In the ever changing landscape of software programming, it is not surprising that developers and employees have a different set of preferences for desired skills.  However the number one language that developers want to learn according to a survey of developers by technical recruiter, Hacker Rank is Python. This is not a surprise considering that Python has been in demand for several years and programmers tend to really enjoy this language for clear syntax, good OOP support and great shortcuts. Python, named “the language of the year” in 2007 and 2010 in the TIOBE Index and has climbed to #4 status in May of 2018.

According to the study, employers want developers who:

-  Have problem-solving skills, such as the ability to break down large, complex problems.
- Are proficient in their programming language and debugging.
- Can design systems.
- Can optimize performance.
- Have experience in reviewing and testing code.
- Are proficient in database design

Surprisingly, formal education is not the deciding factor when it comes to what companies care about the most. People with computer degrees or certifications on a resume are not necessarily a first choice for hiring managers. Others that have years of experience even if those individuals are partially self-taught in the field stand to be taken seriously in the field.   For those individuals with a passion to learn and master a skill, there are ample opportunities with smaller to mid-sized companies.

Some interesting FAQ’s from the study:

    On average, developers know 4 languages, and they aspire to learn 4 more.
    Younger developers between 18 and 24 plan to learn 6 languages.
    Folks older than 35 only plan to learn and additional 3 languages.
    The top languages developers said they will learn were, Go, Python, Scala, Kotlin, and Ruby.
    There is a large gap between employers seeking developers that know React than there are folks that can do it.

So, Why Learn Python?
It is now the most popular introductory teaching language in U.S. universities.  Python is easy to use, powerful, and versatile, making it a great choice for beginners and experts alike. It allows you to think like a programmer and not waste time understanding difficult syntax that other programming languages can command. And, because of its rapid growth, many developers contribute to the Python community and share Python libraries making creativity that much more a reality

Back in the late 90's, there were a number of computer scienctists claiming to know java in hopes of landing a job for $80k+/year.  In fact, I know a woman you did just that:  land a project management position with a large telecom and have no experience whatsoever.  I guess the company figured that some talent was better than no talent and that, with some time and training, she would be productive.  Like all gravey train stories, that one, too, had an end.  After only a year, she was given a pink slip.

Not only are those days over, job prospects for the IT professional have become considerably more demanding.  Saying you know java today is like saying you know that you have expertise with the computer mouse; that's nice, but what else can you do.   This demand can be attributed to an increase in global competition along with the introduction of a number of varied technologies.   Take .NET, Python, Ruby, Spring, Hibernate ... as an example;  most of them, along with many others, are the backbone of the IT infrastructure of most mid-to-large scale US corporations.  Imagine the difficulty in finding the right mix of experience, knowledge and talent to support, maintain and devlop with such desparate technologies.

Well imagine no more.  According to the IT Hiring Index and Skills Report, seventy percent of CIO’s said it's challenging to find skilled professionals today.  If we add the rapid rate of technological innovation into the mix of factors affecting more businesses now than ever before, it’s understandable that the skill gap is widening.  Consider this as well:  the economic downturn has forced many potential retires to remain in the workforce.  This is detailed in MetLife's annual Study of Employee Benefits which states that“more than one-third of surveyed Baby Boomers (35%) say that as a result of economic conditions they plan to postpone their retirement.”  How then does the corporation hire new, more informed/better educated talent?    Indeed, the IT skills gap is ever widening.

In order to compensate for these skill discrepencies, many firms have resorted to hire the ideal candidates by demanding they possess a christmas wish list of expertise in a variety of different IT disciplines.  It would not be uncommon that such individuals have a strong programming background and are brilliant DBA's.  What about training?  That is certainly a way to diminish the skills gap.

There are many excellent opportunities for IT professionals to present themselves to corporate entities for future consulting positions. You can find yourself in your next consulting engagement if you are willing to combine your IT skill-set, a good amount of corporate research and a bit of old fashioned moxie. Contrary to popular practice for applying for jobs through placement agencies and recruiters, it’s possible to meet with hiring managers, representing your firm, you, directly.

Although recruiters may not take a fee directly from you, the fact that they charge a fee for their services to a company after your placement may keep them from being able to place you. On the other hand, corporations searching for individuals with advanced knowledge of IT functions cannot rely on the friends and family of current employees to find superior consults in all fields.

What are some other options? First take the time to research corporations you would like to consult with. Look for the ones that most likely are complementary to your area of expertise because of the goods they produce or the services they provide to the public. Or you may opt to choose a company that is geographically close to where you live for the convenience of a short daily commute.  

Another way to find your next consulting job is to actively scan the news and see what corporations are making waves within their individual marketplaces. This is a good indicator of possible expansion and the need to hire on a contractual basis. Another good indicator of a need for IT professionals is a mention of a company relocation or expansion. Growth or renovation of office environments is often accompanied by a modernization of IT systems. Current IT team members may not be relocating or might not be familiar with systems other than the ones they already service.

Do as much research as possible about each corporation from their own websites and other sources dedicated to their particular field of endeavor. This gives you the edge in being able to speak about the IT systems they already have in place or your ideas for adapting their line of work or new product with the use of an IT system advancement. Their websites will often have a list of their corporate management. Make sure you address your cover-letter and resume to not only the Director of Human Resources, but to all executives in charge of their IT departments. Be specific in your abilities and the fact that you can be flexible with hiring arrangements.

Attend job fairs that have an emphasis on the more technical fields. When possible, also circulate your resume both digitally and in paper format to smaller and mid-sized companies. These corporations may not be able to maintain their own full-time IT group for fiscal reasons and your consulting prowess may be able to “save the day” for them in an emergency. You can become part of a corporate team on your own; all it takes is additional work on your part. However after your consultant job placement, you will be pleased that your efforts have succeeded so well.

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.