Python is an extensible and portable programming language that can run on Unix, Mac, or Windows. Because of this accessibility and portability, there is no shortage of users. New Python users can learn how to work with code quickly, with a large community to support their efforts. A 2016 O’Reilly Media survey found that 54% of data scientists use Python in their work. Business and Research support these numbers. For years, Python has been the language of choice for Facebook’s production engineers. It is in fact the third most popular option. And Python is one of the official languages of Google, which means it can be deployed in production within the company. Walt Disney Animation Studios uses Python for many creative tasks. Companies like Industrial Light and Magic, Spotify, Quora, Netflix, Dropbox, and Reddit all rely on Python for everything from filmmaking to social news aggregators. Python is even the most popular introductory coding language taught for Data Science with Python Course.
Many businesses and organizations with very different purposes enjoy using Python, which is a testament to its versatility. But how exactly does it work?
Why does data science use Python?
Because the language is so versatile and flexible, and easy to read, Python is the clear choice in this regard. However, the use of Python is relatively new. Therefore, Python libraries like Pandas help individuals clean up data and perform advanced operations.
It is hard to find data on Pandas usage, but Quartz notes that Stack Overflow saw 1 million visitors viewing 5 million questions about Pandas in October alone 2017. Python’s evolution in data science has gone hand in hand with Pandas, and has opened up the use of Python for data analysis to a wider audience by allowing them to manage row and column data sets, import CSV files, and more.
Although Pandas is the most famous library, there are hundreds of specialized libraries that serve a similar purpose, such as SymPy (for statistical applications), PyMC (machine learning), matplotlib (drawing) and display) and PyTables (data storage and layout). These and other expert libraries help with everything from machine learning and data preprocessing to neural networks.
Advantaging of Data Science with Python:
One of the main advantages of Python is its flexible nature that allows the data scientist to use a single tool at each step of the process.
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Another advantage is the large community of data scientists, machine learning experts and programmers that have worked hard not only to make learning Python and machine learning easier, but also to provide data packages to test students’ Python proficiency and develop new skills. Whether you’re a social scientist in need of Python for advanced data analysis or a seasoned developer interested in a growing field, a part of the Python community is always at your disposal. With this power, Python becomes the perfect choice for Data Scientists around the world, both new and experienced.
Thus, completing a Data Science with Python Course gives you the preparation you need in order to tackle the subject of Data Science while using one of the most popular programming languages in the world.
Read more: Benefits of Python