Welcome to Cognitive Class Labs.

On the Cognitive Class Labs homepage,

you have all the data science tools at your fingertips.

Once you're on the main page of Cognitive Class Labs,

you'll find several buttons.

Click the My Data link for help in managing your data.

Clicking the OpenRefine link will give you a very powerful way to prepare your data.

The remaining links are aimed at building analytics.

Let's take a closer look at each of these 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 and print the model output and so on.

And you can change the code in an input cell and rerun the cell as often as you'd like,

but that's not all,

the notebook also supports rendering markup cells in line,

so that you can embed text,

markdown HTML images, videos and even interactive widgets,

all within a notebook.

On Cognitive Class Labs with Apache Spark preinstalled,

you can also try working with Big data.

Jupyter notebooks are 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.

Zeppelin interpreter concept allows

any language or data processing back end to be plugged into Zeppelin.

Currently, Apache's 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,

create beautiful visualizations and Web applications.

Like other IDEs, RStudio allows you to code in a console

or a 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 as an interactive environment that allows data scientists to create

a data processing workflow without writing any code directly in the browser.

You can even perform ETL,

manipulating 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,

simply scroll down in Cognitive Class Labs 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.

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