2011 Analytics Recap

December 24, 2011 By Leave a Comment

Events in the field of data that impacted us in 2011

1) Oracle unveiled plans for R Enterprise. This is one of the strongest statements of its focus on in-database analytics. Oracle also unveiled plans for a Public Cloud

2) SAS Institute released version 9.3 , a major analytics software in industry use.

3) IBM acquired many companies in analytics and high tech. Again.However the expected benefits from Cognos-SPSS integration are yet to show a spectacular change in market share.

2011 Selected acquisitions

Emptoris Inc. December 2011

Cúram Software Ltd. December 2011

DemandTec December 2011

Platform Computing October 2011

 Q1 Labs October 2011

Algorithmics September 2011

 i2 August 2011

Tririga March 2011

 

4) SAP promised a lot with SAP HANA- again no major oohs and ahs in terms of market share fluctuations within analytics.

http://www.sap.com/india/news-reader/index.epx?articleID=17619

5) Amazon continued to lower prices of cloud computing and offer more options.

http://aws.amazon.com/about-aws/whats-new/2011/12/21/amazon-elastic-mapreduce-announces-support-for-cc2-8xlarge-instances/

6) Google continues to dilly -dally with its analytics and cloud based APIs. I do not expect all the APIs in the Google APIs suit to survive and be viable in the enterprise software space.  This includes Google Cloud Storage, Cloud SQL, Prediction API at https://code.google.com/apis/console/b/0/ Some of the location based , translation based APIs may have interesting spin offs that may be very very commercially lucrative.

7) Microsoft -did- hmm- I forgot. Except for its investment in Revolution Analytics round 1 many seasons ago- very little excitement has come from MS plans in data mining- The plugins for cloud based data mining from Excel remain promising yet , while Azure remains a stealth mode starter.

8) Revolution Analytics promised us a GUI and didnt deliver (till yet :) ) . But it did reveal a much better Enterprise software Revolution R 5.0 is one of the strongest enterprise software in the R /Stat Computing space and R’s memory handling problem is now an issue of perception than actual stuff thanks to newer advances in how it is used.

9) More conferences, more books and more news on analytics startups in 2011. Big Data analytics remained a strong buzzword. Expect more from this space including creative uses of Hadoop based infrastructure.

10) Data privacy issues continue to hamper and impede effective analytics usage. So does rational and balanced regulation in some of the most advanced economies. We expect more regulation and better guidelines in 2012.

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JMP and R – #rstats

December 16, 2011 By Leave a Comment

An amazing example of R being used sucessfully in combination (and not is isolation) with other enterprise software is the add-ins functionality of JMP and it’s R integration.

See the following JMP add-ins which use R

http://support.sas.com/demosdownloads/downarea_t4.jsp?productID=110454&jmpflag=Y

JMP Add-in: Multidimensional Scaling using R

This add-in creates a new menu command under the Add-Ins Menu in the submenu R Add-ins. The script will launch a custom dialog (or prompt for a JMP data table is one is not already open) where you can cast columns into roles for performing MDS on the data table. The analysis results in a data table of MDS dimensions and associated output graphics. MDS is a dimension reduction method that produces coordinates in Euclidean space (usually 2D, 3D) that best represent the structure of a full distance/dissimilarity matrix. MDS requires that input be a symmetric dissimilarity matrix. Input to this application can be data that is already in the form of a symmetric dissimilarity matrix or the dissimilarity matrix can be computed based on the input data (where dissimilarity measures are calculated between rows of the input data table in R).

Submitted by: Kelci Miclaus SAS employee Initiative: All
Application: Add-Ins Analysis: Exploratory Data Analysis

Chernoff Faces Add-in

One way to plot multivariate data is to use Chernoff faces. For each observation in your data table, a face is drawn such that each variable in your data set is represented by a feature in the face. This add-in uses JMP’s R integration functionality to create Chernoff faces. An R install and the TeachingDemos R package are required to use this add-in.

Submitted by: Clay Barker SAS employee Initiative: All
Application: Add-Ins Analysis: Data Visualization

Support Vector Machine for Classification

By simply opening a data table, specifying X, Y variables, selecting a kernel function, and specifying its parameters on the user-friendly dialog, you can build a classification model using Support Vector Machine. Please note that R package ‘e1071′ should be installed before running this dialog. The package can be found from http://cran.r-project.org/web/packages/e1071/index.html.

Submitted by: Jong-Seok Lee SAS employee Initiative: All
Application: Add-Ins Analysis: Exploratory Data Analysis/Mining

Penalized Regression Add-in

This add-in uses JMP’s R integration functionality to provide access to several penalized regression methods. Methods included are the LASSO (least absolutee shrinkage and selection operator, LARS (least angle regression), Forward Stagewise, and the Elastic Net. An R install and the “lars” and “elasticnet” R packages are required to use this add-in.

Submitted by: Clay Barker SAS employee Initiative: All
Application: Add-Ins Analysis: Regression

MP Addin: Univariate Nonparametric Bootstrapping

This script performs simple univariate, nonparametric bootstrap sampling by using the JMP to R Project integration. A JMP Dialog is built by the script where the variable you wish to perform bootstrapping over can be specified. A statistic to compute for each bootstrap sample is chosen and the data are sent to R using new JSL functionality available in JMP 9. The boot package in R is used to call the boot() function and the boot.ci() function to calculate the sample statistic for each bootstrap sample and the basic bootstrap confidence interval. The results are brought back to JMP and displayed using the JMP Distribution platform.

Submitted by: Kelci Miclaus SAS employee Initiative: All
Application: Add-Ins Analysis: Basic Statistics
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Revolution Webinar Series #Rstats

December 14, 2011 By Leave a Comment

Revolution Analytics Webinar-

 

Featured Webinar
David Champagne REGISTER NOW
Presenter David Champagne
CTO, Revolution Analytics
Date Tuesday, December 20th
Time 11:00AM – 11:30AM Pacific 
Click here for the webinar time in your local time zone

Big Data Starts with R

Traditional IT infrastructure is simply unable to meet

the demands of the new “Big Data Analytics” landscape.   Many enterprises are turning to the “R” statistical programming language and Hadoop (both open source projects) as a potential solution. This webinar will introduce the statistical capabilities of R within the Hadoop ecosystem.  We’ll cover:

  • An introduction to new packages developed by Revolution Analytics to facilitate interaction with the data stores HDFS and HBase so that they can be leveraged from the R environment
  • An overview of how to write Map Reduce jobs in R using Hadoop
  • Special considerations that need to be made when working with R and Hadoop.

We’ll also provide additional resources that are available to people interested in integrating R and Hadoop.

 

Upcoming Webinars
Wed, Dec 14th
11:00AM – 11:30AM PT		 Revolution R Enterprise – 100% R and MoreR users already know why the R language is the lingua franca of statisticians today: because it’s the most powerful statistical language in the world. Revolution Analytics builds on the power of open source R, and adds performance, productivity and integration features to create Revolution R Enterprise. In this webinar, author and blogger David Smith will introduce the additional capabilities of Revolution R Enterprise.
 Archived Webinars-
Revolution Webinar: New Features in Revolution R Enterprise 5.0 (including RevoScaleR) to Support Scalable Data AnalysisRevolution R Enterprise 5.0 is Revolution Analytics’ scalable analytics platform.  At its core is Revolution Analytics’ enhanced Distribution of R, the world’s most widely-used project for statistical computing.  In this webinar, Dr. Ranney will discuss new features and show examples of the new functionality, which extend the platform’s usability, integration and scalability

 

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Graphs in Statistical Analysis

December 11, 2011 By 1 Comment

One of the seminal papers establishing the importance of data visualization (as it is now called) was the 1973 paper by F J Anscombe in http://www.sjsu.edu/faculty/gerstman/StatPrimer/anscombe1973.pdf

It has probably the most elegant introduction to an advanced statistical analysis paper that I have ever seen-

1. Usefulness of graphs

Most textbooks on statistical methods, and most statistical computer programs, pay too little attention to graphs. Few of us escape being indoctrinated with these notions:

(1) numerical calculations are exact, but graphs are rough;

(2) for any particular kind of statistical data there is just one set of calculations constituting a correct statistical analysis;

(3) performing intricate calculations is virtuous, whereas actually looking at the data is cheating.

A computer should make both calculations and graphs. Both sorts of output should be studied; each will contribute to understanding.

Of course the dataset makes it very very interesting for people who dont like graphical analysis too much.

From http://en.wikipedia.org/wiki/Anscombe%27s_quartet

 The x values are the same for the first three datasets.

Anscombe’s Quartet
I II III IV
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89

For all four datasets:

Property Value
Mean of x in each case 9 exact
Variance of x in each case 11 exact
Mean of y in each case 7.50 (to 2 decimal places)
Variance of y in each case 4.122 or 4.127 (to 3 d.p.)
Correlation between x and y in each case 0.816 (to 3 d.p.)
Linear regression line in each case y = 3.00 + 0.500x (to 2 d.p. and 3 d.p. resp.)
But see the graphical analysis -
While R has always been great in emphasizing graphical analysis, thanks in part due to work by H Wickham and others, SAS products and  language has also modified its approach at http://www.sas.com/technologies/analytics/statistics/datadiscovery/
 SAS Visual Data Discovery combines top-selling SAS products (Base SAS, SAS/STAT® and SAS/GRAPH®), along with two interfaces (SAS® Enterprise Guide® for guided tasks and batch analysis and JMP® software for discovery and exploratory analysis).
 and  ODS Statistical Graphs at
While ODS Statistical graphs is still not as smooth as say R’s GGPLOT2 http://tinyurl.com/ggplot2-book, it still is a progressive step
Pretty graphs make for better decisions too !

 

 

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Jobs in Analytics

December 1, 2011 By Leave a Comment

Message from Mary Parker /Recruiter WPS inc

(Disclosure- I have no biz relationship with them)

—————————————————————————————-

 

11.30.11

Review WPS searches below and contact me if you would like to have a confidential conversation.

Full position descriptions at http://www.analyticsearches.com/wps-search-current-positions/ .

Submit resume/ questions to mary@analyticsearches.com.

All referrals are appreciated and earn you $1000.00 each when hired.
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Business Development Manager Data Analytics/Remote/Salary to $125K & Commission

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Submit resume or questions to mary@analyticsearches.com.

http://www.analyticsearches.com

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