2927 search results for "GIS"

Milano (Italy). April 18, 2013. Third Milano R net meeting: agenda

April 10, 2013
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Milano (Italy). April 18, 2013. Third Milano R net meeting: agenda

April 18, 2013 - 18:00 - 21:00 Fiori Oscuri Bistrot & Bar (www.fiorioscuri.it) Via Fiori Oscuri, 3 - Milano (Zona Brera) 18.00 - 18.15 Registration 18.15 - 18.30 Welcome presentation Andrea Spanò, Partner at Quantide 18.30 - 19.00 Digit recognition Machine … Continue reading →

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Gradient Boosting: Analysis of LendingClub’s Data

April 8, 2013
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Gradient Boosting: Analysis of LendingClub’s Data

An old 5.75% CD of mine recently matured and seeing that those interest rates are gone forever, I figured I’d take a statistical look at LendingClub’s data. Lending Club is the first peer-to-peer lending company to register its offerings as securities with the Securities and Exchange Commission (SEC). Their operational statistics are public and available for download. The latest

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Knoxville R Users Group Formed, Free Training Offered

April 8, 2013
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Knoxville R Users Group Formed, Free Training Offered

R is popular free and open-source software for graphics and data analytics. The Knoxville R Users Group is being formed to help people learn R and improve their skills with it. Three departments of The University of Tennessee are working together … Continue reading →

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Instructions for Installing & Using R on Amazon EC2

April 8, 2013
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If you’re an R user, you’ve surely heard all the hype around ‘big data’ and how R is commonly used to analyze these volumes of data. One thing that’s often missing from the discussion is HOW to work around issues using big data and R, specifically how to deal with the fact that R stores Instructions for Installing...

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Use foursquare to locate a twitter user using R

April 8, 2013
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Use foursquare to locate a twitter user using R

I've been doing some work with Twitter data. In much of this work, my life would be so much easier if we could geographically locate the origin of the tweets. There are some ways to do this using the twitter APIs. For example, if a user has geo-locatio...

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Halo Effects vs. Intention-Laden Ratings: Separating Baby and Bathwater

April 8, 2013
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Halo Effects vs. Intention-Laden Ratings: Separating Baby and Bathwater

Are halo effects real or illusory?  Much has been written arguing that rating scales contain extensive amounts of measurement bias.  Some tells us to avoid ratings altogether (What do customers really want?).  Others warn against the use of ratings scales without major adjustments (e.g., overcoming scale usage heterogeneity with the R package bayesm).  Obviously, by including the...

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Spring Cleaning Data: 1of 6- Downloading the Data & Opening Excel Files

April 8, 2013
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With spring in the air, I thought it would be fun to do a series on (spring) cleaning data. The posts will follow my efforts to to download the data, import into R, cleaned it up, merge the different files, add columns of information created, and then ...

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A quick guide to non-transitive Grime Dice

April 7, 2013
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A quick guide to non-transitive Grime Dice

A very special package that I am rather excited about arrived in the mail recently. The package contained a set of 6-sided dice. These dice, however, don’t have the standard numbers one to six on their faces. Instead, they have assorted numbers between zero and nine. Here’s the exact configuration: Aside from maybe making for

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Venue Recommendation – A Simple Use Case Connecting R and Neo4j

April 7, 2013
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Venue Recommendation – A Simple Use Case Connecting R and Neo4j

Last month I attended the CeBIT trade fair in Hannover. Besides the so called “shareconomy” there was also another main topic across all expedition halls - Big Data. This subject is not completely new and I think that a lot of you also have experiences with some of the tools associated with Big Data. But due to the great...

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Worry about correctness and repeatability, not p-values

April 5, 2013
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Worry about correctness and repeatability, not p-values

In data science work you often run into cryptic sentences like the following: Age adjusted death rates per 10,000 person years across incremental thirds of muscular strength were 38.9, 25.9, and 26.6 for all causes; 12.1, 7.6, and 6.6 for cardiovascular disease; and 6.1, 4.9, and 4.2 for cancer (all P < 0.01 for linear Related posts:

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