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You're reading from  Apache Superset Quick Start Guide

Product typeBook
Published inDec 2018
Reading LevelIntermediate
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ISBN-139781788992244
Edition1st Edition
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Shashank Shekhar
Shashank Shekhar
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Shashank Shekhar

Shashank Shekhar is a data analyst and open source enthusiast. He has contributed to Superset and pymc3 (the Python Bayesian machine learning library), and maintains several public repositories on machine learning and data analysis projects of his own on GitHub. He heads up the data science team at HyperTrack, where he designs and implements machine learning algorithms to obtain insights from movement data. Previously, he worked at Amino on claims data. He has worked as a data scientist in Silicon Valley for 5 years. His background is in systems engineering and optimization theory, and he carries that perspective when thinking about data science, biology, culture, and history.
Read more about Shashank Shekhar

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Dataset

A favorite on my blogroll at https://austinrochford.com/ is a good place to discover intuitive explanations of Bayesian machine learning methods. In the December 29, 2017 blog post Quantifying Three Years of Reading (https://austinrochford.com/posts/2017-12-29-quantifying-reading.html), the blogger analyzes changes in their own reading log dataset. The reading log is available as a Google sheet at this link: https://docs.google.com/spreadsheets/d/1wNbJv1Zf4Oichj3-dEQXE_lXVCwuYQjaoyU1gGQQqk4/.

The reading log is a time series dataset, which the blogger frequently updates. I have taken a snapshot of the reading log and saved it in the chapter's GitHub directory, https://github.com/PacktPublishing/Superset-Quick-Start-Guide/Chapter04/. The dataset has been modified by the addition of a new column. You can run the Jupyter Notebook generate_dataset.ipynb to generate the...

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Apache Superset Quick Start Guide
Published in: Dec 2018Publisher: ISBN-13: 9781788992244

Author (1)

author image
Shashank Shekhar

Shashank Shekhar is a data analyst and open source enthusiast. He has contributed to Superset and pymc3 (the Python Bayesian machine learning library), and maintains several public repositories on machine learning and data analysis projects of his own on GitHub. He heads up the data science team at HyperTrack, where he designs and implements machine learning algorithms to obtain insights from movement data. Previously, he worked at Amino on claims data. He has worked as a data scientist in Silicon Valley for 5 years. His background is in systems engineering and optimization theory, and he carries that perspective when thinking about data science, biology, culture, and history.
Read more about Shashank Shekhar