I interviewed Angoss in depth here at http://www.decisionstats.com/interview-eberhard-miethke-and-dr-mamdouh-refaat-angoss-software/
Well they just announced a predictive analytics in the cloud.
KnowledgeCLOUD™ solutions deliver predictive analytics in the Cloud to help businesses gain competitive advantage in the areas of sales, marketing and risk management by unlocking the predictive power of their customer data.
KnowledgeCLOUD clients experience rapid time to value and reduced IT investment, and enjoy the benefits of Angoss’ industry leading predictive analytics – without the need for highly specialized human capital and technology.
KnowledgeCLOUD solutions serve clients in the asset management, insurance, banking, high tech, healthcare and retail industries. Industry solutions consist of a choice of analytical modules:
KnowledgeCLOUD solutions are delivered via KnowledgeHUB™, a secure, scalable cloud-based analytical platform together with supporting deployment processes and professional services that deliver predictive analytics to clients in a hosted environment. Angoss industry leading predictive analytics technology is employed for the development of models and deployment of solutions.
Angoss’ deep analytics and domain expertise guarantees effectiveness – all solutions are back-tested for accuracy against historical data prior to deployment. Best practices are shared throughout the service to optimize your processes and success. Finely tuned client engagement and professional services ensure effective change management and program adoption throughout your organization.
For businesses looking to gain a competitive edge and put their data to work, Angoss is the ideal partner.
Hmm. Analytics in the cloud . Reduce hardware costs. Reduce software costs . Increase profitability margins.
My favorite professor in North Carolina who calls cloud as a time sharing, are you listening Professor?
Here is an interview with Kelci Miclaus, a researcher working with the JMP division of the SAS Institute, in which she demonstrates examples of how the R programming language is a great hit with JMP customers who like to be flexible.
Ajay- How has JMP been using integration with R? What has been the feedback from customers so far? Is there a single case study you can point out where the combination of JMP and R was better than any one of them alone?
Kelci- Feedback from customers has been very positive. Some customers are using JMP to foster collaboration between SAS and R modelers within their organizations. Many are using JMP’s interactive visualization to complement their use of R. Many SAS and JMP users are using JMP’s integration with R to experiment with more bleeding-edge methods not yet available in commercial software. It can be used simply to smooth the transition with regard to sending data between the two tools, or used to build complete custom applications that take advantage of both JMP and R.
One customer has been using JMP and R together for Bayesian analysis. He uses R to create MCMC chains and has found that JMP is a great tool for preparing the data for analysis, as well as displaying the results of the MCMC simulation. For example, the Control Chart platform and the Bubble Plot platform in JMP can be used to quickly verify convergence of the algorithm. The use of both tools together can increase productivity since the results of an analysis can be achieved faster than through scripting and static graphics alone.
I, along with a few other JMP developers, have written applications that use JMP scripting to call out to R packages and perform analyses like multidimensional scaling, bootstrapping, support vector machines, and modern variable selection methods. These really show the benefit of interactive visual analysis of coupled with modern statistical algorithms. We’ve packaged these scripts as JMP add-ins and made them freely available on our JMP User Community file exchange. Customers can download them and now employ these methods as they would a regular JMP platform. We hope that our customers familiar with scripting will also begin to contribute their own add-ins so a wider audience can take advantage of these new tools.
Ajay- Are there plans to extend JMP integration with other languages like Python?
Kelci- We do have plans to integrate with other languages and are considering integrating with more based on customer requests. Python has certainly come up and we are looking into possibilities there.
Ajay- How is R a complimentary fit to JMP’s technical capabilities?
Kelci- R has an incredible breadth of capabilities. JMP has extensive interactive, dynamic visualization intrinsic to its largely visual analysis paradigm, in addition to a strong core of statistical platforms. Since our brains are designed to visually process pictures and animated graphs more efficiently than numbers and text, this environment is all about supporting faster discovery. Of course, JMP also has a scripting language (JSL) allowing you to incorporate SAS code, R code, build analytical applications for others to leverage SAS, R and other applications for users who don’t code or who don’t want to code.
JSL is a powerful scripting language on its own. It can be used for dialog creation, automation of JMP statistical platforms, and custom graphic scripting. In other ways, JSL is very similar to the R language. It can also be used for data and matrix manipulation and to create new analysis functions. With the scripting capabilities of JMP, you can create custom applications that provide both a user interface and an interactive visual back-end to R functionality. Alternatively, you could create a dashboard using statistical and/or graphical platforms in JMP to explore the data and with the click of a button, send a portion of the data to R for further analysis.
Another JMP feature that complements R is the add-in architecture, which is similar to how R packages work. If you’ve written a cool script or analysis workflow, you can package it into a JMP add-in file and send it to your colleagues so they can easily use it.
Ajay- What is the official view on R from your organization? Do you think it is a threat, or a complimentary product or another statistical platform that coexists with your offerings?
Kelci- Most definitely, we view R as complimentary. R contributors are providing a tremendous service to practitioners, allowing them to try a wide variety of methods in the pursuit of more insight and better results. The R community as a whole is providing a valued role to the greater analytical community by focusing attention on newer methods that hold the most promise in so many application areas. Data analysts should be encouraged to use the tools available to them in order to drive discovery and JMP can help with that by providing an analytic hub that supports both SAS and R integration.
Ajay- While you do use R, are there any plans to give back something to the R community in terms of your involvement and participation (say at useR events) or sponsoring contests.
Kelci- We are certainly open to participating in useR groups. At Predictive Analytics World in NY last October, they didn’t have a local useR group, but they did have a Predictive Analytics Meet-up group comprised of many R users. We were happy to sponsor this. Some of us within the JMP division have joined local R user groups, myself included. Given that some local R user groups have entertained topics like Excel and R, Python and R, databases and R, we would be happy to participate more fully here. I also hope to attend the useR! annual meeting later this year to gain more insight on how we can continue to provide tools to help both the JMP and R communities with their work.
We are also exploring options to sponsor contests and would invite participants to use their favorite tools, languages, etc. in pursuit of the best model. Statistics is about learning from data and this is how we make the world a better place.
About- Kelci Miclaus
Kelci is a research statistician developer for JMP Life Sciences at SAS Institute. She has a PhD in Statistics from North Carolina State University and has been using SAS products and R for several years. In addition to research interests in statistical genetics, clinical trials analysis, and multivariate analysis/visualization methods, Kelci works extensively with JMP, SAS, and R integration.
The Analytics 2011 Conference Series combines the power of SAS’s M2010 Data Mining Conference and F2010 Business Forecasting Conference into one conference covering the latest trends and techniques in the field of analytics. Analytics 2011 Conference Series brings the brightest minds in the field of analytics together with hundreds of analytics practitioners. Join us as these leading conferences change names and locations. At Analytics 2011, you’ll learn through a series of case studies, technical presentations and hands-on training. If you are in the field of analytics, this is one conference you can’t afford to miss.
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I am still testing this out.
But if you know bit more about make and .compile in Ubuntu check out
I loved the humorous introduction
Dap is a small statistics and graphics package based on C. Version 3.0 and later of Dap can read SBS programs (based on the utterly famous, industry standard statistics system with similar initials – you know the one I mean)! The user wishing to perform basic statistical analyses is now freed from learning and using C syntax for straightforward tasks, while retaining access to the C-style graphics and statistics features provided by the original implementation. Dap provides core methods of data management, analysis, and graphics that are commonly used in statistical consulting practice (univariate statistics, correlations and regression, ANOVA, categorical data analysis, logistic regression, and nonparametric analyses).
Anyone familiar with the basic syntax of C programs can learn to use the C-style features of Dap quickly and easily from the manual and the examples contained in it; advanced features of C are not necessary, although they are available. (The manual contains a brief introduction to the C syntax needed for Dap.) Because Dap processes files one line at a time, rather than reading entire files into memory, it can be, and has been, used on data sets that have very many lines and/or very many variables.
I wrote Dap to use in my statistical consulting practice because the aforementioned utterly famous, industry standard statistics system is (or at least was) not available on GNU/Linux and costs a bundle every year under a lease arrangement. And now you can run programs written for that system directly on Dap! I was generally happy with that system, except for the graphics, which are all but impossible to use, but there were a number of clumsy constructs left over from its ancient origins.
Copyright © 2001, 2002, 2003, 2004 Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA
sounds too good to be true- GNU /DAP joins WPS workbench and Dulles Open’s Carolina as the third SAS language compiler (besides the now defunct BASS software) see http://en.wikipedia.org/wiki/SAS_language#Controversy
Dap was written to be a free replacement for SAS, but users are assumed to have a basic familiarity with the C programming language in order to permit greater flexibility. Unlike R it has been designed to be used on large data sets.
It has been designed so as to cope with very large data sets; even when the size of the data exceeds the size of the computer’s memory
- R courses from Statistics.com (revolutionanalytics.com)
- Categorical Data Analysis for the Behavioral and Social Sciences (psypress.com)
- Skills of a good data miner (zyxo.wordpress.com)
- GNU Octave 3.4 has just been released (gnu.org)
- Revolution R Enterprise 4.2 now available (revolutionanalytics.com)
Tableau which has been making waves recntly with its great new data visualization tool announced a partner with my old friends at AsterData. Its really cool piece of data vis and very very fast on the desktop- so I can imagine what speed it can help with AsterData’s MPP Row and Column Zingbang AND Parallel Analytical Functions
Tableau and AsterData also share the common Stanfordian connection (but it seems software is divided quite equally between Stanford, Hardvard Dropouts and North Carolina )
It remains to be seen in this announcement how much each company can leverage the partnership or whether it turns like the SAS Institute- AsterData partnership last year or whether it is just to announce connectors in their software to talk to each other.
See a Tableau vis at
AsterData remains the guys with the potential but I would be wrong to say MapReduce–SQL is as hot in December 2010 as it was in June 2009- and the elephant in the room would be Hadoop. That and Google’s continued shyness from encashing its principal comptency of handling Big Data (but hush – I signed a NDA with the Google Prediction API– so things maaaay change very rapidly on ahem that cloud)
Disclaimer- AsterData was my internship sponsor during my winter training while at Univ of Tenn.
- Aster Data and Cloudera Partner to Couple Industry-leading Analytical Database and Hadoop Solutions (prweb.com)
- Aster Data Hosting Big Data Insights Summit 2010, Chicago to Highlight Innovative New Solutions and Trends in Big Data Management and Advanced Analytics (prweb.com)
- Partnering with Cloudera (dbms2.com)
- Exclusive Interview: Quentin Gallivan, Aster Data (arnoldit.com)
- Aster Data Raises Another $30 Million To Help Manage Big Data (techcrunch.com)
- WikiLeaks Cable Gate: the Visualizations and the Infographics (infosthetics.com)