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|a Kashif, Mohammed,
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|a Scalable data analysis in Python with Dask /
|c Mohammed Kashif.
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|a [Place of publication not identified] :
|b Packt,
|c [2019]
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| 300 |
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|a 1 online resource (1 streaming video file (3 hr., 41 min., 52 sec.))
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|a Title from title screen (viewed January 31, 2020).
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|a Date of publication from resource description page.
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|a "In this course, you'll learn to scale your data analysis. Firstly, you will execute distributed data science projects right from data ingestion to data manipulation and visualization using Dask. Then, you will explore the Dask framework. After, see how Dask can be used with other common Python tools such as NumPy, pandas, Matplotlib, scikit-learn, and more. You'll be working on large datasets and performing exploratory data analysis to investigate the dataset, then come up with the findings from the dataset. You'll learn by implementing data analysis principles using different statistical techniques in one go across different systems on the same massive datasets. Throughout the course, we'll go over the various techniques, modules, and features that Dask has to offer. Finally, you'll learn to use its unique offering for Machine Learning, using the Dask-ML package. You'll also start using parallel processing in your data tasks on your own system without moving to the distributed environment."--Resource description page
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|a Python (Computer program language)
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| 650 |
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| 650 |
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|x Distributed processing.
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| 650 |
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|a Information visualization.
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| 650 |
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|2 fast
|0 (OCoLC)fst00906987
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| 650 |
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7 |
|a Information visualization.
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|y Click for online access
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