The interpreted programming language Python has surged in popularity in recent years. Long beloved by system administrators and others who had good use for the way it made routine tasks easy to automate, it has gained traction in other sectors as well. In particular, it has become one of the most-used tools in the discipline of numerical computing and analysis. Being put to use for such heavy lifting has endowed the language with a great selection of powerful libraries and other tools that make it even more flexible. One upshot of this development has been that sophisticated business analysts have also come to see the language as a valuable tool for those own data analysis needs.

Greatly appreciated for its simplicity and elegance of syntax, Python makes an excellent first programming language for previously non-technical people. Many business analysts, in fact, have had success growing their skill sets in this way thanks to the language's tractability. Long beloved by specialized data scientists, the iPython interactive computing environment has also attracted great attention within the business analyst’s community. Its instant feedback and visualization options have made it easy for many analysts to become skilled Python programmers while doing valuable work along the way.

Using iPython and appropriate notebooks for it, for example, business analysts can easily make interactive use of such tools as cohort analysis and pivot tables. iPython makes it easy to benefit from real-time, interactive researches which produce immediately visible results, including charts and graphs suitable for use in other contexts. Through becoming familiar with this powerful interactive application, business analysts are also exposing themselves in a natural and productive way to the Python programming language itself.

Gaining proficiency with this language opens up further possibilities. While interactive analytic techniques are of great use to many business analysts, being able to create fully functioning, independent programs is of similar value. Becoming comfortable with Python allows analysts to tackle and plumb even larger data sets than would be possible through an interactive approach, as results can be allowed to accumulate over hours and days of processing time.

This ability can sometime allow business analysts to address the so-called "Big Data" questions that can otherwise seem the sole province of specialized data scientists. More important than this higher level of independence, perhaps, is the fact that this increased facility with data analysis and handling allows analysts to communicate more effectively with such stakeholders. Through learning a programming language which allows them to begin making independent inroads into such areas, business analysts gain a better perspective on these specialized domains, and this allows them to function as even more effective intermediaries.

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Who Are the Main Players in Big Data?

Although reports made in May 2010 indicate that Android had outsold Apple iPhones, more recent and current reports of the 2^nd quarter of 2011 made by National Purchase Diary (NPD) on Mobile Phone Track service, which listed the top five selling smartphones in the United States for the months of April-June of 2011, indicate that Apple's iPhone 4 and iPhone 3GS outsold other Android phones on the market in the U. S. for the third calendar quarter of 2011. This was true for the previous quarter of the same year; The iPhone 4 held the top spot.  The fact that the iPhone 4 claimed top spot does not come as a surprise to the analysts; rather, it is a testament to them of how well the iPhone is revered among consumers. The iPhone 3GS, which came out in 2009 outsold newer Android phones with higher screen resolutions and more processing power. The list of the five top selling smartphones is depicted below:

1. Apple iPhone 4
2. Apple iPhone 3GS
3. HTC EVO 4G
4. Motorola Droid 3
5. Samsung Intensity II[1]

Apple’s iPhone also outsold Android devices7.8:1 at AT&T’s corporate retail stores in December. A source inside the Apple company told The Mac Observer that those stores sold some 981,000 iPhones between December 1st and December 27^th 2011, and that the Apple device accounted for some 66% of all device sales during that period (see the pie figure below) . Android devices, on the other hand, accounted for just 8.5% of sales during the same period.

According to the report, AT&T sold approximately 981,000 iPhones through AT&T corporate stores in the first 27 days of December, 2011 while 126,000 Android devices were sold during the same period. Even the basic flip and slider phones did better than Android, with 128,000 units sold.[2] However, it is important to understand that this is a report for one particular environment at a particular period in time. As the first iPhone carrier in the world, AT&T has been the dominant iPhone carrier in the U.S. since day one, and AT&T has consistently claimed that the iPhone is its best selling device.

Chart courtesy of Mac Observer: http://www.macobserver.com/tmo/article/iphone_crushes_android_at_att_corporate_stores_in_december/

A more recent report posted in ismashphone.com, dated January 25 2012, indicated that Apple sold 37 million iPhones in Q4 2011.  It appears that the iPhone 4S really helped take Apple’s handset past competing Android phones. According to research firm Kantar Worldpanel ComTech, Apple’s U.S. smartphone marketshare has doubled to 44.9 percent.[3] Meanwhile, Android marketshare in the U.S. dropped slightly to 44.8 percent. This report means that the iPhone has edged just a little bit past Android in U.S. marketshare. This is occurred after Apple’s Q1 2012 conference call, which saw themselling 37 million handsets. Meanwhile, it’s reported that marketers of Android devices, such as Motorola Mobility, HTC and Sony Ericsson saw drops this quarter.

Memory management is always a priority in pretty much any programming language you would want to use. In the lower level languages such as C, there are a number of functions which help you manage the memory your application uses, but they are not the easiest to use. Some of the more modern programming languages such as Python, Ruby, Perl, and of course the subject of this article, Javascript all have a built in feature called garbage collection.

Garbage collection essentially means that the languages compiler will automatically free the memory being occupied by unused variables and objects, but there is no telling when this could occur. It is purely down to the compiler to decide when the garbage collection process should be initiated.