We all like to make the right choices at the brink of time, and this point in time looks exceptionally suitable for a data science enthusiast to start putting the pieces together. Indeed there are several pieces to put together when you are planning to embark on a journey through the ever expanding domain of data science and data analytics as a whole.
Today it is very hard to point at a single industry and tell that this one is the data science industry because it bleeds into and has an impact on every single industry that is thriving on the face of earth, from pharmaceuticals to automobiles the range encompasses almost everything one can mention. The whole engine however, mostly runs on a very simple policy that is going through the history, analyzing the present and trying to judge what is needed for the future. This is like trying to know what the consumer may like a month later while the consumer itself does not have a faintest idea about it. Interesting as the whole concept sounds like things can and they do get very tricky at times.
As the amount and variation of the data increases the whole operation depends on tailor made tools and skilled operators. Python data science can be a way to make a comparatively easy start and go as deep as you can. The programming language Python and all its components are created with two things in mind – simplicity and fun. When you are drowned in a heap of structured and unstructured data a simpler tool to process the data untangles locks. This piece of technology is found to be exceptionally comforting by most coders.
- Python takes less amount of code for equivalent jobs than JAVA or C++.
- It works pretty well with SQL users.
- It can handle the machine learning algorithms better than most languages.
These perks apart it is easier to learn and understand. With Python data science operations are easier. So, if you are planning to learn a new programming language, let it be Python. Python is an open source project. This has certain minor drawbacks when it comes to customer service but the pros are far superior to the cons. The best of the positive features is the presence of an ever expanding and always active community of users who keep the buzz going. You can count on the user forums if you are ever stuck midway in a project. Anyway, successful and wholesome operation of Python requires training and practice; these you can gain from the analytics institutes that are offering Python training. Most of the start ups and a bulk of the major companies are quite dependant on Python for their data science operations; get Python data science skills to become an asset to them.