Tag: design

Interview David Katz ,Dataspora /David Katz Consulting

Here is an interview with David Katz ,founder of David Katz Consulting (http://www.davidkatzconsulting.com/) and an analyst at the noted firm http://dataspora.com/. He is a featured speaker at Predictive Analytics World  http://www.predictiveanalyticsworld.com/sanfrancisco/2011/speakers.php#katz)

Ajay-  Describe your background working with analytics . How can we make analytics and science more attractive career options for young students

David- I had an interest in math from an early age, spurred by reading lots of science fiction with mathematicians and scientists in leading roles. I was fortunate to be at Harry and David (Fruit of the Month Club) when they were in the forefront of applying multivariate statistics to the challenge of targeting catalogs and other snail-mail offerings. Later I had the opportunity to expand these techniques to the retail sphere with Williams-Sonoma, who grew their retail business with the support of their catalog mailings. Since they had several catalog titles and product lines, cross-selling presented additional analytic challenges, and with the growth of the internet there was still another channel to consider, with its own dynamics.

After helping to found Abacus Direct Marketing, I became an independent consultant, which provided a lot of variety in applying statistics and data mining in a variety of settings from health care to telecom to credit marketing and education.

Students should be exposed to the many roles that analytics plays in modern life, and to the excitement of finding meaningful and useful patterns in the vast profusion of data that is now available.

Ajay-  Describe your most challenging project in 3 decades of experience in this field.

David- Hard to choose just one, but the educational field has been particularly interesting. Partnering with Olympic Behavior Labs, we’ve developed systems to help identify students who are most at-risk for dropping out of school to help target interventions that could prevent dropout and promote success.

Ajay- What do you think are the top 5 trends in analytics for 2011.

David- Big Data, Privacy concerns, quick response to consumer needs, integration of testing and analysis into business processes, social networking data.

Ajay- Do you think techniques like RFM and LTV are adequately utilized by organization. How can they be propagated further.

David- Organizations vary amazingly in how sophisticated or unsophisticated the are in analytics. A key factor in success as a consultant is to understand where each client is on this continuum and how well that serves their needs.

Ajay- What are the various software you have worked for in this field- and name your favorite per category.

David- I started out using COBOL (that dates me!) then concentrated on SAS for many years. More recently R is my favorite because of its coverage, currency and programming model, and it’s debugging capabilities.

Ajay- Independent consulting can be a strenuous job. What do you do to unwind?

David- Cycling, yoga, meditation, hiking and guitar.

Biography-

David Katz, Senior Analyst, Dataspora, and President, David Katz Consulting.

David Katz has been in the forefront of applying statistical models and database technology to marketing problems since 1980. He holds a Master’s Degree in Mathematics from the University of California, Berkeley. He is one of the founders of Abacus Direct Marketing and was previously the Director of Database Development for Williams-Sonoma.

He is the founder and President of David Katz Consulting, specializing in sophisticated statistical services for a variety of applications, with a special focus on the Direct Marketing Industry. David Katz has an extensive background that includes experience in all aspects of direct marketing from data mining, to strategy, to test design and implementation. In addition, he consults on a variety of data mining and statistical applications from public health to collections analysis. He has partnered with consulting firms such as Ernst and Young, Prediction Impact, and most recently on this project with Dataspora.

For more on David’s Session in Predictive Analytics World, San Fransisco on (http://www.predictiveanalyticsworld.com/sanfrancisco/2011/agenda.php#day2-16a)

Room: Salon 5 & 6
4:45pm – 5:05pm

Track 2: Social Data and Telecom 
Case Study: Major North American Telecom
Social Networking Data for Churn Analysis

A North American Telecom found that it had a window into social contacts – who has been calling whom on its network. This data proved to be predictive of churn. Using SQL, and GAM in R, we explored how to use this data to improve the identification of likely churners. We will present many dimensions of the lessons learned on this engagement.

Speaker: David Katz, Senior Analyst, Dataspora, and President, David Katz Consulting

Exhibit Hours
Monday, March 14th:10:00am to 7:30pm

Tuesday, March 15th:9:45am to 4:30pm

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R Commander Plugins-20 and growing!

First graphical user interface in 1973.
Image via Wikipedia
R Commander Extensions: Enhancing a Statistical Graphical User Interface by extending menus to statistical packages

R Commander ( see paper by Prof J Fox at http://www.jstatsoft.org/v14/i09/paper ) is a well known and established graphical user interface to the R analytical environment.
While the original GUI was created for a basic statistics course, the enabling of extensions (or plug-ins  http://www.r-project.org/doc/Rnews/Rnews_2007-3.pdf ) has greatly enhanced the possible use and scope of this software. Here we give a list of all known R Commander Plugins and their uses along with brief comments.

  1. DoE – http://cran.r-project.org/web/packages/RcmdrPlugin.DoE/RcmdrPlugin.DoE.pdf
  2. doex
  3. EHESampling
  4. epack- http://cran.r-project.org/web/packages/RcmdrPlugin.epack/RcmdrPlugin.epack.pdf
  5. Export- http://cran.r-project.org/web/packages/RcmdrPlugin.Export/RcmdrPlugin.Export.pdf
  6. FactoMineR
  7. HH
  8. IPSUR
  9. MAc- http://cran.r-project.org/web/packages/RcmdrPlugin.MAc/RcmdrPlugin.MAc.pdf
  10. MAd
  11. orloca
  12. PT
  13. qcc- http://cran.r-project.org/web/packages/RcmdrPlugin.qcc/RcmdrPlugin.qcc.pdf and http://cran.r-project.org/web/packages/qcc/qcc.pdf
  14. qual
  15. SensoMineR
  16. SLC
  17. sos
  18. survival-http://cran.r-project.org/web/packages/RcmdrPlugin.survival/RcmdrPlugin.survival.pdf
  19. SurvivalT
  20. Teaching Demos

Note the naming convention for above e plugins is always with a Prefix of “RCmdrPlugin.” followed by the names above
Also on loading a Plugin, it must be already installed locally to be visible in R Commander’s list of load-plugin, and R Commander loads the e-plugin after restarting.Hence it is advisable to load all R Commander plugins in the beginning of the analysis session.

However the notable E Plugins are
1) DoE for Design of Experiments-
Full factorial designs, orthogonal main effects designs, regular and non-regular 2-level fractional
factorial designs, central composite and Box-Behnken designs, latin hypercube samples, and simple D-optimal designs can currently be generated from the GUI. Extensions to cover further latin hypercube designs as well as more advanced D-optimal designs (with blocking) are planned for the future.
2) Survival- This package provides an R Commander plug-in for the survival package, with dialogs for Cox models, parametric survival regression models, estimation of survival curves, and testing for differences in survival curves, along with data-management facilities and a variety of tests, diagnostics and graphs.
3) qcc -GUI for  Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts
4) epack- an Rcmdr “plug-in” based on the time series functions. Depends also on packages like , tseries, abind,MASS,xts,forecast. It covers Log-Exceptions garch
and following Models -Arima, garch, HoltWinters
5)Export- The package helps users to graphically export Rcmdr output to LaTeX or HTML code,
via xtable() or Hmisc::latex(). The plug-in was originally intended to facilitate exporting Rcmdr
output to formats other than ASCII text and to provide R novices with an easy-to-use,
easy-to-access reference on exporting R objects to formats suited for printed output. The
package documentation contains several pointers on creating reports, either by using
conventional word processors or LaTeX/LyX.
6) MAc- This is an R-Commander plug-in for the MAc package (Meta-Analysis with
Correlations). This package enables the user to conduct a meta-analysis in a menu-driven,
graphical user interface environment (e.g., SPSS), while having the full statistical capabilities of
R and the MAc package. The MAc package itself contains a variety of useful functions for
conducting a research synthesis with correlational data. One of the unique features of the MAc
package is in its integration of user-friendly functions to complete the majority of statistical steps
involved in a meta-analysis with correlations. It uses recommended procedures as described in
The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009).

A query to help for ??Rcmdrplugins reveals the following information which can be quite overwhelming given that almost 20 plugins are now available-

RcmdrPlugin.DoE::DoEGlossary
Glossary for DoE terminology as used in
RcmdrPlugin.DoE
RcmdrPlugin.DoE::Menu.linearModelDesign
RcmdrPlugin.DoE Linear Model Dialog for
experimental data
RcmdrPlugin.DoE::Menu.rsm
RcmdrPlugin.DoE response surface model Dialog
for experimental data
RcmdrPlugin.DoE::RcmdrPlugin.DoE-package
R-Commander plugin package that implements
design of experiments facilities from packages
DoE.base, FrF2 and DoE.wrapper into the
R-Commander
RcmdrPlugin.DoE::RcmdrPlugin.DoEUndocumentedFunctions
Functions used in menus
RcmdrPlugin.doex::ranblockAnova
Internal RcmdrPlugin.doex objects
RcmdrPlugin.doex::RcmdrPlugin.doex-package
Install the DOEX Rcmdr Plug-In
RcmdrPlugin.EHESsampling::OpenSampling1
Internal functions for menu system of
RcmdrPlugin.EHESsampling
RcmdrPlugin.EHESsampling::RcmdrPlugin.EHESsampling-package
Help with EHES sampling
RcmdrPlugin.Export::RcmdrPlugin.Export-package
Graphically export objects to LaTeX or HTML
RcmdrPlugin.FactoMineR::defmacro
Internal RcmdrPlugin.FactoMineR objects
RcmdrPlugin.FactoMineR::RcmdrPlugin.FactoMineR
Graphical User Interface for FactoMineR
RcmdrPlugin.IPSUR::IPSUR-package
An IPSUR Plugin for the R Commander
RcmdrPlugin.MAc::RcmdrPlugin.MAc-package
Meta-Analysis with Correlations (MAc) Rcmdr
Plug-in
RcmdrPlugin.MAd::RcmdrPlugin.MAd-package
Meta-Analysis with Mean Differences (MAd) Rcmdr
Plug-in
RcmdrPlugin.orloca::activeDataSetLocaP
RcmdrPlugin.orloca: A GUI for orloca-package
(internal functions)
RcmdrPlugin.orloca::RcmdrPlugin.orloca-package
RcmdrPlugin.orloca: A GUI for orloca-package
RcmdrPlugin.orloca::RcmdrPlugin.orloca.es
RcmdrPlugin.orloca.es: Una interfaz grafica
para el paquete orloca
RcmdrPlugin.qcc::RcmdrPlugin.qcc-package
Install the Demos Rcmdr Plug-In
RcmdrPlugin.qual::xbara
Internal RcmdrPlugin.qual objects
RcmdrPlugin.qual::RcmdrPlugin.qual-package
Install the quality Rcmdr Plug-In
RcmdrPlugin.SensoMineR::defmacro
Internal RcmdrPlugin.SensoMineR objects
RcmdrPlugin.SensoMineR::RcmdrPlugin.SensoMineR
Graphical User Interface for SensoMineR
RcmdrPlugin.SLC::Rcmdr.help.RcmdrPlugin.SLC
RcmdrPlugin.SLC: A GUI for slc-package
(internal functions)
RcmdrPlugin.SLC::RcmdrPlugin.SLC-package
RcmdrPlugin.SLC: A GUI for SLC R package
RcmdrPlugin.sos::RcmdrPlugin.sos-package
Efficiently search R Help pages
RcmdrPlugin.steepness::Rcmdr.help.RcmdrPlugin.steepness
RcmdrPlugin.steepness: A GUI for
steepness-package (internal functions)
RcmdrPlugin.steepness::RcmdrPlugin.steepness
RcmdrPlugin.steepness: A GUI for steepness R
package
RcmdrPlugin.survival::allVarsClusters
Internal RcmdrPlugin.survival Objects
RcmdrPlugin.survival::RcmdrPlugin.survival-package
Rcmdr Plug-In Package for the survival Package
RcmdrPlugin.TeachingDemos::RcmdrPlugin.TeachingDemos-package
Install the Demos Rcmdr Plug-In

 

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Interview Ajay Ohri Decisionstats.com with DMR

From-

http://www.dataminingblog.com/data-mining-research-interview-ajay-ohri/

Here is the winner of the Data Mining Research People Award 2010: Ajay Ohri! Thanks to Ajay for giving some time to answer Data Mining Research questions. And all the best to his blog, Decision Stat!

Data Mining Research (DMR): Could you please introduce yourself to the readers of Data Mining Research?

Ajay Ohri (AO): I am a business consultant and writer based out of Delhi- India. I have been working in and around the field of business analytics since 2004, and have worked with some very good and big companies primarily in financial analytics and outsourced analytics. Since 2007, I have been writing my blog at http://decisionstats.com which now has almost 10,000 views monthly.

All in all, I wrote about data, and my hobby is also writing (poetry). Both my hobby and my profession stem from my education ( a masters in business, and a bachelors in mechanical engineering).

My research interests in data mining are interfaces (simpler interfaces to enable better data mining), education (making data mining less complex and accessible to more people and students), and time series and regression (specifically ARIMAX)
In business my research interests software marketing strategies (open source, Software as a service, advertising supported versus traditional licensing) and creation of technology and entrepreneurial hubs (like Palo Alto and Research Triangle, or Bangalore India).

DMR: I know you have worked with both SAS and R. Could you give your opinion about these two data mining tools?

AO: As per my understanding, SAS stands for SAS language, SAS Institute and SAS software platform. The terms are interchangeably used by people in industry and academia- but there have been some branding issues on this.
I have not worked much with SAS Enterprise Miner , probably because I could not afford it as business consultant, and organizations I worked with did not have a budget for Enterprise Miner.
I have worked alone and in teams with Base SAS, SAS Stat, SAS Access, and SAS ETS- and JMP. Also I worked with SAS BI but as a user to extract information.
You could say my use of SAS platform was mostly in predictive analytics and reporting, but I have a couple of projects under my belt for knowledge discovery and data mining, and pattern analysis. Again some of my SAS experience is a bit dated for almost 1 year ago.

I really like specific parts of SAS platform – as in the interface design of JMP (which is better than Enterprise Guide or Base SAS ) -and Proc Sort in Base SAS- I guess sequential processing of data makes SAS way faster- though with computing evolving from Desktops/Servers to even cheaper time shared cloud computers- I am not sure how long Base SAS and SAS Stat can hold this unique selling proposition.

I dislike the clutter in SAS Stat output, it confuses me with too much information, and I dislike shoddy graphics in the rendering output of graphical engine of SAS. Its shoddy coding work in SAS/Graph and if JMP can give better graphics why is legacy source code preventing SAS platform from doing a better job of it.

I sometimes think the best part of SAS is actually code written by Goodnight and Sall in 1970’s , the latest procs don’t impress me much.

SAS as a company is something I admire especially for its way of treating employees globally- but it is strange to see the rest of tech industry not following it. Also I don’t like over aggression and the SAS versus Rest of the Analytics /Data Mining World mentality that I sometimes pick up when I deal with industry thought leaders.

I think making SAS Enterprise Miner, JMP, and Base SAS in a completely new web interface priced at per hour rates is my wishlist but I guess I am a bit sentimental here- most data miners I know from early 2000’s did start with SAS as their first bread earning software. Also I think SAS needs to be better priced in Business Intelligence- it seems quite cheap in BI compared to Cognos/IBM but expensive in analytical licensing.

If you are a new stats or business student, chances are – you may know much more R than SAS today. The shift in education at least has been very rapid, and I guess R is also more of a platform than a analytics or data mining software.

I like a lot of things in R- from graphics, to better data mining packages, modular design of software, but above all I like the can do kick ass spirit of R community. Lots of young people collaborating with lots of young to old professors, and the energy is infectious. Everybody is a CEO in R ’s world. Latest data mining algols will probably start in R, published in journals.

Which is better for data mining SAS or R? It depends on your data and your deadline. The golden rule of management and business is -it depends.

Also I have worked with a lot of KXEN, SQL, SPSS.

DMR: Can you tell us more about Decision Stats? You have a traffic of 120′000 for 2010. How did you reach such a success?

AO: I don’t think 120,000 is a success. Its not a failure. It just happened- the more I wrote, the more people read.In 2007-2008 I used to obsess over traffic. I tried SEO, comments, back linking, and I did some black hat experimental stuff. Some of it worked- some didn’t.

In the end, I started asking questions and interviewing people. To my surprise, senior management is almost always more candid , frank and honest about their views while middle managers, public relations, marketing folks can be defensive.

Social Media helped a bit- Twitter, Linkedin, Facebook really helped my network of friends who I suppose acted as informal ambassadors to spread the word.
Again I was constrained by necessity than choices- my middle class finances ( I also had a baby son in 2007-my current laptop still has some broken keys :) – by my inability to afford traveling to conferences, and my location Delhi isn’t really a tech hub.

The more questions I asked around the internet, the more people responded, and I wrote it all down.

I guess I just was lucky to meet a lot of nice people on the internet who took time to mentor and educate me.

I tried building other websites but didn’t succeed so i guess I really don’t know. I am not a smart coder, not very clever at writing but I do try to be honest.

Basic economics says pricing is proportional to demand and inversely proportional to supply. Honest and candid opinions have infinite demand and an uncertain supply.

DMR: There is a rumor about a R book you plan to publish in 2011 :-) Can you confirm the rumor and tell us more?

AO: I just signed a contract with Springer for ” R for Business Analytics”. R is a great software, and lots of books for statistically trained people, but I felt like writing a book for the MBAs and existing analytics users- on how to easily transition to R for Analytics.

Like any language there are tricks and tweaks in R, and with a focus on code editors, IDE, GUI, web interfaces, R’s famous learning curve can be bent a bit.

Making analytics beautiful, and simpler to use is always a passion for me. With 3000 packages, R can be used for a lot more things and a lot more simply than is commonly understood.
The target audience however is business analysts- or people working in corporate environments.

Brief Bio-
Ajay Ohri has been working in the field of analytics since 2004 , when it was a still nascent emerging Industries in India. He has worked with the top two Indian outsourcers listed on NYSE,and with Citigroup on cross sell analytics where he helped sell an extra 50000 credit cards by cross sell analytics .He was one of the very first independent data mining consultants in India working on analytics products and domestic Indian market analytics .He regularly writes on analytics topics on his web site www.decisionstats.com and is currently working on open source analytical tools like R besides analytical software like SPSS and SAS.

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2011 Forecast-ying

Free twitter badge
Image via Wikipedia

I had recently asked some friends from my Twitter lists for their take on 2011, atleast 3 of them responded back with the answer, 1 said they were still on it, and 1 claimed a recent office event.

Anyways- I take note of the view of forecasting from

http://www.uiah.fi/projekti/metodi/190.htm

The most primitive method of forecasting is guessing. The result may be rated acceptable if the person making the guess is an expert in the matter.

Ajay- people will forecast in end 2010 and 2011. many of them will get forecasts wrong, some very wrong, but by Dec 2011 most of them would be writing forecasts on 2012. almost no one will get called on by irate users-readers- (hey you got 4 out of 7 wrong last years forecast!) just wont happen. people thrive on hope. so does marketing. in 2011- and before

and some forecasts from Tom Davenport’s The International Institute for Analytics (IIA) at

http://iianalytics.com/2010/12/2011-predictions-for-the-analytics-industry/

Regulatory and privacy constraints will continue to hamper growth of marketing analytics.

(I wonder how privacy and analytics can co exist in peace forever- one view is that model building can use anonymized data suppose your IP address was anonymized using a standard secret Coco-Cola formula- then whatever model does get built would not be of concern to you individually as your privacy is protected by the anonymization formula)

Anyway- back to the question I asked-

What are the top 5 events in your industry (events as in things that occured not conferences) and what are the top 3 trends in 2011.

I define my industry as being online technology writing- research (with a heavy skew on stat computing)

My top 5 events for 2010 were-

1) Consolidation- Big 5 software providers in BI and Analytics bought more, sued more, and consolidated more.  The valuations rose. and rose. leading to even more smaller players entering. Thus consolidation proved an oxy moron as total number of influential AND disruptive players grew.

 

2) Cloudy Computing- Computing shifted from the desktop but to the mobile and more to the tablet than to the cloud. Ipad front end with Amazon Ec2 backend- yup it happened.

3) Open Source grew louder- yes it got more clients. and more revenue. did it get more market share. depends on if you define market share by revenues or by users.

Both Open Source and Closed Source had a good year- the pie grew faster and bigger so no one minded as long their slices grew bigger.

4) We didnt see that coming –

Technology continued to surprise with events (thats what we love! the surprises)

Revolution Analytics broke through R’s Big Data Barrier, Tableau Software created a big Buzz,  Wikileaks and Chinese FireWalls gave technology an entire new dimension (though not universally popular one).

people fought wars on emails and servers and social media- unfortunately the ones fighting real wars in 2009 continued to fight them in 2010 too

5) Money-

SAP,SAS,IBM,Oracle,Google,Microsoft made more money than ever before. Only Facebook got a movie named on itself. Venture Capitalists pumped in money in promising startups- really as if in a hurry to park money before tax cuts expired in some countries.

 

2011 Top Three Forecasts

1) Surprises- Expect to get surprised atleast 10 % of the time in business events. As internet grows the communication cycle shortens, the hype cycle amplifies buzz-

more unstructured data  is created (esp for marketing analytics) leading to enhanced volatility

2) Growth- Yes we predict technology will grow faster than the automobile industry. Game changers may happen in the form of Chrome OS- really its Linux guys-and customer adaptability to new USER INTERFACES. Design will matter much more in technology on your phone, on your desktop and on your internet. Packaging sells.

False Top Trend 3) I will write a book on business analytics in 2011. yes it is true and I am working with A publisher. No it is not really going to be a top 3 event for anyone except me,publisher and lucky guys who read it.

3) Creating technology and technically enabling creativity will converge at an accelerated rate. use of widgets, guis, snippets, ide will ensure creative left brains can code easier. and right brains can design faster and better due to a global supply chain of techie and artsy professionals.

 

 

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Trying out Google Prediction API from R

Ubuntu Login
Image via Wikipedia

So I saw the news at NY R Meetup and decided to have a go at Prediction API Package (which first started off as a blog post at

http://onertipaday.blogspot.com/2010/11/r-wrapper-for-google-prediction-api.html

1)My OS was Ubuntu 10.10 Netbook

Ubuntu has a slight glitch plus workaround for installing the RCurl package on which the Google Prediction API is dependent- you need to first install this Ubuntu package for RCurl to install libcurl4-gnutls-dev

Once you install that using Synaptic,

Simply start R

2) Install Packages rjson and Rcurl using install.packages and choosing CRAN

Since GooglePredictionAPI is not yet on CRAN

,

3) Download that package from

https://code.google.com/p/google-prediction-api-r-client/downloads/detail?name=googlepredictionapi_0.1.tar.gz&can=2&q=

You need to copy this downloaded package to your “first library ” folder

When you start R, simply run

.libPaths()[1]

and thats the folder you copy the GooglePredictionAPI package  you downloaded.

5) Now the following line works

  1. Under R prompt,
    2. > install.packages("googlepredictionapi_0.1.tar.gz", repos=NULL, type="source")

6) Uploading data to Google Storage using the GUI (rather than gs util)

Just go to https://sandbox.google.com/storage/

and thats the Google Storage manager

Notes on Training Data-

Use a csv file

The first column is the score column (like 1,0 or prediction score)

There are no headers- so delete headers from data file and move the dependent variable to the first column  (Note I used data from the kaggle contest for R package recommendation at

http://kaggle.com/R?viewtype=data )

6) The good stuff:

Once you type in the basic syntax, the first time it will ask for your Google Credentials (email and password)

It then starts showing you time elapsed for training.

Now you can disconnect and go off (actually I got disconnected by accident before coming back in a say 5 minutes so this is the part where I think this is what happened is why it happened, dont blame me, test it for yourself) –

and when you come back (hopefully before token expires)  you can see status of your request (see below)

> library(rjson)
> library(RCurl)
Loading required package: bitops
> library(googlepredictionapi)
> my.model <- PredictionApiTrain(data="gs://numtraindata/training_data")
The request for training has sent, now trying to check if training is completed
Training on numtraindata/training_data: time:2.09 seconds
Training on numtraindata/training_data: time:7.00 seconds

7)

Note I changed the format from the URL where my data is located- simply go to your Google Storage Manager and right click on the file name for link address  ( https://sandbox.google.com/storage/numtraindata/training_data.csv)

to gs://numtraindata/training_data  (that kind of helps in any syntax error)

8) From the kind of high level instructions at  https://code.google.com/p/google-prediction-api-r-client/, you could also try this on a local file

Usage

## Load googlepredictionapi and dependent libraries
library(rjson)
library(RCurl)
library(googlepredictionapi)

## Make a training call to the Prediction API against data in the Google Storage.
## Replace MYBUCKET and MYDATA with your data.
my.model <- PredictionApiTrain(data="gs://MYBUCKET/MYDATA")

## Alternatively, make a training call against training data stored locally as a CSV file.
## Replace MYPATH and MYFILE with your data.
my.model <- PredictionApiTrain(data="MYPATH/MYFILE.csv")

At the time of writing my data was still getting trained, so I will keep you posted on what happens.

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RWui :Creating R Web Interfaces on the go

Here is a great R application created by http://sysbio.mrc-bsu.cam.ac.uk

R Wui for creating R Web Interfaces

its been there for some time now- but presumably R Apache is more well known.

From-

http://sysbio.mrc-bsu.cam.ac.uk/Rwui/tutorial/Rwui_Rnews_final.pdf

The web application Rwui is used to create web interfaces  for running R scripts. All the code is generated automatically so that a fully functional web interface for an R script can be downloaded and up and running in a matter of minutes.

Rwui is aimed at R script writers who have scripts that they want people unversed in R to use. The script writer uses Rwui to create a web application that will run their R script. Rwui allows the script writer to do this without them having to do any web application programming, because Rwui generates all the code for them.

The script writer designs the web application to run their R script by entering information on a sequence of web pages. The script writer then downloads the application they have created and installs it on their own server.

http://sysbio.mrc-bsu.cam.ac.uk/Rwui/tutorial/Technical_Report.pdf

The web application Rwui is used to create web interfaces
for running R scripts. All the code is generated
automatically so that a fully functional web interface
for an R script can be downloaded and up and running
in a matter of minutes.
Rwui is aimed at R script writers who have
scripts that they want people unversed in R to use.
The script writer uses Rwui to create a web application
that will run their R script. Rwui allows the
script writer to do this without them having to do
any web application programming, because Rwui
generates all the code for them.
The script writer designs the web application to
run their R script by entering information on a sequence
of web pages. The script writer then downloads
the application they have created and installs
it on their own server. This is a simple matter of
copying one le from the download onto the server.and http://sysbio.mrc-bsu.cam.ac.uk/Rwui/continue.do

Features of web applications created by Rwui

  1. Whole range of input items available if required – text boxes, checkboxes, file upload etc.
  2. Facility for uploading of an arbitrary number of files (for example, microarray replicates).
  3. Facility for grouping uploaded files (for example, into ‘Diseased’ and ‘Control’ microarray data files).
  4. Results files displayed on results page and available for download.
  5. Results files can be e-mailed to the user.
  6. Interactive results files using image maps.
  7. Repeat analyses with different parameters and data files – new results added to results list, as a link to the corresponding results page.
  8. Real time progress information (text or graphical) displayed when running the application.

Requirements

In order to use the completed web applications created by Rwui you will need:

  1. A Java webserver such as Tomcat version 5.5 or later.
  2. Java version 1.5
  3. R – a version compatible with your R script(s).

Using Rwui

Using Rwui to create a web application for an R script simply involves:

  1. Entering details about your Rscript on a sequence of web pages.
  2. Rwui is quite flexible so you can backtrack, edit and insert, as you design your application.
  3. Rwui then generates the web application, which is Java based and platform independent.
  4. The application can be downloaded either as a .zip or .tgz file.
  5. Unpacked, the download contains all the source code and a .war file.
  6. Once the .war file is copied to the Tomcat webapps directory, the application is ready to use.
  7. Application details are saved in an ‘application definition file’ for reuse and modification.
Interested-
go click and check out a new web app from http://sysbio.mrc-bsu.cam.ac.uk/Rwui/ in a matter of minutes
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Nice BI Tutorials

Tutorials screenshot.
Image via Wikipedia

Here is a set of very nice, screenshot enabled tutorials from SAP BI. They are a bit outdated (3 years old) but most of it is quite relevant- especially from a Tutorial Design Perspective –

Most people would rather see screenshot based step by step powerpoints, than cluttered or clever presentations , or even videos that force you to sit like a TV zombie. Unfortunately most tutorial presentations I see especially for BI are either slides with one or two points, that abruptly shift to “concepts” or videos that are atleast more than 10 minutes long. That works fine for scripting tutorials or hands on workshops, but cannot be reproduced for later instances of study.

The mode of tutorials especially for GUI software can vary, it may be Slideshare, Scribd, Google Presentation,Microsoft Powerpoint but a step by step screenshot by screenshot tutorial is much better for understanding than commando line jargon/ Youtub   Videos presentations, or Powerpoint with Points.

Have a look at these SAP BI 7 slideshares

and

Speaking of BI, the R Package called Brew is going to brew up something special especially combined with R Apache. However I wish R Apache, or R Web, or RServe had step by step install screenshot tutorials to increase their usage in Business Intelligence.

I tried searching for JMP GUI Tutorials too, but I believe putting all your content behind a registration wall is not so great. Do a Pareto Analysis of your training material, surely you can share a couple more tutorials without registration. It also will help new wanna-migrate users to get a test and feel for the installation complexities as well as final report GUI.

 

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