Data is the “new oil” of the modern world. The ability of companies and enterprises to extract meaningful insights from the enormous amount of data in the world of business is mission-critical. This new utility for success is why so many companies are turning to highly-skilled data scientists to help them find meaning in overwhelming amounts of data, make strategic decisions, and stay ahead of the competition. This is also why the demand is so incredibly high for trained data scientists.
These data scientists are part mathematician, part problem solver, part coder, and part communication experts. Their training helps them to digest complicated numbers and algorithms to help their companies be as successful as possible. This means using the best tools available on the market to help accomplish their goals. For many, one of their top tools is the Python development language.
According to recent studies, Python is the number one choice of data scientists. We could intuitively say that these professionals make such a pick because they need an easy-to-use language with a variety of tools to make their lives easier. And given that there are many situations within the field of data science for which Python is a natural fit, it’s ranking at the top seems obvious.
Yet, Python as the definitive language for data science isn’t something written in stone. There are reasons why the language is increasingly popular among data scientists, including the ones below.
What Makes Python the Preferred Data Science Language and Tool?
Data scientists must face complex problems on the regular. They then solve these problems with the four major steps of problem-solving: data collection and cleaning, data exploration, data modeling, and data visualization. Python provides the tools necessary to effectively complete the problem-solving process with dedicated tools available and attributed to each step.
Python is also the top language choice by data scientists for the following reasons:
1. It’s easy to learn.
Data scientists aren’t experts in a single area – they aren’t only developers or only statisticians. They must have a wheelhouse of tools at their disposal that they can rely on and thoroughly understand while not dedicating their entire careers to learning the art of coding.
In that sense, one of the most alluring factors of Python is that anyone aspiring or in need of learning this language is able to do so easily and relatively quickly. Compared to other languages used in data science (namely R), Python offers a condensed learning curve and easily understood syntax for simplified code work.
2. Python is scalable, more flexible, and faster than other languages.
Compared to R, the second leading language in data science, Python is a much more scalable language and is ultimately faster, including when compared to other languages such as Stata and Matlab.
Its flexibility allows data scientists to build data models, create machine learning-powered algorithms, apply data mining, and systemize data sets to accomplish many different tasks in a shorter period of time than with another language. Many of the top sites of today, including YouTube, utilize Python for its flexible and fast nature.
3. It features notable graphics and visualization tools.
Data scientists must have the ability to communicate complicated data sets to their other team members through data visualization. Python offers a pack of diverse visualization options to make data much more accessible and easier-to-use in the real world. This includes traditional charts and graphs but also web-ready interactive plots and such to get the best sense of the data as possible.
4. Python comes with a huge collection of libraries.
Python is one of the most supported languages on the internet. Every Python user has access to an incredible list of completely free libraries. This helps ensure that data scientists and coders alike will continue to receive support as libraries constantly grow and provide more robust solutions. These libraries make the language a great solution without the hassle and additional expenses associated with expansion.
5. It offers an expanded pack of tools for analytics
Python helps data scientists handle data after they’ve gathered it. This means they must use the perfect tool for completing the processing of complex data. Python’s built-in data analytics tools work excellently for tasks such as the reduction of data, pattern penetration, and the correlation of information.
6. The language’s online community is enormous and ever-growing.
The Python ecosystem is always expanding. Many volunteers create new data science libraries to help the language extend further into the field. This online support paved the way for the creation of the most modern tools and data processing systems.
Whatever queries or questions a data scientist comes across in the field, or even if they just need a quick solution to their coding problems, professionals know that another dev probably found a solution for their exact problem.
The success of a company directly depends on its ability to collect data, extract knowledge and insights, and use said knowledge to make the most informed and effective business decisions. In that context, Python is the top choice by data scientists in the field for a reason: it helps companies accomplish those goals.