RevoDeployR and commercial BI using R and R based cloud computing using Open CPU

Revolution Analytics has of course had RevoDeployR, and in a  webinar strive to bring it back to center spotlight.

BI is a good lucrative market, and visualization is a strength in R, so it is matter of time before we have more R based BI solutions. I really liked the two slides below for explaining RevoDeployR better to newbies like me (and many others!)

Integrating R into 3rd party and Web applications using RevoDeployR

Please click here to download the PDF.

Here are some additional links that may be of interest to you:


( I still think someone should make a commercial version of Jeroen Oom’s web interfaces and Jeff Horner’s web infrastructure (see below) for making customized Business Intelligence (BI) /Data Visualization solutions , UCLA and Vanderbilt are not exactly Stanford when it comes to deploying great academic solutions in the startup-tech world). I kind of think Google or someone at Revolution  should atleast dekko OpenCPU as a credible cloud solution in R.

I still cant figure out whether Revolution Analytics has a cloud computing strategy and Google seems to be working mysteriously as usual in broadening access to the Google Compute Cloud to the rest of R Community.

Open CPU  provides a free and open platform for statistical computing in the cloud. It is meant as an open, social analysis environment where people can share and run R functions and objects. For more details, visit the websit:

and esp see

Jeff Horner’s

Jerooen Oom’s

Interview John Myles White , Machine Learning for Hackers

Here is an interview with one of the younger researchers  and rock stars of the R Project, John Myles White,  co-author of Machine Learning for Hackers.

Ajay- What inspired you guys to write Machine Learning for Hackers. What has been the public response to the book. Are you planning to write a second edition or a next book?

John-We decided to write Machine Learning for Hackers because there were so many people interested in learning more about Machine Learning who found the standard textbooks a little difficult to understand, either because they lacked the mathematical background expected of readers or because it wasn’t clear how to translate the mathematical definitions in those books into usable programs. Most Machine Learning books are written for audiences who will not only be using Machine Learning techniques in their applied work, but also actively inventing new Machine Learning algorithms. The amount of information needed to do both can be daunting, because, as one friend pointed out, it’s similar to insisting that everyone learn how to build a compiler before they can start to program. For most people, it’s better to let them try out programming and get a taste for it before you teach them about the nuts and bolts of compiler design. If they like programming, they can delve into the details later.

We once said that Machine Learning for Hackers  is supposed to be a chemistry set for Machine Learning and I still think that’s the right description: it’s meant to get readers excited about Machine Learning and hopefully expose them to enough ideas and tools that they can start to explore on their own more effectively. It’s like a warmup for standard academic books like Bishop’s.
The public response to the book has been phenomenal. It’s been amazing to see how many people have bought the book and how many people have told us they found it helpful. Even friends with substantial expertise in statistics have said they’ve found a few nuggets of new information in the book, especially regarding text analysis and social network analysis — topics that Drew and I spend a lot of time thinking about, but are not thoroughly covered in standard statistics and Machine Learning  undergraduate curricula.
I hope we write a second edition. It was our first book and we learned a ton about how to write at length from the experience. I’m about to announce later this week that I’m writing a second book, which will be a very short eBook for O’Reilly. Stay tuned for details.

Ajay-  What are the key things that a potential reader can learn from this book?

John- We cover most of the nuts and bolts of introductory statistics in our book: summary statistics, regression and classification using linear and logistic regression, PCA and k-Nearest Neighbors. We also cover topics that are less well known, but are as important: density plots vs. histograms, regularization, cross-validation, MDS, social network analysis and SVM’s. I hope a reader walks away from the book having a feel for what different basic algorithms do and why they work for some problems and not others. I also hope we do just a little to shift a future generation of modeling culture towards regularization and cross-validation.

Ajay- Describe your journey as a science student up till your Phd. What are you current research interests and what initiatives have you done with them?

John-As an undergraduate I studied math and neuroscience. I then took some time off and came back to do a Ph.D. in psychology, focusing on mathematical modeling of both the brain and behavior. There’s a rich tradition of machine learning and statistics in psychology, so I got increasingly interested in ML methods during my years as a grad student. I’m about to finish my Ph.D. this year. My research interests all fall under one heading: decision theory. I want to understand both how people make decisions (which is what psychology teaches us) and how they should make decisions (which is what statistics and ML teach us). My thesis is focused on how people make decisions when there are both short-term and long-term consequences to be considered. For non-psychologists, the classic example is probably the explore-exploit dilemma. I’ve been working to import more of the main ideas from stats and ML into psychology for modeling how real people handle that trade-off. For psychologists, the classic example is the Marshmallow experiment. Most of my research work has focused on the latter: what makes us patient and how can we measure patience?

Ajay- How can academia and private sector solve the shortage of trained data scientists (assuming there is one)?

John- There’s definitely a shortage of trained data scientists: most companies are finding it difficult to hire someone with the real chops needed to do useful work with Big Data. The skill set required to be useful at a company like Facebook or Twitter is much more advanced than many people realize, so I think it will be some time until there are undergraduates coming out with the right stuff. But there’s huge demand, so I’m sure the market will clear sooner or later.

The changes that are required in academia to prepare students for this kind of work are pretty numerous, but the most obvious required change is that quantitative people need to be learning how to program properly, which is rare in academia, even in many CS departments. Writing one-off programs that no one will ever have to reuse and that only work on toy data sets doesn’t prepare you for working with huge amounts of messy data that exhibit shifting patterns. If you need to learn how to program seriously before you can do useful work, you’re not very valuable to companies who need employees that can hit the ground running. The companies that have done best in building up data teams, like LinkedIn, have learned to train people as they come in since the proper training isn’t typically available outside those companies.
Of course, on the flipside, the people who do know how to program well need to start learning more about theory and need to start to have a better grasp of basic mathematical models like linear and logistic regressions. Lots of CS students seem not to enjoy their theory classes, but theory really does prepare you for thinking about what you can learn from data. You may not use automata theory if you work at Foursquare, but you will need to be able to reason carefully and analytically. Doing math is just like lifting weights: if you’re not good at it right now, you just need to dig in and get yourself in shape.
John Myles White is a Phd Student in  Ph.D. student in the Princeton Psychology Department, where he studies human decision-making both theoretically and experimentally. Along with the political scientist Drew Conway, he is  the author of a book published by O’Reilly Media entitled “Machine Learning for Hackers”, which is meant to introduce experienced programmers to the machine learning toolkit. He is also working with Mark Hansenon a book for laypeople about exploratory data analysis.John is the lead maintainer for several R packages, including ProjectTemplate and log4r.

(TIL he has played in several rock bands!)

You can read more in his own words at his blog at
He can be contacted via social media at Google Plus at or twitter at is back from a dDOS

  1. Servers were okay, it was the DNS server that got swamped.
  2. I am sorry for the downtime- hopefully you didnt even notice
  3. I have faced challenges like domain name hijacking, sql injection , malicious WP plugins and thats why shifted to a professional hosting. I stand by my vendors and their professional judgement, moving away would mean the hackers won.
  4. This was very clever to swamp the DNS provider- my compliments to the tech talent behind this.
  5. You would think that every webmaster would have a back up plan in case his site went dDOS, but surprisingly even corporate websites dont have a back up (under attack) plan


Big Noise on Big Data

Increasingly Big Data is used in writing where Business Analytics was used, and data mining is thrown in as a word just to keep liberal art majors happy that they are reading a scientific article.

Some Big Words I have noticed in my Short life-

Big Data? High Performance Analytics? High Performance Computing ? Cloud Computing? Time Sharing? Data Mining? SEMMA? CRISP-DM? KDD? Business Intelligence? Business Analytics and Optimization? (pick a card and any card)

(or Just Moore’s Law catching up with the analytics)

Some examples-

Replace Big Data with Analytics in these articles and let me know if you can make out much of a difference

  • Big Data on Campus

  • From the man who famously said BI is dead, is now burying Business Analytics within the new buzzword , SAS CMO Jim Davis

How to transform big data from an obstacle into an asset

(Related- Is big data over hyped? by Jim Davis )

I am sure by 2015, Jim Davis, NYT and the merry men of analytics will find some other buzzwords to rally the troops. In the meantime, let me throw out the flag and call it Big  .

Latest R Journal

Including juicy stuff on using a cluster of Apple Machines for grid computing , seasonality forecasting (Yet Another Package For Time Series )

But I kind of liked Sumo too-

Sumo is a fully-functional web application template that exposes an authenticated user’s R session within java server pages.

Sumo: An Authenticating Web Application with an Embedded R Session by Timothy T. Bergsma and Michael S. Smith Abstract Sumo is a web application intended as a template for developers. It is distributed as a Java ‘war’ file that deploys automatically when placed in a Servlet container’s ‘webapps’
directory. If a user supplies proper credentials, Sumo creates a session-specific Secure Shell connection to the host and a user-specific R session over that connection. Developers may write dynamic server pages that make use of the persistent R session and user-specific file space.

and for Apple fanboys-

We created the xgrid package (Horton and Anoke, 2012) to provide a simple interface to this distributed computing system. The package facilitates use of an Apple Xgrid for distributed processing of a simulation with many independent repetitions, by simplifying job submission (or grid stuffing) and collation of results. It provides a relatively thin but useful layer between R and Apple’s ‘xgrid’ shell command, where the user constructs input scripts to be run remotely. A similar set of routines, optimized for parallel estimation of JAGS (just another Gibbs sampler) models is available within the runjags package (Denwood, 2010). However, with the exception of runjags, none of the previously mentioned packages support parallel computation over an Apple Xgrid.

Hmm I guess parallel computing enabled by Wifi on mobile phones would be awesome too ! So would be anything using iOS . See the rest of the R Journal at