Mehak mehak-sachdeva

NYC Data Science Academy Workshop <05.17.2017>

To follow along - Set up a Google Api key here (you will need to be logged into your gmail account for this): https://developers.google.com/maps/documentation/directions/get-api-key

The branch of Epidemiology, a cornerstone of public health, is based on the practice of identifying risk factors for disease and shapes policy decisions finding patterns and abnormalities in the evidence.
Among other factors, spatial variations in disease incidences and health outcomes can play a crucial role in evaluating policies around health-care distribution and performance. Analysis of visible spatial patterns in data, using Moran's I, can lead to evidence of patterns of dependence and the level of noise in the data. Moran's I study provides with clustering patterns and concentrated abnormalities that allow the investigator to study to illuminate any unusual patterns and explore reasons for variations beyond normal in those areas. Detection of such patterns can be a crucial first step toward recognizing emerging environmental hazards or even persistent errors in data recording methodology.

< 04.12.2017>

<03.24.2017>

##Columbia Workshop

#Intro to Builder

November 29, 2016

Mehak Sachdeva, [email protected], @MehakSachdeva_

This original link is here: http://bit.ly/1117-yale

November 17, 2016

Mehak Sachdeva, [email protected], @MehakSachdeva_

##Data Observatory: We will query the real-estate data for Los Angeles, CA. We start with an empty map and rename the table to LA:

INSERT INTO LA(the_geom, name)
SELECT *
FROM OBS_GetBoundariesByPointAndRadius(

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