Scalable Data Analysis in Python with Dask | Packt

Scalable Data Analysis in Python with Dask | Packt
English | Size: 1.10 GB
Genre: eLearning

Understand the concept of Block algorithms and how Dask leverages it to load large data.
Implement various example using Dask Arrays, Bags, and Dask Data frames for efficient parallel computing
Combine Dask with existing Python packages such as NumPy and Pandas
See how Dask works under the hood and the various in-built algorithms it has to offer
Leverage the power of Dask in a distributed setting and explore its various schedulers
Implement an end-to-end Machine Learning pipeline in a distributed setting using Dask and scikit-learn
Use Dask Arrays, Bags, and Dask Data frames for parallel and out-of-memory computations
Data analysts, Machine Learning professionals, and data scientists often use tools such as Pandas, Scikit-Learn, and NumPy for data analysis on their personal computer. However, when they want to apply their analyses to larger datasets, these tools fail to scale beyond a single machine, and so the analyst is forced to rewrite their computation.

If you work on big data and you’re using Pandas, you know you can end up waiting up to a whole minute for a simple average of a series. And that’s just for a couple of million rows!

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.

All the code files and related files are uploaded on GitHub at this link:

Style and Approach
This hands-on course covers all the important components of Dask (arrays, bags, data frames, schedulers, and the Futures API) to parallelize your existing Python code and perform computations in a distributed setting. This course is designed with minimum theory and maximum practical implementation, followed by step-by-step instructions to get you up and running.

Leverage the power of parallel computing using Dask.delayed
Get complete exposure to using Dask to handle large data in a distributed setting
Learn how to do machine learning by combining scikit-learn and Dask in a distributed setting

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