How to Become a Python Developer
Who Uses Python Today?
A wide range of financial institutions, tech companies, government agencies, and more look for candidates who have a mastery of Python for roles within web and software development and data science, among others.
Generally speaking, Python Developers (sometimes just referred to as Web Developers, or, more precisely Back-End Developers) use Python to develop websites, applications, games, and more.
Data Scientists use Python for computing large amounts of data and drawing from large databases and libraries. It’s particularly useful for those who specialize in machine learning as Python is able to access machine learning frameworks for those working with AI.
Where Developers and Data Scientists go, however, Python will follow – and startups hire a lot of both. Tech startups are drawn to how scalable the language is. Fintech startups and traditional financial institutions alike rely on Python as part of their tech stack as well.
Python is used by Intel, IBM, NASA, Pixar, Netflix, Facebook, JP Morgan Chase, Spotify, and a number of other massive companies. It’s one of the four main languages at Google, while Google's YouTube is largely written in Python. Same with Reddit, Pinterest, and Instagram. The original BitTorrent client was also written in Python. And it is used as a scripting language to control Maya, the industry standard 3D modeling and animation tool.
Python is used heavily in academic research, particularly in bioinformatics, biology, and mathematics. It is the standard introductory language for many university computer science programs.
Which Companies Use Python?
Some examples of well-known companies that require Developers and Data Specialists to work with Python are Uber, Goldman Sachs, PayPal, Netflix, and Google.
Google has backed Python from almost the very beginning. Early on, the founders of Google decided to use Python whenever they could and only use C++ where they couldn’t use Python. So C++ was used where memory control was imperative and low latency was desired. For everything else, Python enabled ease of maintenance and relatively fast delivery.
Facebook loves Python because the ease of using its libraries means that production engineers don’t have to write or maintain as much code, freeing them to focus on getting improvements live. Also, it ensures that Facebook’s infrastructure scales efficiently.
Netflix Engineers have cheered Python’s standard library, the extremely active development community, and the rich variety of third-party libraries available to solve nearly any given problem. Additionally, because Python is so easy to develop, it has become a linchpin in many of Netflix’s other services.
Finally, because many of Dropbox’s libraries and internals are proprietary and not open source, the company has released a very efficient API coded in Python that allows you to see how their engineers are thinking. Interviews with Dropbox Engineers have indicated that a huge percentage of their server side code is Python.
Python, however, isn’t only sought after in specific companies, there are entire industries that rely on the use of Python. Astronomy, social science, AI, and the Internet of Things are examples of fields that have multiple roles that rely on Python.
Kick-Start Your Python Developer Career
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Recommended Courses for Python Developer
The Data Science Full-Time program is an intensive course designed to launch students' careers in data.
Taught by data professionals working in the industry, the part-time Data Science course is built on a project-based learning model, which allows students to use data analysis, modeling, Python programming, and more to solve real analytical problems.
The part-time Data Analytics course was designed to introduce students to the fundamentals of data analysis.
The Python Programming certificate course provides individuals with fundamental Python programming skills to effectively work with data.
The part-time Machine Learning course was designed to provide you with the machine learning frameworks to make data-driven decisions.