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Python has been a consistent chart-topper when it comes to programming languages. In the field of data science, its popularity is unparalleled. And it is not like Python became so popular overnight. The developers across the globe have worked really hard to turn it into an absolute powerhouse when it comes to advanced analytics, machine learning, and data science in general. We will explore the features offered by Python that make it so useful for data science professionals.
The ease of use
Python is famous for its elegant syntax and easy going learning curve. Engineers suggest that people from non-engineering backgrounds interested in coding should start with Python. It requires less amount of code than most other languages to achieve the same results. It is easy to fix bugs and you are less likely to make silly syntactical mistakes. You can also append code implemented in different languages like C or C++, onto Python with ease.
Unlike SAS, Python is an open source language. Not only does it mean that you can access it for free, but also that people from around the globe can contribute to its development. That is why this language stays up to date, ready to take on the growing needs of the data science milieu.
Yes, not only is Python user friendly but it is also AI friendly. AI or artificial intelligence refers to cognitive and reactive qualities in a machine. This is achieved through machine learning – the process of training a machine to interpret data and take action by itself. A recent development in machine learning is deep learning, in which deep neural networks are formed to process data.
Python has grown as a language that can tackle and catalyze these processes with comparative ease. This is primarily assisted by the Python Libraries.
Python has numerous libraries dedicated to analytical and statistical functions. Libraries like NumPy and SciPy can assist mathematical functions with great effect. Pandas is a popular library used mostly for data manipulation and MatplotLib is a great tool for data visualization. Scikit-Learn is dedicated towards machine learning. There are other libraries that are very useful for creating deep learning algorithms.
To sum up the benefits
Using Python for data science saves a lot of time and effort not only because of its highly efficient syntax but also because the Python libraries reduce the amount of code needed very significantly. Its focus on statistical computing has made life easier for data scientists and machine learning professionals.
Since Python is an object oriented, general purpose language, it can be used for a lot of different things. No manual says that a data scientist cannot attempt to design a fun game, and he can actually do that with Python. You would be hard pressed to find this sort of versatility in another language. R, for example is an excellent language for statistical analysis, but it does not offer the kind of flexibility that Python can offer.