Controversy was recently courted as Southern California Edison (SCE) prepares to cut their own staff while looking to meet their staffing needs with offshore employees skilled in the field of “IT” or Informational Technology. This has been the second major utility company in the United States to take this path towards providing services to its consumers while holding current rates at consistent levels. SCE does not disclose the exact numbers of expected lay-offs, but the LA Times reports that it is in the hundreds.  Utility companies tell their consumers that these moves are necessary as a hedge against inflation and to keep their services at rates that their customers can easily afford. Critics claim that the use of foreign workers is the first step to using an entirely foreign workforce and promoting large scale unemployment amongst American citizens. Often this has been seen as a conflict between national and international workers for the same jobs, salaries and careers.

It has been noted that this State of California utility company, much like other corporations that hire foreign workers does so primarily when there is a shortage of national citizens that can perform these jobs well. IT workers that are brought in with H-1B Visa work permits usually are college educated and hold expertise in technical areas and studies that local employees may not be especially trained in. Once again, critics decry the fact that these employees are not hired directly. On shore contracting companies operating in the continental United States are directly hired by the utility companies. These contracted companies then serve as “middle-men” and hire a wide range of foreign workers with H-1B paperwork so that they can move to the United States. The workers then perform a variety of jobs instead of American workers who were either born in the country or have achieved American citizenship on their own.

Needless to say, the amount of visas issued in a given year is a concern for U.S workers in various fields but particularly in Information Technology. As large corporations stack the employment deck with foreign workers who put in the hours for a fraction of the pay-rate for local employees, local IT professionals are finding it more difficult to find work nationally.  They encounter rejections, endless interview processes or low –ball offers from companies and recruiting agencies looking to fill positions at a bare minimum cost for coveted skill-sets.  

Meanwhile, an H-1B worker is a worker brought in on a temporary basis with a visa allowing them to work freely in the United States. Much like a student or travel visa, it is issued for on a calendar oriented basis.  Applicants who successfully renew the visa for an extended period of time can expect to work in the United States for up to ten years.  Although U.S companies hiring these employees may pay them less than their local employees, the salaries earned by H-1B Visa workers are almost always higher than these workers would earn in their own country of origin.

Both sides can agree on several issues. When it comes to these H-1B Visa workers, their assignments are generally of a contractual nature and require them to reside in this country for a period of months to years. However it is also an accepted fact that while they are in this country, they are responsible for paying rent, utilities and all other living expenses. As residents of the United States on a permanent basis, they are also liable for taxes on any salary they have earned while living here.

Dr. Norman Matloff, a professor at the University of California, Davis and writer on political matters believes the shortage to be fiction. In his writing for the University of Michigan Journal of Law Reform, he claims that “there has been no shortage of qualified American citizens to fill American computer-related jobs, and that the data offered as evidence of American corporations needing H-1B visas to address labor shortages was erroneous. The American Immigration Lawyers Association (AILA) agrees with him and describes the situation as a crisis. Likewise, other studies from Duke, Alfred P. Sloan Foundation and Georgetown University have disputed that in some years, the number of foreign programmers and engineers imported outnumbered the number of jobs created by the industry

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.

Related:

Who Are the Main Players in Big Data?

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.

The Zen of Python, by Tim Peters has been adopted by many as a model summary manual of python's philosophy.  Though these statements should be considered more as guideline and not mandatory rules, developers worldwide find the poem to be on a solid guiding ground.

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!