#statistical computing

Interview Tobias Verbeke Open Analytics #rstats #startups

Here is an interview with Tobias Verbeke, Managing Director of Open Analytics (http://www.openanalytics.eu/). Open Analytics is doing cutting edge work with R in the enterprise software space. Ajay- Describe your career journey including your involvement with Open Source and R. What things enticed you to try R?

Tobias-I discovered the free software foundation while still at university and spent…

Using Windows Azure Machine Learning as a service with R #rstats

A Brief Tutorial I wrote by playing with the software at manage.windowsazure.com

Using Windows Azure Machine Learning as a service with R #rstats from Ajay Ohri

Worldwide IT spending will continue to grow in 2013 according to Forrester research and self-service computing and analytics is one driver for the growth. Self-service statistical computing tools are also a top trend of business intelligence in 2013 based on some predictions. Data from various aspects show that self-service statistical computing and analytics tools are in growing need. Even some big companies shifted their focus to self-service tools and Microsoft is a typical example. But why self-service statistical computing software is more and more popular? What makes them so important? One excellence of self-service tools is that they enable business users to leverage data insights for decision making with unprecedented efficiency. Self-service data computing and analytics tools are developed for the users of all levels. Since some users may not be tech-savvy, traditional data handling software like SQL is too difficult for them. Self-service business computing and analytics are very agile and flexible which make business users’ work very convenient. This is what self-service data computing tools like esProc and esCalc are great for. Besides, self-service statistical computing software is visual and easy to use. Business users can interact with data easily and effectively without the involvement of IT department. That’s to say, business users put everything in their own hands with self-service computing and analysis tools.

In addition, self-service business computing tools often move faster than IT. In the digital world, data is generated at any time and fast results allow them to make quick reaction to the ever-changing market. Since self-service statistical computing tools don’t need modeling beforehand, business users can run ad hoc queries and build their own reports, making work more nimble. At the same time, the pressure of IT staff has been taken off and leaves them more time to focus on other value-added tasks as a help for self-service computing and analytics.