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You're reading from  Apache Superset Quick Start Guide

Product typeBook
Published inDec 2018
Reading LevelIntermediate
Publisher
ISBN-139781788992244
Edition1st Edition
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Author (1)
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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Summary statistics – headline

Superset has a chart useful for dashboards called a headline. It is a chart that plots a single metric. Single numbers can answer key questions that we have about datasets.

In the process of analyzing the page count feature using charts in Superset, we looked at its distribution and its relationship with other features. One of the simplest questions one can ask is the average number of days required to read per page.

We will plot the average value for the page count divided by days across all books to capture the answer to that question. In Superset, we can write Custom SQL code to calculate metrics:

Customizing SQL code to calculate a metric

After clicking on the Metric, select the Custom SQL tab to write AVG(pages/days) as the code:

Average number of pages read each reading day

Information in charts must be easy to understand. The precision...

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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