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5 Compelling Reasons to Learn Python for Data Science and Make a Career in it.
Data Science Online Training is a broad field with tons of entry points, depending on where you want to start and how. I started studying basic data science in a language called R, until I got to one of its many limits. Python has won the R vs Python battle of data science, as I have learned. When I wanted to take the next step in my data science journey, I relied on Python. Learning Python data science is one of the fastest, easiest, and most fun ways to get into data science.
Data Science Training in Noida is a very important skill, with both high income and job satisfaction, yet there are still more companies sending job listings to data scientists than to existing real data scientists.
Although Python is a common target language, this article gives you 5 solid reasons why you should learn Python in data science and explain what Python is in data science.
Python is easy to read
Coding can be intimidating, especially for a beginner. But Python is different. It has amazingly simple syntax so you can pick it up quickly, especially when it comes to complex languages like C, C ++, and Java. Python, a data scientist, is an obvious choice for the language you can learn.
It is so simple that Next Academy actually recommends it as a great option for kids to learn to write with code. And for non-coding, there are many cheap or free resources to start learning Python.
Easy to Read
Python has a clean and simple syntax similar to English, so whatever you create will be understood by you and most people, even if they are not Pythonistas themselves.
When I first started reading Python, part of the reason it was so easy to learn was that I could read examples of Python code and understand what they were trying to do. If you want to get into data science, you should definitely consider readability as a key part of any language you choose.
It is popular
If you read Python, you will be one of many. It is one of the most widely used languages in data science (and elsewhere). The third most widely used language in the world according to TIOBE 2020 index. And in the science of data directly, it emerged as a leader, surpassing my old R-favorite language.
As I mentioned above, many companies use Python to build frameworks and projects. Google, for example, created Tensor flow, based on Python; Facebook and Netflix also rely heavily on their data science projects.
Great Pythonist Community
I remember reading the name of the person who wrote about Python: Pythonista. I liked it. And one of the great benefits of learning Python data science is that you will gain access to the amazing Pythonistas community and be yourself. (There are more benefits than just a cool name.)
The reason why this makes an anaconda reading a good choice for data science is that learning any language is difficult, especially if you feel pressured by work. Communities like the one from Python make that easier.
Complete set of Data Science libraries
Python as a data science language is moving on its own. But in addition to simple syntax, simple words, readability, community, and all the other benefits I have listed, there are libraries. Libraries such as Pandas, statsmodels, NumPy, SciPy, and Scikit-Learn are very popular in data science communities.