Python is one of the most popular and dynamic programming languages within and outside of the field of data science. And while there are many debates about the ranking of programming languages and their popularity amongst data scientists, according to Statista, Python is the third most popular programming language among developers worldwide! As an essential open-source coding language, Python has gained popularity because of its easy-to-learn syntax and versatility, multitude of libraries and packages, and the dynamic and interactive community of Python users that you can find online.
With data science being one of the fastest-growing industries of the 21st century, and Python being one of the most popular tools in the industry, the demand for data scientists that are knowledgeable in Python is also growing. Therefore, by learning more about what Python is and why it is used in data science, you can decide if taking a course in Python is right for you!
What is Python?
As a widely known programming language, Python was developed and established in the late 1980s and early 1990s by Guido van Rossum and has been widely used around the world ever since. Part of the reason that Python has become so popular is due to the principles of Python that van Rossum outlined in that initial development. Specifically, Python was created as an open-source programming language that would be both easy to use and a powerful data processor that can be freely downloaded by anyone. The language of Python is very similar to English grammar and syntax, making it easier to learn than more complex and challenging programming languages. In addition, the programming language’s development has maintained this ease and understandability over time, ensuring that Python users will continue to learn and teach the language.
Why is Python Used in Data Science?
Depending on the size of information and data that an individual is working on, some type of software or data science tool is generally required to work with data. Working with data not only includes housing the data after it is collected, but also using programs or tools that allow you to clean the data, organize it, analyze it, and visualize or model it in a way that is easy for others to understand. As a multipurpose data science tool, Python serves many of these purposes, which is why it is so commonly used within data science. Offering ease of use and the open-source principles that van Rossum initiated and that Python Developers have maintained, Python continues to grow and develop. The following list expands on some of the key reasons why Python is the go-to programming language for data scientists.
Open-source software is any software that is licensed to allow users to modify and change the software as they wish without having complete ownership over the product itself. Generally, open-source software is popular within communities of programmers due to these allowances and, as an open-source programming language, Python users are able to freely edit, customize, and share the code that they work on. This makes the use of Python a more collaborative experience and gives users new to the programming language the opportunity to create their own modifications and build on the programs of others.
The language of Python is not only easier to code than that of many other languages but also easier to understand, due to the syntax used. Unlike programming languages which were created for users with a computer science or engineering background, Python was created to be accessible for anybody to learn and teach. This principle is especially apparent when looking at the Python syntax, which is made up of simple English keywords that are clearly related to specific statements, operations, and commands.
Python’s popularity and longevity means that it has thousands of libraries that can be used by users interested in data science and machine learning. Python libraries can be used to find code that is relevant to the data project that you are undertaking, and these libraries make the programming process both faster and easier for Python users. Some of the most popular Python libraries for data science projects are Pandas, Matplotlib, SciPy, and NumPy. Many of these libraries are covered in the Noble Desktop Data Science Certificate program, so feel free to check out this live online course option to learn more about these popular Python libraries!
As a data science tool that can be used for data cleaning, analysis, visualization, modeling, and whatever program a user designs, Python is an incredibly versatile tool for data scientists that want to work with a data-set in a multitude of ways. Python is also versatile when it comes to how it can be used, and many platforms and applications were designed with Python. As a result, data scientists that are interested in other forms of technological expertise, such as web design and development, can also use their knowledge of Python in multiple industries and projects.
Due to the versatility of Python, as well as the open-source nature of the software, Python users are able to utilize online platforms, message boards, and community groups to share code, tips, and techniques on how to use Python for data science projects. In comparison to more obscure programming languages, the popularity of Python allows users to access a considerable amount of information and instruction on the language, even after you initially learn the language.
Where can you learn Python?
Now that you know more about the key reasons that Python is used within data science, there are many ways that you can learn more about this programming language and join the Python community! There are multiple classes, courses, and certificate programs that can teach you how to learn Python for data science. For example, Noble Desktop’s Python classes and the data science classes offer both in-person and live online courses focused on learning programming for data science. Whether you are looking for in-person Python classes in your area or live online Python classes that you can take from anywhere, there are multiple options.