Why the software development jobs market remains a rollercoaster

Times are good for many software developments, as demands for programmers help them capitalize on their skill sets and gain rewarding jobs in growing IT sectors. At the same time, data analytics thought leaders continue to assert that a lack of professionals trained in advanced data analysis tools is hindering many companies' ability to profit from the information explosion. Software developers, both those who work in established enterprises and those who act as consultants and freelancers, can offer their talents in a variety of analysis and application opportunities.

The burgeoning software development and computer science field recently snagged three of the top 10 spots in the U.S. News "100 Best Jobs of 2014" report.

Software developer took the top spot on the strength of significant growth in demand for both applications developers and systems-focused programmers. Almost 140,000 new positions are expected to pop up in this sector by 2022, according to the Bureau of Labor and Statistics.

Computer systems analyst placed second on the list, receiving high marks for its complexity, which requires workers to understand computer software, hardware and networks. The BLS projected 24.5 percent employment growth by 2022.

Web developers occupied the ninth spot on the list, with 20 percent job growth by 2022 anticipated by 2022.

The other seven jobs on the list are all in the medical field, suggesting that the programming and data science field may be its equal in employment stamina and future opportunities. It also led Tech News Plus contributor Faaez Usman to boldly declare: "Software development is a better job than a doctor."

On the other hand...
Despite the vast potential for software developers to strut their stuff and cash in on their knowledge, data scientists and programmers continue to come at a premium. Many companies struggle with finding candidates that can jump start their data analysis programs. There are many facets and tasks that go into a well-oiled analytics machine, and companies are having a hard time determining how to best approach it.

Complicating matters is that many companies want to devote a large chunk of their research and development resources to data-driven decision making, so they need to find candidates that combine a firm understanding of business models and objectives with application development acumen.

Quality control, security, storage and data cleaning are just some of the tasks that a person in a position that deals with app development and analysis may be asked to perform, wrote SmartData Collective contributor Roman Vladimirov. Programmers may be asked to build applications for novice analytics users. These programs have to deliver querying and visualization tools that are intuitive and easy to understand for most users. This means that the software developer cannot operate independently of enterprise objectives. Business logic must be baked in to every tool.

Building data scientist teams
Instead of delaying data insights because teams lack an individual skilled in every aspect of information analysis, organizations can take a collaborative, piecemeal approach, argued The Wall Street Journal contributors Jeanne Harris. Nathan Shetterley, Allan Alter and Krista Schnell.

"Create a team of people who individually lack the full skillset of a data scientist, but as a group possesses them all. When physicists take on a big project, they bring together a team to design the equipment, run experiments and analyze the data. Likewise, it makes sense to divide the labor of a data scientist rather than search for one person who can do it all," the contributors wrote. "Businesses are long on experience with managing teams. They will remain short on data scientists. Why shouldn't businesses use what already know to compensate for what they lack?"

This practice helps software developers hone their talents while offering them insight, through other contributors, into the challenges involved in the company's analytics efforts and their objectives in analysis.


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