How to Become a Python Developer
What is Python?
Python is an interpreted, object-oriented, high-level programming language with easy to learn syntax that emphasizes readability. In fact, Python was designed for readability, with a syntax that is similar to a math-infused version of the English language.
Python is an open source, cross-platform language, meaning it can be run on Mac, Windows, Linux, and Raspberry Pi.
Apart from this, Python differs from other programming languages in a few ways, including using new lines to complete a command (as opposed to semicolon or parentheses) and whitespace to define the scope of loops, functions, and classes (as opposed to the curly brackets used in other languages)
Developers love Python because it gives their productivity a huge boost. With no compilation step, the edit-test-debug cycle is incredibly fast.
Debugging Python programs is straightforward: a bug or bad input won’t cause a segmentation fault. Instead, when the interpreter discovers an error, it raises an exception. When the program doesn't catch the exception, the interpreter prints a stack trace. A source level debugger allows inspection of local and global variables, evaluation of arbitrary expressions, setting breakpoints, stepping through the code a line at a time, and more.
The debugger is written in Python itself, testifying to Python's power.
What Are the Benefits of Python?
Python’s readability and cross-platform adaptability gives it a number of benefits, including:
- Versatility. Python can be used in web development, data science, scripting, and more. Python focuses on code readability. The language is versatile, neat, easy to use and learn, readable, and well-structured.
- Readable and maintainable code. Similarities with the English language make it beginner-friendly. The learning curve is very mild and the language is feature-rich. Python is dynamically typed, which makes it friendly and faster to develop with.
- Less code required to complete projects. Python’s syntax allows Developers to write programs with fewer lines than other programming languages.
- Compatibility with major platforms, including Windows, Mac, Linux, and more.
- In-depth libraries, including Pandas and NumPy. You can find a library for basically anything you could imagine: from web development, through game development, to machine learning.
- Faster prototyping. It can speed up prototyping as it runs on an interpreter system, allowing code to be executed as soon as it is written.
- Open source tools and frameworks, with a dynamic, community to boot. You can download Python for free and begin writing code in a matter of minutes. And some of the smartest minds in the IT world are contributing both to the language itself and to its support forums.
What Can You Do With Python?
From web development to data science, machine learning, and more, the number of potential real-world applications for Python is endless.
Python’s ease of use and features have made it a versatile programming language for a range of tasks and projects, including:
- Server-side web development
- Software development
- Creating workflows
- Connecting database systems
- Big data and complex mathematics
- Rapid prototyping
- System scripting
Let’s take a look at how tech giants use Python. Google has used Python from the start, and it’s gained a spot as one of the company’s main server-side languages. Guido van Rossum, Python’s Benevolent Dictator for Life, even worked there for several years, overseeing the language’s development.
Instagram is known for running the world’s largest deployment of the Django web framework, which is written entirely in Python.
Spotify, meanwhile, uses Python in its data analysis and back-end services. Spotify’s team says Python’s ease of use leads to a lightning-quick development pipeline. Spotify performs a ton of analyses to give recommendations to their users, so they need something that’s simple but also works well. That’s Python in a nutshell.
And Facebook Production Engineers are keen on Python too, making it the third most popular language at the social media giant (just behind C++ and their proprietary PHP dialect, Hack). On average, there are over 5,000 commits to utilities and services at Facebook, managing infrastructure, binary distribution, hardware imaging, and operational automation.
Why Is Python Popular With Data Scientists?
With a manageable learning curve and an array of libraries that allow for near-endless applications, Python is the top programming language of choice for many Data Scientists who appreciate its accessibility, ease of use and general-purpose versatility. In fact, BrainStation’s 2019 Digital Skills Survey found that Python was the most frequently used tool for Data Scientists overall.
Since being introduced in 1991, Python has built up a growing number of dedicated libraries to carry out tasks, including data preprocessing, analysis, predictions, visualization, and preservation. Meanwhile, Python libraries, including Tensorflow, pandas, and scikit-learn allow for more advanced machine learning or deep learning applications.
Data Scientists also tend to find Python to be generally faster than R and better for data manipulation. It’s also known for increasing productivity due to the simplicity of the language and the fast debugging cycle.
Data science involves extrapolating useful information from massive stores of statistics, registers, and data. These data are usually unsorted and difficult to correlate with any meaningful accuracy. Machine learning can make connections between disparate data sets but requires serious computational sophistry and power.
Python fills that need by being a general-purpose programming language. It allows you to create CSV output for easy data reading in a spreadsheet.
No matter what Data Scientists are looking to do with Python, whether it’s predictive causal analytics or prescriptive analytics, Python has the toolset to perform a variety of powerful functions.
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.