Why is Python the language of choice for data scientists?

In this article, we will explain why is Python the language of choice for data scientists.

Python and R Programming are two of the most regularly cited and preferred technologies in job adverts for various data science positions. However, numerous studies have demonstrated that data scientists are increasingly using the Python programming language.

For many years, R was the obvious choice for anyone interested in data science. However, something has changed in recent years, and R has been dethroned. How is Python challenging R's well-established position, and why is Python now the king of data science? Let us see.

Python has a lot to offer, thus a rising number of individuals are using it for their programming needs. Python is far ahead of R in Google Trends. Python is suitable for many types of data science applications, but it is especially popular in the financial sector. We'll give just one example − Bank of America has chosen Python as its preferred tool for processing financial data.

What makes Python so well-suited to data science?

Here are the main reasons why Python is regarded as one of the world's fastest-growing languages −

Complete package

Python, in addition to being a general-purpose language, is suited to any data analysis need. Data scientists must employ a variety of algorithms, for which the language is also designed.

One language for all

Python is a universal general-purpose programming language. It's a quick but powerful tool with a lot of features. Python allows you to create machine learning models, web applications, and whatever else you need in a single language. This will simplify your project while saving you both time and money.

Massive community

Python is supported by a large community. This community is quite cooperative, and there are those who make data scientists' jobs easier by assisting them in many ways. If you're a data scientist who is having Python problems, the most likely solution is to bring your query to a Python expert.

Libraries and scalability

This widely used programming language has a wide range of free data science, machine learning, and data analysis packages, such as Pandas and Scikit-Learn. Pandas provide quick, versatile, and expressive data structures that make it simple and natural to work with "relational" or "labelled" data. It is one of the most efficient and adaptable open-source data analysis tools on the market.

Python includes all of the libraries that data scientists need. It also includes a large number of exclusive libraries. Moreover, Python has emerged as the best scalable language for developing concrete applications. This functionality is great for data scientists who are working on the development of specific data analysis applications and models.

Easy implementation

Before delving into the complexities of artificial intelligence or machine learning, any data scientist must first understand the language. They do not need to spend time learning how to code in Python because it is popular as a beginner's language and does not have a steep learning curve. Python is a popular programming language for creating applications that work with the fewest lines of code necessary. This simplicity is one of the most important aspects in Python's success.

A lot of deep learning frameworks

There are several deep learning frameworks available, including Caffe, TensorFlow, PyTorch, Keras, and mxnet. You can choose from a variety of free tools that will work for your project, allowing you to develop deep learning architectures with surprisingly few lines of Python code.

Process tons of data

If you need to process a large amount of data, you can use PySpark or Hadoop. If Spark's overhead is too much for your specific case, there is also an MPI binding for distributed processing.

If you utilize Spark, several experts propose writing solutions in Scala, Spark's "native" language. Because of the comprehensive PySpark API, Python is an even better alternative for many.

Python is the perfect language for Big Data/Machine Learning prototyping.

Code readability

As you can see, one of the most key design concepts of Python is code readability. Several programmers may build various Python applications, but the goal is that the code is not only identical but also simple to understand and read. Python code is extremely readable; some programmers even compare it to the English language. Why is it significant? It is beneficial to revisit your code months after the product has been released to repair an issue or add a feature. Furthermore, it is easily accomplished by others.

Apart from all of this, the ability to execute machine learning tasks is likely the most important element that has given the Python programming language a significant advantage over its competitors. It includes practically every package a data scientist may require. From scientific computing and statistical modeling to linear algebra and symbolic algebra, among other things, the necessary tools are readily available.

Some of the best data science libraries in Python

• SciPy
• Pandas
• NumPy
• SciKit Learn for Machine Learning
• Data Viz: Matplotlib, Seaborn, Bokeh, Plotly, etc.

Python was clearly the most preferred language for data science practitioners and trainees, according to an Analytics India Magazine survey on data science recruiting in 2019.

Over 75% of poll respondents said it was a must-have talent for job seekers, particularly in the data science industry. R remained in second place because to its capability, which includes statistical processing, optimization, and machine learning. Python is used by some of the world's most successful organizations, including Google, Facebook, Netflix, Spotify, Instagram, Reddit, and Quora. According to these trends, Python will continue to be the most in-demand language among companies, perhaps increasing your take-home pay dramatically.

Conclusion

With the above Python benefits in mind, it can be stated that aspiring data scientists must earn a Python certification. As a Python certification holder, you will be able to add significant value to your resume and make the path to becoming a data scientist considerably easier than those who do not. Furthermore, if you want to change careers and enter the field of data science, having a Python certification will make it easier to demonstrate that you have the necessary understanding to manage various data-related projects. Finally, as a qualified Python specialist, you will be able to secure better positions with higher pay.