Data Science Hands on with Open source Tools – Course Summary


Welcome to Data Scientist Workbench! In this course, and particularly in the hands-on
labs, you learned about all about the powerful data science tools available to you on Data
Scientist Workbench. You also learned that Data
Scientist Workbench an all-in-one solution
for programmers, data engineers, data journalists, and data scientists who are interested in
running their data analysis in the cloud. You saw that Data
Scientist Workbench comes with many useful tools
and programming languages pre-installed, like Python, R, Scala and Apache Spark! These powerful tools provide a wealth of functionality
that can make any data science or data analytics project easy, and to make things even better,
they’re all FREE! You also have free access to a large number
of tutorials on Data
Scientist Workbench, and again, all of them are available at no cost. So, let’s quickly review what you learned
in each of the modules: In module 1, you went through an overview
of Data
Scientist Workbench in which you learned: How to register an account with Data
Scientist Workbench
How to upload and work with your data How to use OpenRefine to prepare your data
How to load and build the powerful analytical tools on Data
Scientist Workbench, namely, Jupyter and Zeppelin
Notebbooks, RStudio IDE, and Seahorse. How to change your profile settings on Data
Scientist Workbench,
and even You also learned how to change your profile
settings and even how to open the Feedback Forum and vote on your favourite ideas. In module 2, which reviewed Jupyter notebooks,
you learned How to open and navigate within Jupyter
Notebooks How to create and save R scripts
How to run R commands and check your environment variables
You also learned how to upload your files to Jupyter
How to install new packages and load libraries, and
How to restart Jupyter. In module 3 about Zeppelin notebooks,
you learned How to open and navigate within Zeppelin
Notebooks Where to find Zeppelin documentation and
tutorials How to import or create new notes
How to import notebooks (and json files), and
How to restart your Interpreter. In module 4, which covered essential information
about RStudio IDE, you learned How to open and navigate within RStudio
IDE How to create and save R scripts
You also learned how to run R commands and check your environment variables
As well as how to upload files to RStudio How to install new packages and load libraries,
and How to restart RStudio. And finally, in module 5, about Seahorse,
you learned How to open and navigate within Seahorse
As well as how to create, upload, export or download workflows
You also learned how to clone, edit, run, and clear workflows
How to zoom in and zoom out in workflows, and
How to move nodes or fit them all in the visible area. We encourage you to keep practicing with these
tools so you can make use of their full potential. And don’t forget to take the final exam,
when you’re ready. Like a carpenter’s workbench, Data
Scientist Workbench has all
the data science tools you need, right at your fingertips! We hope this course has inspired you to use
these tools on your data science and data analytics projects, and that they’ll help
you along the way to become a great data scientist! Thanks for watching!

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