No industry is as global as software development.  Pervasive networking means that software developers can, and do, work from anywhere. This has led many businesses to hiring development subcontractors in other countries, aiming to find good development talent at lower prices, or with fewer hassles on entry into the US.

While this is an ongoing and dynamic equilibrium, there are compelling reasons for doing software development in the United States, or using a hybrid model where some parts of the task are parceled out to foreign contractors and some are handled locally.

Development Methodologies

The primary reason for developing software overseas is cost reduction. The primary argument against overseas software development is slower development cycles. When software still used the "waterfall" industrial process for project management (where everything is budgeted in terms of time at the beginning of the project), offshoring was quite compelling. As more companies emulate Google and Facebook's process of "release early, update often, and refine from user feedback," an increasing premium has been put on software teams that are small enough to be agile (indeed, the development process is called Agile Development), and centralized enough, in terms of time zones, that collaborators can work together. This has made both Google and Facebook leaders in US-based software development, though they both still maintain teams of developers in other countries tasked with specific projects.

Localization For Americans

The United States is still one of the major markets for software development, and projects aimed at American customers needs to meet cultural norms. This applies to any country, not just the U.S. This puts a premium on software developers who aren't just fluent in English, but native speakers, and who understand American culture. While it's possible (and even likely) to make server-side software, and management utilities that can get by with terse, fractured English, anything that's enterprise-facing or consumer-facing requires more work on polish and presentation than is practical using outsourced developers. There is a reason why the leaders in software User Interface development are all US-based companies, and that's because consumer-focused design is still an overwhelming US advantage.

Ongoing Concerns

The primary concern for American software development is talent production. The US secondary education system produces a much smaller percentage of students with a solid math and engineering background, and while US universities lead the world in their computer science and engineering curricula, slightly under half of all of those graduates are from foreign countries, because American students don't take the course loads needed to succeed in them. Software development companies in the United States are deeply concerned about getting enough engineers and programmers out of the US university system. Some, such as Google, are trying to get programmers hooked on logical problem solving at a young age, with the Summer of Code programs. Others, like Microsoft, offer scholarships for computer science degrees.

Overall, the changes in project management methodologies mean that the US is the current leader in software development, and so long as the primary market for software remains English and American-centric, that's going to remain true. That trend is far from guaranteed, and in the world of software, things can change quickly.

Java still has its place in the world of software development, but is it quickly becoming obsolete by the more dynamically enabled Python programming language? The issue is hotly contested by both sides of the debate. Java experts point out that Java is still being developed with more programmer friendly updates. Python users swear that Java can take up to ten times longer to develop. Managers that need to make the best decision for a company need concrete information so that an informed and rational decision can be made.

First, Java is a static typed language while Python is dynamically typed. Static typed languages require that each variable name must be tied to both a type and an object. Dynamically typed languages only require that a variable name only gets bound to an object. Immediately, this puts Python ahead of the game in terms of productivity since a static typed language requires several elements and can make errors in coding more likely.

Python uses a concise language while Java uses verbose language. Concise language, as the name suggests, gets straight to the point without extra words. Removing additional syntax can greatly reduce the amount of time required to program.  A simple call in Java, such as the ever notorious "Hello, World" requires three several lines of coding while Python requires a single sentence. Java requires the use of checked exceptions. If the exceptions are not caught or thrown out then the code fails to compile. In terms of language, Python certainly has surpassed Java in terms of brevity.

Additionally, while Java's string handling capabilities have improved they haven't yet matched the sophistication of Python's. Web applications rely upon fast load times and extraneous code can increase user wait time. Python optimizes code in ways that Java doesn't, and this can make Python a more efficient language. However, Java does run faster than Python and this can be a significant advantage for programmers using Java. When you factor in the need for a compiler for Java applications the speed factor cancels itself out leaving Python and Java at an impasse.

While a programmer will continue to argue for the language that makes it easiest based on the programmer's current level of knowledge, new software compiled with Python takes less time and provides a simplified coding language that reduces the chance for errors. When things go right, Java works well and there are no problems. However, when errors get introduced into the code, it can become extremely time consuming to locate and correct those errors. Python generally uses less code to begin with and makes it easier and more efficient to work with.

Ultimately, both languages have their own strengths and weaknesses. For creating simple applications, Python provides a simpler and more effective application. Larger applications can benefit from Java and the verbosity of the code actually makes it more compatible with future versions. Python code has been known to break with new releases. Ultimately, Python works best as a type of connecting language to conduct quick and dirty work that would be too intensive when using Java alone. In this sense, Java is a low-level implementation language. While both languages are continuing to develop, it's unlikely that one language will surpass the other for all programming needs in the near future.

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.

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