Course
Parallel Programming with Dask in Python
IntermediateSkill Level
Updated 04/2024PythonProgramming4 hr15 videos51 Exercises4,150 XP4,843Statement of Accomplishment
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Use Parallel Processing to Speed Up Your Python Code
With this 4-hour course, you’ll discover how parallel processing with Dask in Python can make your workflows faster.When working with big data, you’ll face two common obstacles: using too much memory and long runtimes. The Dask library can lower your memory use by loading chunks of data only when needed. It can lower runtimes by using all your available computing cores in parallel. Best of all, it requires very few changes to your existing Python code.
Analyze Big Structured Data Using Dask DataFrames
In this course, you use Dask to analyze Spotify song data, process images of sign language gestures, calculate trends in weather data, analyze audio recordings, and train machine learning models on big data.You’ll start by learning the basics of Dask, exploring how parallel processing in Python can speed up almost any code. Next, you’ll explore Dask DataFrames and arrays and how to use them to analyze big structured data.
Train machine learning models using Dask-ML
As you progress through the 51 exercises in this course, you’ll learn how to process any type of data, using Dask bags to work with unstructured and structured data. Finally, you’ll learn how to use Dask in Python to train machine learning models and improve your computing speeds.Prerequisites
Data Manipulation with pandasPython Toolbox1
Lazy Evaluation and Parallel Computing
This chapter will teach you the basics of Dask and lazy evaluation. At the end of this chapter, you'll be able to speed up almost any Python code by using parallel processing or multi-threading. You'll learn the difference between these two task scheduling methods and which one is better under which circumstances.
2
Parallel Processing of Big, Structured Data
Here you’ll learn how to analyze big structured data using Dask arrays and Dask DataFrames. You'll learn how everything you know about NumPy and pandas can easily be applied to data that is too large to fit in memory.
3
Dask Bags for Unstructured Data
Process any kind of data. You'll learn how Dask bags can be used to efficiently process unstructured text data, semi-structured JSON data, and even recorded audio.
4
Dask Machine Learning and Final Pieces
Harness the power of Dask to train machine learning models. You'll learn how to train machine learning models on big data using the Dask-ML package, and how to split Dask calculations across a mixture of processes and threads for even greater computing speed.
Parallel Programming with Dask in Python
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FAQs
Is Python good for parallel processing?
Yes, multiprocessing in Python allows your computer to utilize multiple cores of a CPU to run tasks/processes in parallel, helping to speed up your code.
What is Dask in Python used for?
Dask is a Python library used for parallel processing. It can help you scale your data science or machine learning workflows by improving the performance and speed of Python script execution.
Is Dask faster than pandas?
Yes, tasks in Dask generally execute faster than in pandas.
Is Dask faster than NumPy?
Yes, because of parallelization, tasks in Dask are generally faster than in NumPy.
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