Data science is the new buzz word and rightfully so. If you have had the chance of having a conversation about Data Science to literally anyone, professional or beginner, you must have heard them say “Future belongs to data”. WHAT! “Haven’t it always belong to data“ is what you must have wondered. Yes, it always has, but not in the ways we are familiar with nowadays. Today we have tools to play around with that data and manipulate it to extract information.
With an overwhelming amount of data and an equally overwhelming tools for that data, it is very much justified if you have the question: What is best tool? If you are a beginner, then Python for Data Science is your top option. The next very reasonable question is “Why”?
Read on to find out why Python is optimal for Data Science for Beginners.
Ease of use
Seriously who has time to take care of colons and whatnot at the end of each line of code? Imagine the frustration of looking through your code and finding out it isn’t working because you missed a colon in like a 100 line code. Python rid you of all these intricacies and lets you write the code in almost a natural language like syntax. This is why Python For Data Science for Beginners is helpful because it lets you focus more on logic than getting caught up in the syntax.
One other reason why Python scored a whopping third in Stack Overflow’s Developer Survey 2018 is its wide database of libraries which help the users with numeric and visual operations. Some of the famous and widely used libraries used for Data Science are SciKit, Pandas, Keras, Tensorflow, Pytorch and Matplotlib. Tensorflow, Keras and Pytorch are specifically used for data science because a number of factors like flexibility, debugging capabilities, and short training duration.
Everyone loves everything free and especially when it comes with such a huge number of functionalities. One of the reasons why python is preferred over other programming languages is because it’s basic functionalities and other libraries are all compete free of cost and you can access and manipulate to any extent without any stop on what you can use them for. Due to its free access, it is widely used even in international competitions. The winner of SpaceNet’s winner for Off-Nadir Building Footprint Extraction also used Python to code his way to the top of the ladder.
Almost all other languages give users the liberty of customization while designing their own programs. What sets Python’s customization apart from others is its customization and plug-ins across a number of platforms. No matter what background you come from, there’s always a plug-in for the propriety softwares you use. Not only does Python provide customizations across various platforms but it also makes it easier for the user. That is also one of the reasons, why Python for Data Science for beginners is a suitable fit.
Playing around with data is one thing but as long as you are not capable of see it and analyze it visually, it can create lots of confusion and ambiguity. One of the ways you can visualize any data is just to plug it in MS Excel and have some charts and see the trends. But can that really work? Will you go through the trouble of shifting from one tool to another just to see how your data is behaving or you would simply want to have that feature integrated in your code? Obviously the later. With Matplotlib, this problem is solved and you can easily install this library in your editor/compiler. This helps visualize the data while in no time.
One of the most amazing feature of Python that makes it a very good fit for Data Science purposes is OOP. The structural hierarchy enables the users to structure their data in a way that will keep things organized and treat data as objects which are easily callable, editable and you can easily overwrite them anytime without having to mess up the rest of the code.
That being said, there are many languages that are used for data analytics and manipulation. People and organizations use, R, JAVA, Matlab and all sorts of other programming languages. Just because this article lists a bunch of uses of Python for Data Science for Beginners doesn’t mean its set in stone. It all boils down what sort of data you have and how do you plan to manipulate it and for what prose exactly so, as a beginner, you should keep yourself open to options and weigh in all the factors like the amount and kind of data and how you do plan to manipulate it and then decide which language fits your needs better. If you are confused, you can always consult Mstweeks for more information on web development and tools information or get in touch with us directly.