How to do Data Science with coLaboratory or Google Colab Platforms

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Building products and making decisions based on data is at the core of what we do as Data Scientist. Increasingly common among fields such as journalism and government, this data-driven mindset is changing the way traditionally non-technical organizations do work.

What is coLaboratory ?

Colaboratory, or "Colab" for short, allows you to write and execute Python in your browser, with
  • Zero configuration required
  • Free access to GPUs
  • Easy sharing
Whether you're a student, a data scientist or an AI researcher, Colab can make your work easier.


Watch to learn more.

In order to bring this approach to even more fields, Google Research is excited to be a partner in the coLaboratory project, a new tool for data science and analysis, designed to make collaborating on data easier.

Data Science

With Colab you can harness the full power of popular Python libraries to analyze and visualize data. The code cell below uses numpy to generate some random data, and uses matplotlib to visualize it. To edit the code, just click the cell and start editing.


You can import your own data into Colab notebooks from your Google Drive account, including from spreadsheets, as well as from Github and many other sources. To learn more about importing data, and how Colab can be used for data science, see the links below under Working with Data.


 Created by Google Research, Matthew Turk (creator of the yt visualization package), and the IPython/Jupyter development team, coLaboratory merges successful open source products with Google technologies, enabling multiple people to collaborate directly through simultaneous access and analysis of data.
Setting up an environment for collaborative data analysis can be a hurdle, as requirements vary among different machines and operating systems, and installation errors can be cryptic.
The coLaboratory Chrome App addresses this hurdle. One-click installs coLaboratory, IPython, and a large set of popular scientific python libraries (with more on the way). Furthermore, because we use Portable Native Client (PNaCl), coLaboratory runs at native speeds and is secure, allowing new users to start working with IPython faster than ever.

More Resources

Working with Notebooks in Colab

Read related article

How to do Machine Learning with Google colab ?. Read more.

 For more information about this project please see Google coLaboratory's talks on collaborative data science and zero dependency python. 

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