Python is a widely used programming language among programmers and data scientists. But why is Python so popular, and why do so many data scientists prefer it to alternative programming languages? We’ll look at the benefits of Python programming and why it’s beneficial in data science in this article.
WHY DOES DATA SCIENCE NEED PYTHON?
Python’s simplicity is the first of several advantages in data research. While some data scientists have backgrounds in computer science or know other programming languages, many come from backgrounds in statistics, mathematics, or other technical subjects and may not have as much coding knowledge when they enter the profession. Python syntax is straightforward to understand and write, making it a quick and easy programming language to pick up.
Furthermore, there are numerous free resources available online to help you learn Python and obtain assistance if you get stuck. Python is an open source language, which means it is freely available to the general public. Because there is no expense to begin learning Python, it is advantageous for data scientists who want to learn a new language. This also suggests that a large number of data scientists are already using Python, implying that there is a sizable Python community comprising both developers and data scientists.
Python has a huge, thriving, and inviting community. According to a 2020 Stack Overflow survey of approximately 65,000 developers, Python is the fourth most popular language among all developers. Python is a popular programming language among data scientists. According to SlashData, 8.2 million people are using Python, with “a stunning 69 percent of machine learning engineers and data scientists currently utilizing Python (compared to 24 percent using R).” 4 Python users benefit from a big community that provides a plethora of resources. There are several books and tutorials available, as well as conferences such as PyCon, where Python enthusiasts from all over the world may gather to share information and connect. Python has established a welcome and helpful community of data scientists who are eager to share new ideas and assist one another.
If the sheer amount of people who use Python for data science isn’t enough to persuade you, perhaps the libraries available to make data science coding easier will. In Python, a library is a collection of modules that contain pre-written code to assist with common tasks. They essentially allow us to reap the benefits of others’ efforts and build on them. Some data science tasks would be difficult and time consuming to code from scratch in other languages. NumPy, Pandas, and Matplotlib are just a few of the Python libraries that make data cleaning, data analysis, data visualization, and machine learning activities easier.
WHAT OTHER DATA SCIENCE PROGRAMMING LANGUAGES ARE USED?
Python is the most popular data science programming language. Python is also necessary in most job ads for data science professions if you’re looking for a new job as a data scientist. Job ads from popular job posting sites were scraped by Jeff Hale, a General Assembly data science lecturer, to investigate what was necessary for jobs with the title “Data Scientist.” Python appears in approximately 75% of all job advertisements, according to Hale. Many data science job advertisements mention Python libraries like Tensorflow, Scikit-learn, Pandas, Keras, Pytorch, and Numpy.
R, another prominent data science programming language, was mentioned in almost 55% of the job advertisements. While R is an excellent data science tool with numerous advantages such as data cleansing, visualization, and statistical analysis, Python is becoming more popular and preferred among data scientists for the bulk of tasks. Indeed, between 2018 and 2019, the average percentage of job ads requiring R decreased by around 7%, while the percentage of job postings required Python grew. This isn’t to imply that studying R is pointless; data scientists who are fluent in both languages can take advantage of the benefits of both for distinct objectives. However, because Python is getting more popular, there’s a good chance that your team already uses it, and it’s crucial to choose the language that your staff is most familiar with.
WHAT DOES PYTHON’S FUTURE HOLD FOR DATA SCIENCE?
Python’s application in data science will certainly increase as Python’s popularity grows and the number of data scientists grows. As machine learning, deep learning, and other data science activities develop, we’ll undoubtedly see these advancements become available as Python libraries. For years, Python has been well-maintained and growing in popularity, and many of today’s leading companies utilize it. Python will be employed in the business for years to come, thanks to its expanding popularity and support.
You can benefit from studying Python for data science whether you’ve been a data scientist for a long time or are just getting started. Python is distinguished from other programming languages by its simplicity, readability, support, community, and popularity, as well as the libraries available for data cleansing, visualization, and machine learning. If you’re not already utilizing Python in your job, give it a shot and see how it can help you streamline your data science process. Singapore Coding Club is currently offering python programming course, learn more about it more 这里, and get started with data science and python!