For many people, one of the most exciting and challenging career choices is computer programming. There are several ways that people can enter the computer programming profession; however, the most popular method has traditionally been the educational route through an educational institution of higher learning such as a college or technical school.

Even though many people think of computer programmers as individuals with a technical background, some programmers enter the computer programming profession without a structured technical background. In addition, after further investigation several interesting facts are uncovered when a profile of the best computer programmers is analyzed.

When observing how the top programmers in the profession work, there are four characteristics that tend to separate the top programmers from the average programmers. These four characteristics are:

1.Creativity.
2.Attention To Detail.
3.Learns New Things Quickly.
4.Works Well With Others.

Creativity.

Being a top computer programmer requires a combination of several unique qualities. One of these qualities is creativity. In its very essence, computer programming is about creating programs to accomplish specific tasks in the most efficient manner. The ability to develop computer code to accomplish tasks takes a certain level of creativity. The top computer programmers tend to have a great deal of creativity, and they have the desire to try things in a variety of ways to produce the best results for a particular situation.

Attention To Detail.

While creativity is important for top programmers an almost opposite quality is needed to produce great computer programs on a consistent basis, this quality is attention to detail. The very nature of computer programming requires the need to enter thousands of lines of computer programming code. What separates many top programmers from average programmers is the ability to enter these lines of code with a minimum amount of errors and just as importantly test the code to catch any unseen errors. Top computer programmers have the necessary attention to detail to successfully create and enter the necessary computer code project after project.

Learns New Things Quickly.

The technology field is constantly changing. Almost daily new technology innovations are being developed that require computer programmers to learn new technology or enhancements to current technology on a regular basis. The top computer programmers are able to learn new technology or enhancements quickly, and then they are able to apply what has been learned to their current and future programming projects in a seamless manner.

Works Well With Others.

There are several differences between top computer programmers and other programmers. However, one of the biggest differences is the ability to work well with others. By its very nature, computer programming requires programmers to spend a lot of time alone developing computer code, but the top computer programmers are able to excel at this aspect of computer programming along with being able to work well with other people.

Regarding computer programmers, the top programmers approach and handle their jobs differently than other programmers, and these differences set them apart from the other programmers. For any average programmers who have the desire to excel as a computer programmer, they must understand and embrace the characteristics of top programmers.

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Machine learning systems are equipped with artificial intelligence engines that provide these systems with the capability of learning by themselves without having to write programs to do so. They adjust and change programs as a result of being exposed to big data sets. The process of doing so is similar to the data mining concept where the data set is searched for patterns. The difference is in how those patterns are used. Data mining's purpose is to enhance human comprehension and understanding. Machine learning's algorithms purpose is to adjust some program's action without human supervision, learning from past searches and also continuously forward as it's exposed to new data.

The News Feed service in Facebook is an example, automatically personalizing a user's feed from his interaction with his or her friend's posts. The "machine" uses statistical and predictive analysis that identify interaction patterns (skipped, like, read, comment) and uses the results to adjust the News Feed output continuously without human intervention. 

Impact on Existing and Emerging Markets

The NBA is using machine analytics created by a California-based startup to create predictive models that allow coaches to better discern a player's ability. Fed with many seasons of data, the machine can make predictions of a player's abilities. Players can have good days and bad days, get sick or lose motivation, but over time a good player will be good and a bad player can be spotted. By examining big data sets of individual performance over many seasons, the machine develops predictive models that feed into the coach’s decision-making process when faced with certain teams or particular situations. 

General Electric, who has been around for 119 years is spending millions of dollars in artificial intelligence learning systems. Its many years of data from oil exploration and jet engine research is being fed to an IBM-developed system to reduce maintenance costs, optimize performance and anticipate breakdowns.

Over a dozen banks in Europe replaced their human-based statistical modeling processes with machines. The new engines create recommendations for low-profit customers such as retail clients, small and medium-sized companies. The lower-cost, faster results approach allows the bank to create micro-target models for forecasting service cancellations and loan defaults and then how to act under those potential situations. As a result of these new models and inputs into decision making some banks have experienced new product sales increases of 10 percent, lower capital expenses and increased collections by 20 percent. 

Emerging markets and industries

By now we have seen how cell phones and emerging and developing economies go together. This relationship has generated big data sets that hold information about behaviors and mobility patterns. Machine learning examines and analyzes the data to extract information in usage patterns for these new and little understood emergent economies. Both private and public policymakers can use this information to assess technology-based programs proposed by public officials and technology companies can use it to focus on developing personalized services and investment decisions.

Machine learning service providers targeting emerging economies in this example focus on evaluating demographic and socio-economic indicators and its impact on the way people use mobile technologies. The socioeconomic status of an individual or a population can be used to understand its access and expectations on education, housing, health and vital utilities such as water and electricity. Predictive models can then be created around customer's purchasing power and marketing campaigns created to offer new products. Instead of relying exclusively on phone interviews, focus groups or other kinds of person-to-person interactions, auto-learning algorithms can also be applied to the huge amounts of data collected by other entities such as Google and Facebook.

A warning

Traditional industries trying to profit from emerging markets will see a slowdown unless they adapt to new competitive forces unleashed in part by new technologies such as artificial intelligence that offer unprecedented capabilities at a lower entry and support cost than before. But small high-tech based companies are introducing new flexible, adaptable business models more suitable to new high-risk markets. Digital platforms rely on algorithms to host at a low cost and with quality services thousands of small and mid-size enterprises in countries such as China, India, Central America and Asia. These collaborations based on new technologies and tools gives the emerging market enterprises the reach and resources needed to challenge traditional business model companies.

Is it possible for anyone to give Microsoft a fair trial? The first half of 2012 is in the history books. Yet the firm still cannot seem to shake the public opinion as The Evil Empire that produces crap code.

I am in a unique position. I joined the orbit of Microsoft in 1973 after the Army decided it didn't need photographers flying around in helicopters in Vietnam anymore. I was sent to Fort Lewis and assigned to 9th Finance because I had a smattering of knowledge about computers. And the Army was going to a computerized payroll system.

Bill and Paul used the University of Washington's VAX PDP computer to create BASIC for the Altair computer. Certainly laughable by today's standards, it is the very roots of the home computer.

Microsoft became successful because it delivered what people wanted.

Python and Ruby, each with roots going back into the 1990s, are two of the most popular interpreted programming languages today. Ruby is most widely known as the language in which the ubiquitous Ruby on Rails web application framework is written, but it also has legions of fans that use it for things that have nothing to do with the web. Python is a big hit in the numerical and scientific computing communities at the present time, rapidly displacing such longtime stalwarts as R when it comes to these applications. It too, however, is also put to a myriad of other uses, and the two languages probably vie for the title when it comes to how flexible their users find them.

A Matter of Personality...

That isn't to say that there aren't some major, immediately noticeable, differences between the two programming tongues. Ruby is famous for its flexibility and eagerness to please; it is seen by many as a cleaned-up continuation of Perl's "Do What I Mean" philosophy, whereby the interpreter does its best to figure out the meaning of evening non-canonical syntactic constructs. In fact, the language's creator, Yukihiro Matsumoto, chose his brainchild's name in homage to that earlier language's gemstone-inspired moniker.

Python, on the other hand, takes a very different tact. In a famous Python Enhancement Proposal called "The Zen of Python," longtime Pythonista Tim Peters declared it to be preferable that there should only be a single obvious way to do anything. Python enthusiasts and programmers, then, generally prize unanimity of style over syntactic flexibility compared to those who choose Ruby, and this shows in the code they create. Even Python's whitespace-sensitive parsing has a feel of lending clarity through syntactical enforcement that is very much at odds with the much fuzzier style of typical Ruby code.

For example, Python's much-admired list comprehension feature serves as the most obvious way to build up certain kinds of lists according to initial conditions:

a = [x**3 for x in range(10,20)]
b = [y for y in a if y % 2 == 0]

first builds up a list of the cubes of all of the numbers between 10 and 19 (yes, 19), assigning the result to 'a'. A second list of those elements in 'a' which are even is then stored in 'b'. One natural way to do this in Ruby is probably:

a = (10..19).map {|x| x ** 3}
b = a.select {|y| y.even?}

but there are a number of obvious alternatives, such as:

a = (10..19).collect do |x|
x ** 3
end

b = a.find_all do |y|
y % 2 == 0
end

It tends to be a little easier to come up with equally viable, but syntactically distinct, solutions in Ruby compared to Python, even for relatively simple tasks like the above. That is not to say that Ruby is a messy language, either; it is merely that it is somewhat freer and more forgiving than Python is, and many consider Python's relative purity in this regard a real advantage when it comes to writing clear, easily understandable code.

And Somewhat One of Performance