The most popular programming language in the word, Python has an estimated 8.2 million active users. Let’s find out why this programming language is so popular and why it’s an essential skill for anyone looking to launch a data science career
What is Python?
It’s a high-level, general purpose programming language that was specifically designed with simplicity in mind. Python supports object oriented, structured and functional programming, and is open source – meaning it’s free to use, something that has been key to the formation of its large community of developers.
Who created Python?
Dutch programmer Guido van Rossum created Python in the late 1980s. He wanted to create a modernized version of the ABC programming language.
When naming his new invention, van Rossum wanted something that was short, unique and had a bit of mystery and so took inspiration from legendy English comedy Monty Python.
Why’s it so great for data science?
In a word, simplicity. In a field full of complexity and ambiguity, Python, a syntax that is simple to follow and write, enables those without experience in coding to get started in data science.
Aside from its unparalleled ease of use, Python is also highly flexible. It’s great for everything from data analysis and data mining to machine learning and artificial intelligence.
Getting to grips with Python is made even easier by the fact that it’s open source and free to use. This also opens the door to collaborations. And there’s lots of people to collaborate with – Python has the largest community of any programming language, a community that prides itself on its enthusiasm, diversity and inclusivity.
It also has a huge library base, with over 137,000 python libraries around today, many of them playing a key role in the creation of machine learning, data science, data visualization and data analysis applications.
All of these reasons explain the consistent popularity – almost 70% of data scientists and ML developers use Python.
How long does it take to learn?
It depends if you have coding experience or if you’re learning it as a complete beginner. For those with some coding experience, learning the fundamentals of python usually takes a couple of months, rising to around six months for those without experience.
Like all great creative tools, you can learn the basics of Python fairly quickly, but mastering it can take years.
What can you do with it?
Python is used for everything from building websites and automating tasks to conduct complex data analysis and building machine learning algorithms.
As it’s so easy to learn, Python is often used by non-programmers or data scientists without much coding experience to carry out simple daily organizational tasks. However, for those who learn to use some of its many libraries, Python is capable of carrying out more complex operations.
The main Python libraries and their uses
The fundamental Python package for computing, pretty every data scientist uses it. NumPy is used to implement a wide variety of mathematical operations, from data analysis to data cleaning.
A key data analysis tool, pandas is very popular and user-friendly. It enables you to carry out a variety of data manipulation techniques, such as merging and reshaping, as well as data cleaning and data wrangling.
A cross-platform graphical plotting library, Matplotlib is one of Python’s key data visualization tools. It’s used in conjunction with NumPy to create static, animated and interactive visualizations, from bar charts to scatter plots.
The other key data visualization tool in Python’s list of libraries. While Matplotlib is used for basic data visualization, Seaborn is used for more advanced graphics. It works with the dataset as a whole and is much more intuitive than Matplotlib, on which it is based.
One of Python’s key web scraping libraries, Requests simplicity is its greatest strength. User-friendly and highly responsive, knowledge of Requests is a key skill for any data scientist.
What career is learning Python good for?
All of them! Basically, if you want a career in data science, you’re going to need to know Python.
In terms of specific knowledge of the most popular Python libraries, there are a few careers that depend on it more than others. Knowledge of pandas for data manipulation is crucial for data analysts and data engineers, while Matplotlib is an essential visualization tool for business intelligence analysts and data visualization specialists.
Some of the other job titles that use Python include machine learning engineers, software engineers and data architects.
Python is the most in-demand tech skill for data scientists, with over 70% of data science job listings requiring knowledge of Python. And, with an unwavering popularity and a growing community of supportive users, there’s little doubt that Python will be a lynchpin in the field of data science for years to come.
- Beginner friendly – this should be the first code you learn
- Versatile – you can do almost anything with Python
- Home to a supportive, inclusive community – the largest programming language community in the world
- The future of data science – Python is more flexible and scalable than any other programming language
Download CopeOp’s Data Science Course Guide and learn to master Python, alongside a variety of key data science skills.