2012 Second Presidential Debate WordCloud

Michigan Lean Startup Conf. Twitter Visualizations

Today I am out at the Michigan Lean Startup Conference in Grand Rapids, MI. The conference is put on by Momentum…catch us on Twitter @solidlogictech

Our new CIO, Michael Bommarito, created a couple quick visualizations of the activity of the Twitter hashtag for the conference #leanstartupmi. We used a combination of Python and R code to do this. We’ll update the charts after the conference concludes.

Here they are:



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When will R overtake SPSS and SAS?

Summary:

R (formally ‘The R Project for Statistical Computing’) is growing quickly in usage and the user base. Based on some recently published forecasts R will overtake SAS & SPSS around 2015.

SLTI Commentary:

We use R for many different items and just got back from the R in Finance 2012 conference last week. Check out our post about thoughts on it here. The findings about R possibly overtaking more commercial languages are interesting, but its a bit of an apples to oranges comparison – R is viewed, in a general sense, as free vs. SAS & SPSS which are commercial packages.  I would be more interested to see some kind of market share or other metrics to show the change occurring in larger commercial installations. We met some of the people from Revolution Analytics last week and are very impressed with their solution – They should do a lot to increase the use of R within the enterprise.

Any ideas on if or when R will overtake either SAS or SPSS in commercial use?

Source:

http://r4stats.com/2012/05/09/beginning-of-the-end/ & http://blog Discover More.revolutionanalytics.com/2012/05/how-long-before-r-overtakes-sas-and-spss.html

RinFinance Conference Thoughts

Thursday and Friday of this week our new CIO, Michael Bommarito and I were in Chicago for the R in Finance conference. The conference was great, with a lot of very well done presentations. The R project has come a long way and is growing quickly. We use the R language for some of the more complex analytical projects we work on with great results. While the conference subject matter was very specific to the quantitative finance field, there were many things that carry over into other fields. A couple of the main ones are below:

Business Intelligence Labor Market

The Business Intelligence (BI) labor pool for people with skills in Big Data, Business Intelligence and enterprise level statistics and analytics area looks to be small, relative to other IT fields, and seems to be centered within specific geographic areas (NYC, Chicago, Silicon Valley, etc.). The skilled people are in high demand now and wages are up across the board. If your company is outside of these areas or not a large operation with a large budget for BI your options are fairly limited and generally priced accordingly. This tightness is in part due to the large growth this area is seeing, but also the newness of the field and the speed of innovation currently seen.

Commercial Open Source Software

The R project, being an open source project has a wide spectrum of users. At the conference there were people from large, leading hedge funds and investment firms to students and hobbyists. This wide user base seems to be unique to open source software and even more unique is the fact that it all seems to work so well and support different requirements well. Quick example….a student doesn’t have the money to pay for an enterprise level BI software package so he/she doesn’t use it. He/she may use a free or open source alternative product click resources. The enterprise customer generally won’t use an open source product without dedicated technical support or  community support, unless they have in-house talent to fix issues that may come up. This can leave the software project in a bind because of the disparity between the users and then people that would pay money to sustain development.

One solution to this is commercial open source software. Some easy examples of this are Red Hat Linux and MySQL (now part of Oracle). The commercial company gets a potentially large set of available labor to develop and work on the products and the students have a job market open for them with their new skills. The end client ends up with a additional layers of ‘insurance’ and support – from their chosen consulting company, the commercial company supporting the product, and the original the large open source community of free users – generally at a lower cost than in-house developed or proprietary solutions.

This is likely one of the reasons that we’ve seen large growth in the market share of commercial open source software in recent years.

Image courtesy of http://www.flickr.com/photos/opensourceway/5392982007/sizes/o/in/photostream/

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