<p>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. </p><p> </p><p>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! </p><p> </p><p>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. </p><p> </p><p>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. </p><p> </p><p>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. </p><p> </p><p>All the code files and related files are uploaded on GitHub at this link: https://github.com/PacktPublishing/-Scalable-Data-Analysis-in-Python-with-Dask </p><p></p>