If you're someone who's interested in computer programming, chances are you've considered pursuing a career in it. However, being a computer programmer is definitely not for everyone, as it takes some special characteristics to succeed as a computer programmer.

Good at Math

While you don't have to be a math genius in order to be a good computer programmer, being good at math really does help. In general, as long as you know your trigonometry and advanced high school algebra, you should be set for programming.

However, in a few instances, knowledge of more advanced math ends up being necessary. For example, for shader programming, you should be familiar with integration of multiple variables, matrix algebra, and basic differentiation. You will also require considerable math skills in order to program 3D.

Excellent Problem Solver

To be a successful computer programmer, you definitely need to be an excellent problem solver. It is vital for a computer programmer to break a problem down into small parts. They must then be able to decide the best way to approach individual pieces of the problem. Computer programmers also need to know how to anticipate and prevent potential problems. While problem-solving, they also need to keep in mind things like user experience and performance.

If you're not a good problem solver, knowing a particular language and syntax will be useless if you can't even identify the problem at hand. Therefore, excellent problem solving skills are a critical foundation for computer programming.

Patience

If you are not a patient person, you will quickly become very frustrated with computer programming. Problem-solving is not always easy and fast. In fact, it may take a very long time, especially if you're either inexperienced or working on an especially hard project.

Debugging after the coding process is also very frustrating and tedious. No matter how hard you try, you will always have bugs in your coding, and these bugs, while often easy to fix, tend to be very difficult to detect. Therefore, you will end up spending a lot of your time searching for bugs that take very little time to fix.

Well-Rounded Skills

Generally, computer programmers who are very skilled in one area tend to stick around longer than jack-of-all-trades, as specialized programmers are harder to replace with outsourcing than general programmers. Therefore, it will do you well to specialize in one area of computer programming.

However, while specializing is good, you should still know at least a little about everything, especially skills that relate to the area you specialize in. For example, if you're a core Java programmer, you should know about SQL programming and ideally a scripting language or some regular expressions.

As you can see, not everyone has what it takes to pursue computer programming as a career and succeed at it. In fact, just because you love to program doesn't mean it's a good career choice for you. However, if you feel that you possess all the characteristics listed above, then you should definitely consider computer programming as a career.

Static variables in Python are created as part of the class declaration.  By contrast, instance variables are created as part of a regular method and not a classmethod or staticmethod.

class A:
  i=3  # static variable
	
  def dosomethingregularmethod(self):
       self.k=4   # instance variable
	
# to access static variables


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Communication is one of the main objectives that an organization needs to have in place to stay efficient and productive. A breakdown in accurate and efficient communication between departments at any point in the organization can result in conflict or loss of business.  Sadly, the efficiency between different departments in an organization becomes most evident when communication breaks down. As an example, David Grossman reported in “The Cost of Poor Communications” that a survey of 400 companies with 100,000 employees each cited an average loss per company of $62.4 million per year because of inadequate communication to and between employees.

With the dawning of the big-data era and the global competition that Machine Learning algorithms has sparked, it’s more vital than ever for companies of all sizes to prioritize departmental communication mishaps. Perhaps, today, as a result of the many emerging markets, the most essential of these connections are between IT and the business units. CMO’s and CIO’s are becoming natural partners in the sense that CMO’s, in order to capture revenue opportunities, are expected to master not just the art of strategy and creativity but also the science of analytics. The CIO, on the other hand, is accountable for using technical groundwork to enable and accelerate revenue growth. Since business and technology people speak very different languages, there’s a need on both sides to start sharing the vocabulary or understanding of what is expected in order to avoid gridlock.

In the McKinsey article, Getting the CMO and CIO to work as partners, the author speaks to five prerequisite steps that the CMO and the CIO can take in order to be successful in their new roles.

--- Be clear on decision governance
Teams should define when decisions are needed, what must be decided, and who is responsible for making them.

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.