Python is THE language for Data Science

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“Python has been an important part of Google since the beginning, and remains so as the system grows and evolves. Today dozens of Google engineers use Python, and we’re looking for more people with skills in this language,” said Peter Norvig, Director of Search Quality at Google, Inc. Even if there was no other reason to use Python the support of a renowned corporate sponsor like Google is a big reason to consider it…not to mention that Facebook and Amazon Web Services also heavily back it. Here are six other reasons why Python is the language for data science.

Easy to Learn and Use

When Guido van Rossum released Python in 1991 one of his goals was an emphasis on readability. Python reads almost like plain English and allows you to write complex tasks simply. When you use it to solve a complex Machine Learning problem it allows you to focus on that problem not the technical details of the programming language.

Versatile, Efficient, Reliable and Speedy

Python can run on any platform and allow coders to switch easily from one to another. It can be used in nearly any kind of environment without performance loss: mobile applications, desktop, web development and hardware programming. Python doesn’t restrict developers from creating any sort of application, and doesn’t require the recompilation of the source code, making it easier to view results.

Mature and Supportive Python Community

Having been created 30 years ago there has been time to develop lots of documentation, guides and video tutorials. As an open source language this information can be easily accessed, shared and discussed in a variety of forums. All of this along with Python being free has contributed to its growth in academic settings, which in turn adds to the resources that are created and available.

Rich Library

Last but certainly not least are the hundreds of python libraries and frameworks available to every user. Statisticians and scientists resisted becoming “programmers” until Python and its libraries became available. This is probably the main reason Python is the language for data science. For machine learning there are Pandas, PyBrain and Scikit-learn, for natural language processing there is Nltk, NumPy facilitates working with arrays, TensorFlow is for neural networks and Keras and Caffe are for deep learning just to name a few that make the data scientists’s job doable.

The September 2021 TIOBE Index of the popularity of programming languages ranked Python second only to C. Python is only .16% behind C and if it becomes #1 it will be only the third language to top the Index following C and Java. The PYPL (PopularitY of Programming Language Index) scores Python as the most popular language and also the fastest growing language at 17.6% in the last five years.

Interested in putting your Python skills to work? Contact Smith Hanley Associate’s Data Science and Analytics Recruiter, Paul Chatlos at pchatlos@smithhanley.com.


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