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Data Science with Andreas Lauschke, Session#11: Managing and Analyzing Large Data
Andreas Lauschke
Andreas Lauschke, a senior mathematical programmer, live-demos key Wolfram Language features that are very useful in data science. In this eleventh session, he explores the handling of large data, that means data that is too large to fit into kernel memory. Andreas' dataset is the Amazon customer reviews dataset which is about 70 GB in size. He demonstrates some data preprocessing strategies for targeted data extraction, shows several smart queries against that data, and provides some visualizations.
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Channels: Wolfram Language Livestreams
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Andreas Lauschke Andreas Lauschke live-demos key features of Wolfram Language that are very useful in data science. |
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Andreas Lauschke Andreas Lauschke live-demos key features of Wolfram Language that are very useful in data science. |
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Alan Calvitti Alan Calvitti continues his live coding presentation on functional methods and patterns to write compact but flexible pipes for data science using real-world examples. |
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Andreas Lauschke Andreas Lauschke continues his live-demo of key features of Wolfram Language that are very useful in data science. |
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Alan Calvitti Alan Calvitti presents functional methods and patterns to write compact but flexible pipes for data science using real-world examples. |
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Alan Calvitti Alan Calvitti continues his live coding presentation on functional methods and patterns to write compact but flexible pipes for data science using real-world examples. |
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Andreas Lauschke Andreas Lauschke live-demos key features of Wolfram Language that are very useful in data science. |
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Andreas Lauschke Andreas Lauschke continues his live-demo of key features of Wolfram Language that are very useful in data science. |