Getting involved with the IT, or Internet Technology industry, is a way for you to break into a variety of potential coveted career paths and job openings. Whether you prefer working with the back-end of programming or if you enjoy improve user experience while browsing online, there are many different in-demand IT skills that are useful to obtain today if you are seeking a career in the tech industry yourself.

Cloud Computing

Working with cloud computing, otherwise known as "the cloud", requires you to work within various types of servers that store and access data globally from any location. With the increase in mobile usage, cloud computing is becoming even more prevalent in today's society. When you want to work with cloud computing, understanding the basics of programming and network security is a must. Working in cloud computing is a way to help with building new applications, expanding companies online as well as allowing anyone internationally to locate and access a specific blog, website or mobile app.

UX Design

UX Design is also known as user experience design. A user experience designer specializes in understanding the usability and overall experience a web visitor has when browsing on a site or blog. UX design is essential to ensure that all visitors on a website are capable of navigating the blog properly and accessing the site's content with ease, regardless of the browser they are using or the type of device that is being used to access the site itself. Cross-browser compatibility and ensuring that all websites you are working with are accessible via mobile platforms is another responsibility of many UX designers today. Working in UX design is highly recommended if you believe you have an eye for "good" web design and if you have an interest in improving the overall experience web users for a specific audience have when visiting the blog or website you represent or that you are building for yourself.

IT Security

IT security is one of the fastest-growing positions throughout the entire IT industry and field. IT security requires you to understand network infrastructures as well as how to properly manage each server individually to provide security and protection from potential hackers and online thieves looking to steal sensitive data and information. Maintaining the security of a network and all servers for a company is only becoming more popular with the expansion of mobile phone usage along with the growth of the Internet altogether.

Understanding the variety of IT skills that are in demand today can help you to better decide on a path that is right for you. The more you understand about various IT skills, the easier it is to find a position or career in your future that is most suitable for the type of work you enjoy. Whether you are looking to develop new apps or if you are interested in managing the security of company servers, there are hundreds of positions and skills that are in demand in the IT industry today.

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Applications are becoming more and more sophisticated as languages such as Python open the doors to the world of programming for people who have the creative vision but always felt actually writing code was beyond their grasp.

A large part of any programs success is based on how well it can react to the events which it has been programmed to understand and listen for.

A good example of an event would be when the user clicks a button on the applications window. What happens when that button is clicked?

Well, the first thing that happens is the operating system sends out a message to let any listening software know that the button was clicked. Next, your application needs to do something in response to that event.

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.

When making a strategic cloud decision, organizations can follow either one of two ideologies: open or closed.

In the past, major software technologies have been widely accepted because an emerging market leader simplified the initial adoption.  After a technology comes of age, the industry spawns open alternatives that provide choice and flexibility, and the result is an open alternative that quickly gains traction and most often outstrips the capabilities of its proprietary predecessor.

After an organization invests significantly in a technology, the complexity and effort required steering a given workload onto a new system or platform is, in most cases, significant. Switching outlays, shifting to updated or new software/hardware platforms, and the accompanying risks may lead to the ubiquitousness of large, monolithic and complex ERP systems – reason not being that they offer the best value for an organization, but rather because shifting to anything else is simply – unthinkable.

There’s no denying that these are critical considerations today since a substantial number of organizations are making their first jump into the cloud and making preparations for the upsetting shift in how IT is delivered to both internal and external clientele. Early adopters are aware of the fact that the innovation brought about by open technologies can bring dramatic change, and hence are realizing how crucial it is to be able to chart their own destiny.