Tag Archives: R programming courses in Gurgaon

Analyze Smartphone Sensor Data with R and the BreakoutDetection Package

Analyze-Smartphone-Sensor-Data-with-R-and-the-BreakoutDetection-Package
 

Quite interetsing. Juggling with sensor data is starkly different from economics data, document processing or social networking, but very worthwhile. In this blog, we will take a practical approach to analyze smartphone sensor data with R. We are going to use the accelerometer smartphone data that Datarella presented in its Data Fiction competition. The dataset signifies the stimulation along the three axes of the smartphone:

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ANZ uses R programming for Credit Risk Analysis

At the previous month’s “R user group meeting in Melbourne”, they had a theme going; which was “Experiences with using SAS and R in insurance and banking”. In that convention, Hong Ooi from ANZ (Australia and New Zealand Banking Group) spoke on the “experiences in credit risk analysis with R”. He gave a presentation, which has a great story told through slides about implementing R programming for fiscal analyses at a few major banks.

 
ANZ uses R programming for Credit Risk Analysis
 

In the slides he made, one can see the following:

How R is used to fit models for mortgage loss at ANZ

A customized model is made to assess the probability of default for individual’s loans with a heavy tailed T distribution for volatility.

 

One slide goes on to display how the standard lm function for regression is adapted for a non-Gaussian error distribution — one of the many benefits of having the source code available in R.

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Introducing The New R Tools For Visual Studio

Introducing The New R Tools For Visual Studio
 

It is a great new development that the new Visual Studio now speaks the R Language!

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How is data science helping NFL players win Super bowl?!

Recently, a discussion was held, which invited data scientists and analysts all over the world, to take part in the Science of Super Bowl discussion panel, this discussion was held by Newswise.

Data Science in Super bowl

We found one notable discussion topic, which answered three very important questions related to data science that the sports industry could use:

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