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Data Visualization

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Data Visualization

Georgia MarianiOctober 2, 2015 0

How and why education customers are using SAS

Recently, I interviewed four higher education customers to hear firsthand how each is using analytics in real life (IRL). In this blog series, we will learn how educators are using analytics, why they chose SAS, and the impact it has had on their users and their institutions. In addition, they

Data Visualization

Trent SmithOctober 1, 2015 4

Hey, hey mama, it ain’t easy being a black dog

Black Dog Syndrome may sound like the clinical term for a Led Zeppelin earworm but, as any animal shelter worker will tell you, it actually describes the challenge black dogs face in getting adopted. This phenomenon is in the spotlight today because Oct. 1 is National Black Dog Day! Black

Data Visualization

Average finish times by age group for the New York City Marathon

Paul VezzettiSeptember 17, 2015 2

Am I running slower as I get older?

I read an article recently discussing how runners inevitably slow down with age, particularly after 50. Data from the New York Marathon and Boston Marathon back this up with generally flat average finishing times for ages 20-49 followed by a steady, almost exponential, increase after 50. I haven’t reached the

English

Data Visualization

VirtualOil Rolling 5-Year Portfolio Aug 15

Ian JonesSeptember 4, 2015 0

Time for VirtualOil 2.0?

Since our last VirtualOil update in May, oil prices have continued to take a beating. As the chart of the rolling five-year portfolio shows, much of our strip of options is now out-of-the-money and the average value per barrel of that optionality has sunk below $7. No surprise then that

Data Visualization

Dr. Biljana Belamaric WilseyAugust 21, 2015 2

"Analytics": meaning and use

As a linguist, I am fascinated with words, their meanings and use. So when I recently saw the words “learning analytics” in a conference paper title, I started thinking about the prevalence of the word “analytics” itself. In the last decade, we have preceded “analytics” with many modifiers referring to

English

Data Visualization

Leo SadovyAugust 11, 2015 0

Visualization – Worth a thousand words

Why visualization? Several reasons, actually, the most compelling being that sometimes visualization literally solves the problem for you. I remember an exercise in eighth grade English class where we were asked to describe, in words only, an object set in front of us with sufficient clarity such that our classmates,

Data Visualization

Simon KirbyJuly 30, 2015 0

How insurers can take a giant leap forward with big data

Oh, how times have changed during my 20-plus years in the insurance industry. Data wasn’t a word we used much back in the 80s and 90s, unless of course you worked in those arcane and mysterious IT data centres. Even amidst the computerisation of the insurance industry in the 80s, many

Analytics | Data Visualization

Leo SadovyJuly 28, 2015 2

Why build models?

We are all modelers. Whenever you plan, you are building a model. Whenever you imagine, you are building a model. When you create, write, paint or speak, you first build in your head a model of what you want to accomplish, and then fill in the details with words, movements

English

Data Visualization

Dr. Jim GoodnightJuly 8, 2015 11

Disaster relief efforts show promise of analytics and seemingly unrelated data sources

As monsoon season begins, many Nepal earthquake victims have shelter over their heads thanks in part to an unlikely intersection of two SAS global development projects. The first project is with the International Organization for Migration (IOM). IOM is the first responder to any crisis that displaces people. IOM provides

English

Data Visualization

Mercedes ad depicting left and right brain functions

Natan MeekersJune 29, 2015 2

See what you never expected with data visualization

A strong quote from John Tukey explains the essence of data visualization: “The greatest value of a picture is when it forces us to notice what we never expected to see.” Tukey was a famous American mathematician who truly understood data – its structure, patterns and what to look for.

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