Whether you’re an expert or beginner, Python is a data scientist’s best friend. There are many reasons why Python has become the leading programming language for data science and AI projects. Learn about them here!
Python is a general-purpose programming language.
Python is a general-purpose programming language. This means that you can use it to solve problems from many different areas of computing. However, it is widely acknowledged to be one of the best tools for working with data and we’ll highlight the reasons for that here.
Python is popular for use in many types of data science and AI projects. It’s used for data analysis, machine learning, and data visualization. Data Scientists can also use it to incorporate their insights into web development; scientific computing; education; game development; and many other fields.
Python was created by Guido Van Rossum in 1991 while he was working at the National Research Institute for Mathematics and Computer Science. The first version of Python was released on January 27th, 1991. As of December 6, 2022, the latest version of Python available is Python 3.11.1.
Because Python can be used for many different tasks it has a large community of users dedicated to the maintenance of the language. It is currently the second most popular programming language on Github, a site where people can upload their code in remote repositories.
Python is often used in data science because of its simplicity and direct syntactical style when compared to other languages like C++, Java (which are also used in data science), Julia, R, or even Rust. Python also has many libraries specifically designed for data science workflows such as Pandas, NumPy and SciPy.
In addition to the user-friendly syntax of Python, there are many reasons people in the data community enjoy working with it. It has a large and active community, as well as many well maintained libraries for doing data science. The ease of use of Python makes it a great option for fast prototyping and quick implementation of ideas.
You can also find support in the form of communities like /r/python or StackOverflow, which have hundreds of thousands of users who are eager to help out with any problems you encounter while using this language.
Tools like Jupyter Notebooks make data exploration and experimentations with visualization techniques accessible and practical.
The Python community.
As mentioned above, Python has a large and active community, with lots of users and contributors. This means that you can easily find help, answers to questions, and solutions to problems.
Not only is it a popular choice for individual developers, but companies often make the choice to use Python, too. This means that companies often provide their employees with training on programming languages like Python so they can use it for their work. Because it’s a popular language for companies to use, there are plenty of high quality resources adapted for business use-case to learn from!
Efficient Data Scientists
With Python, you can use the same language for prototyping and production. This is possible thanks to its modular design that allows you to choose which parts of the program will be executed at runtime. As a result, Python code can be written with no errors in its syntax and still be able to run without crashing.
The libraries available for data science make using Python easy as well. They include NumPy (Numerical Mathematics), SciPy (Scientific Computing), Pandas (Data analysis), and many more! Each of these libraries has extensive documentation on their websites that covers all their features in detail. This means that if you are new to the field or just starting out with one tool like NumPy but want to learn more about other tools such as pandas then there will always be an answer waiting for you somewhere online!
While some organizations and data scientists will opt for another language in their quest for the perfect data science tool, there are many people who would agree: Python is the swiss army knife of data languages!
At Data Sleek, we have professional staff who can help you take your implementations of data tooling to the next level. Of course, our skill set doesn’t stop with Python, but we believe that Python proficiency is a key element of any data scientist’s toolkit.