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I quite like Google’s monthly email on account activity. It is the Google way to offer free services, as well as treat users as special, that continues to command loyalty despite occasional exasperation with corporate thingies.
See this dashboard-
The medium range font shows persons sent/from statistics, and the color shades are done to empahsize or de-emphasize the metric
Colors used are black/grey, green and blue coincident with the Corporate Logo.
However some of the JS for visualizations need to be tweaked. Clearly the hover script ( an integral part of Dashboard design ) needs better elucidiation or formatting)
I would also venture my neck and suggest that rather than just monthly snapshots, atleast some way of comparing snapshots across periods or even the total time period be enabled- rather than be in seperate views. This may give the user a bit more analytical value.
Overall, a nice and simple dashboard which may be of some use to the business user who makes or views a lot of reports on online properties. Minimal and effective- and in keeping with Open Data- Data Liberation Principles. I guess Google is secure in the knowledge that users do not view time spent on Google services as a total waste , unlike some of the other more social ;) websites they spend time on.
Due to changes in Google APIs my earlier post on using Google Analytics in R is deprecated. Unfortunately it is still on top 10 results for Google results for Using Google Analytics with R.
That post is here http://decisionstats.com/2012/03/20/using-google-analytics-with-r/
A more updated R package on Google Analytics and R is here . https://github.com/skardhamar/rga
A better updated post on an easy to use tutorial on using Google Analytics with R using OAuth 2 playground is here.
- Set the Google analytics query parameters for preparing the request URI
- Get the access token from Oauth 2.0 Playground
- Retrieve and select the Profile
- Retrieving GA data
Note it is excellent for learning to use RJSON method as well. You can see the details on the Tatvic blog above.
Hat tip- Vignesh Prajapati
- (not provided): Using R and the Google Analytics API (r-bloggers.com)
Ajay- Why did you choose Rapid Miner and R? What were the other software alternatives you considered and discarded?
Analyst- We considered most of the other major players in statistics/data mining or enterprise BI. However, we found that the value proposition for an open source solution was too compelling to justify the premium pricing that the commercial solutions would have required. The widespread adoption of R and the variety of packages and algorithms available for it, made it an easy choice. We liked RapidMiner as a way to design structured, repeatable processes, and the ability to optimize learner parameters in a systematic way. It also handled large data sets better than R on 32-bit Windows did. The GUI, particularly when 5.0 was released, made it more usable than R for analysts who weren’t experienced programmers.
Ajay- What analytics do you do think Rapid Miner and R are best suited for?
Analyst- We use RM+R mainly for sports analysis so far, rather than for more traditional business applications. It has been quite suitable for that, and I can easily see how it would be used for other types of applications.
Ajay- Any experiences as an enterprise customer? How was the installation process? How good is the enterprise level support?
Analyst- Rapid-I has been one of the most responsive tech companies I’ve dealt with, either in my current role or with previous employers. They are small enough to be able to respond quickly to requests, and in more than one case, have fixed a problem, or added a small feature we needed within a matter of days. In other cases, we have contracted with them to add larger pieces of specific functionality we needed at reasonable consulting rates. Those features are added to the mainline product, and become fully supported through regular channels. The longer consulting projects have typically had a turnaround of just a few weeks.
Ajay- What challenges if any did you face in executing a pure open source analytics bundle ?
Analyst- As Rapid-I is a smaller company based in Europe, the availability of training and consulting in the USA isn’t as extensive as for the major enterprise software players, and the time zone differences sometimes slow down the communications cycle. There were times where we were the first customer to attempt a specific integration point in our technical environment, and with no prior experiences to fall back on, we had to work with Rapid-I to figure out how to do it. Compared to the what traditional software vendors provide, both R and RM tend to have sparse, terse, occasionally incomplete documentation. The situation is getting better, but still lags behind what the traditional enterprise software vendors provide.
Ajay- What are the things you can do in R ,and what are the things you prefer to do in Rapid Miner (comparison for technical synergies)
Analyst- Our experience has been that RM is superior to R at writing and maintaining structured processes, better at handling larger amounts of data, and more flexible at fine-tuning model parameters automatically. The biggest limitation we’ve had with RM compared to R is that R has a larger library of user-contributed packages for additional data mining algorithms. Sometimes we opted to use R because RM hadn’t yet implemented a specific algorithm. The introduction the R extension has allowed us to combine the strengths of both tools in a very logical and productive way.
In particular, extending RapidMiner with R helped address RM’s weakness in the breadth of algorithms, because it brings the entire R ecosystem into RM (similar to how Rapid-I implemented much of the Weka library early on in RM’s development). Further, because the R user community releases packages that implement new techniques faster than the enterprise vendors can, this helps turn a potential weakness into a potential strength. However, R packages tend to be of varying quality, and are more prone to go stale due to lack of support/bug fixes. This depends heavily on the package’s maintainer and its prevalence of use in the R community. So when RapidMiner has a learner with a native implementation, it’s usually better to use it than the R equivalent.
Udacity is a smaller player but welcome competition to Coursera. I think companies that have on demand learning programs should consider donating a course to these online education players (like SAS Institute for SAS , Revolution Analytics for R, SAP, Oracle for in-memory analytics etc)
Coursera is doing a superb job with huge number of free courses from notable professors. 111 courses!
Crowd Analytix- the Bangalore based Indian startup is moving fast in the
data scientist contest space (so watch out Kaggle!! )
Churn (loss of customers to competition) is a problem for telecom companies because it is more expensive to acquire a new customer than to keep your existing one from leaving. This contest is about enabling churn reduction using analytics.
To join, go to – http://www.crowdanalytix.com/contests/why-customer-churn/