What Is Data Science In Python?

Python is quite a new data programming language, gradually increasing in prominence in the data science industry. This programming environment makes for a quite powerful language that is used for different kind of applications. A major advantage of this tool is its open sourced nature. This has allowed the collective majority of its users to actually develop a set of tools, which is not just compatible but also works quite efficiently within Python.

Python doesn’t happen to be the only tool for data analytics out there. There have been giants like SAS and R who have time and again ruled the data science market. With so many debates arising in the recent times, it seems that Python’s entry has more or less settled them for the long term. Apart from being open sourced and free to install, this programming language brings with itself, a well-rounded and super helpful online community. Not just that but it is also quite easy to learn, which works amazingly in its favour.

Python has the immense potential of becoming the common language that is used for data science as well as in the process of production of web based analytics products. The fact that it is an interpreted language as opposed to a compiled language, may end up taking a lot of your CPU time. But that fact is still negated by the ease which a user experiences while learning python, thereby making it a right choice.

If you happen to be a beginner, who has just started off the data science journey in Python, then you will end up coming across two different types of Python languages. One thing to remember here is both Python 2.7 and Python 3.4 are great options which can be used and worked along with. Let us talk about how Python 2.7 work within the sphere of data science. It is touted to be one of the ‘it’ tools that you must have in your early days, mainly owing to the great community support that it offers.

It has a huge reserve of third party libraries as well for all of those data scientists who are usually involved in specific applications like web development and dependence on external modules. At the same time, Python 3.4 happens to be both cleaner as well as faster. Those data analytics professionals working with it have already ensured a smooth working environment within this tool. While it is not necessary to choose one of the two versions, it is very necessary to learn this language and how to work with it, in terms of your respective needs.

There are various data structures that are used in Python, some of them are lists, strings, tuples, and dictionary and so on. Python makes for a really useful tool, which is clearly derivative from its increasing popularity. One very important factor working in its favour is that it is highly compatible with various other databases and tools like Hadoop, Spark and so on. This is why we see so many professionals taking up training courses from institutes like Imarticus Learning to master Python.