In most business circles, the question of whether or not a website truly helps a company's business has become somewhat moot. Simply put, a website is a necessary evil, like it or not. The question is no longer, should a company have a website, but rather, is the website optimized to ensure the best potential results. Of course, it is important to understand what is meant by "helping a company."

Many businesses are under the assumption that a website is going to turn into cold hard cash for the company. Well, that could be the case if the organization is using a type of e-commerce platform to buy and sell goods. Many businesses are service oriented and as such, the website serves an entirely different purpose.

F#, which is usually pronounced as F sharp is one of the newly launched and rapidly developing programming languages.  It has recently become focus of attention due to its quick advancement to the 12^th position in the recent TIOBE index and the overall rise in popularity.

What is F#?

F# is an open source, functional and object oriented programming language which is available in cross platform. It was developed by a company called F# software foundation with help of Microsoft and other open contributors. F# runs on Linux, windows, iOS, Android and the GPUs and HTML as well. It is a mature programming language which helps the users as well as the organizations to solve the complex problems in a much simpler way with easy code. With its wide range of usage in the specialist and application areas, it is proving itself to be a worthy contender for the top 10 list.

Why choose F#?

Attempting to create an intelligent entry about Linux for such a wide ranging audience is a bit like trying to compare a presentation on quantum mechanics for physicists while being readable by somebody with a junior high school education.

If you are a *NIX expert, then I suggest you keep on reading so you know how to explain this world to mere mortals.

I called it *nix for a reason. It represents a family of thinking. It is not unlike the difference between the language C and C++. Are there differences between the two? Certainly. And it is not like the difference between COBOL and C.

Years ago when I ran my own vo-tech school I would teach the students there are only two things in life. MS and *NIX. A friend of mine, a genius developer in her own right was aghast.

Python programming language is general purpose open source programming language. One of its main features is flexibility and ease of use. Python has a variety of useful set of utilities and libraries for data processing and analytical tasks. Currently due to the rise in demand of big data processing python has grown in popularity because its features are easy to use which are core to the processing of huge chunks of information.

Guido Van Rossum, the pioneer of python, introduced python in the year 1980 and then implemented it in 1989. The intention behind the development of python was to make it open source language that can also be used for commercial projects. The fundamental principle of python is to write the code that is easy to use, highly readable and embrace writing fewer lines of code for achieving a particular task. One of the most popular standard libraries which have ready to use tools for performing a various work is Python Package Index. It was introduced in January 2016 and contains more than 72,000 packages for third-party software usage.

Python plays a critical role in linking data to customers. Recently python has found few entry barriers and many people have had access to have experienced the power of python in the past. So, what makes python the best language for big data analytics?

One of the reasons to choose python is that python ecosystem is very vibrant, the ratings at Redmonk are a proof of the strength python community. The Redmonk ranking is based on StackOverflow discussions and contribution made in Github to determine the popularity of programming language on the method used by users to ask questions about Python and the number of the open source projects contributions.