic charts are already included in Zeppelin, allowing
you to convert from data tables directly into visualizations without any code.
Zeppelin also aggregates values and displays them in pivot charts with simple drag and
drop.
You can easily create charts with multiple aggregated values including sum, count, average,
minimum, and maximum.
And lastly, with Zeppelin you can also dynamically create some input forms in your notebook.
RStudio IDE allows you to analyze data, take advantage of many statistical packages, and
create beautiful visualizations and web applications.
Like other IDEs, RStudio allows you to code in a console or script editor, as well as
keep track of your variables and history.
You can display your plots, manage your packages, and see help documentation for R. Taking it
a step further, with R Shiny library you can make your visualizations interactive.
Using Shiny you can create all sorts of web-based interactive apps just using R code.
Seahorse is an interactive environment that allows data scientists to create a data processing
workflow without writing any code directly in the browser.
You can perform ETL, manipulate and clean data, create reports, and even run predictive
analytics.
Designed with big data analytics in mind, Seahorse workflows can also be deployed to
Spark clusters.
If you're not yet familiar with any of these tools, scroll down in Data Scientist Workbench to get access to
tutorials to learn how to use them, or watch the other videos in this course to quickly
get up to speed on these tools.
This brings us to the end of this video. Thanks for watching!