Scaling Python Data Applications with Dask

Learn how to work with very large datasets without leaving familiar and rich Python data ecosystem. This course will teach you how to leverage power of Dask library in order to handle data that is too big for regular tools like Pandas or NumPy.
Course info
Rating
(14)
Level
Intermediate
Updated
Jun 21, 2019
Duration
1h 21m
Table of contents
Description
Course info
Rating
(14)
Level
Intermediate
Updated
Jun 21, 2019
Duration
1h 21m
Description

Working with so-called ‘Big Data’ can be a daunting task and many tools that solve this problem have a very steep learning curve. Also, developers familiar with Python may not want to resort to solutions built on another technology stack. In this course, Scaling Python Data Applications with Dask, you will gain the ability to work with very large datasets using a Python-native and approachable tool. First, you will learn how to use Dask when your application written using standard Python stops working because of the growing size of the data. Next, you will discover how Dask works underneath and what techniques it uses to make processing large datasets in various scenarios possible and accessible. Finally, you will explore how to exchange Pandas and NumPy for their Big Data variants, with practically no changes to the code. When you’re finished with this course, you will have the skills and knowledge of Dask needed to confidently write data applications that scale, using exclusively Python stack.

About the author
About the author

Paweł is a software engineer passionate about knowledge sharing. He's especially focused on processing and exploring data sets (be it big or small) and is always searching for emerging tools that will make working with data simpler in the future.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
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