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Featured Blog Posts – June 2015 Archive (66)

Data Scientists are Improving the World

MIT Sloan Management Review (MIT SMR), through an academic-industry collaborative partnership with SAS, has developed a long term research initiative connected with analytics and management innovation.

Last month I posted a blog pointing to the findings from our latest data and analytics global executive study and report, "The Talent Dividend," highlighting the role of analytics talent in creating competitive advantage at data-oriented…

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Added by Robert Holland on June 2, 2015 at 11:57am — No Comments

Environmental Monitoring using Big Data

In this post, I will cover in-depth a Big Data use case: monitoring and forecasting air pollution.

A typical Big Data use case in the modern Enterprise includes the collection and storage of sensor data, executing data analytics at scale, generating forecasts, creating visualization portals, and automatically raising alerts in the case of abnormal deviations or threshold breaches.

This article will focus on an implemented use case: monitoring and analyzing air quality…

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Added by Axibase Corp on June 2, 2015 at 6:00am — No Comments

7 Ingredients for Great Visualizations

Great article by Bernard Marr. Here we present a summary. A link to the full article is provided below.

Source for picture: click here

1. Identify your target audience. 

2. Customize the…

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Added by Vincent Granville on June 1, 2015 at 9:30am — No Comments

Data Science Summer Reading List 2015

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Added by Michael Walker on June 1, 2015 at 7:30am — No Comments

R Functions for Exploratory Analysis, Data Frame Merging & Map Displays

Given below is a list of R functions for quickly exploring the key attributes of the data set. The data set is based on car prices & insurance…

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Added by Vozag on June 1, 2015 at 5:53am — 3 Comments

NFL Play by Play analysis using Cloudera Impala

We had the chance to use the NFL play by play dataset all the way from 2002 through 2013 and the best part is the analysis was carried within Hadoop using Cloudera Impala.

For the analysis we wanted to be at the individual game level but the data contained mixed grain including the play by play data. So what we ended up doing was apply some SQL filters to restrict it to the first row of each play by play dataset.

Here are some interesting…

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Added by Nilesh Jethwa on June 1, 2015 at 5:49am — 2 Comments

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