Python has become the go-to programming language for data science over the last decade, surpassing its predecessor R. This shift was driven by Python's simplicity and flexibility as a general-purpose language, its extensive libraries and frameworks for data science, and its ability to attract a diverse audience. Software engineers, statisticians, and data scientists formed a melting pot of talent that has contributed to some of the most impressive technological advancements in recent history. In this article, we will explore how Python became the language of data science, and discuss concrete examples of projects and innovations that were made possible thanks to Python.
A Longtime Companion
Python was first released in 1991 by Guido van Rossum. Since its release, Python has always been a general-purpose programming language that can be used for a wide range of applications, including web development, machine learning, data analysis, scientific computing, and more. One of the reasons Python has become so popular in data science is its versatility as a general-purpose language. In fact, before becoming popular for data science and machine learning, Python was already adopted and familiar in many large organizations as a development language. It was in a sense a trusted option even before it became so mainstream, something that R never was able to do given it was always a niche language used by statisticians and economists.
Python’s intuitive syntax made it an accessible language for people with different backgrounds. This attracted statisticians, business people, and software engineers, creating a "data melting pot" that ultimately led to the creation of a new field and job: data science and data scientists. In a sense, it could be argued that the early data scientists were software engineers in need of a deeper statistical knowledge to make sense of the data while working on large-scale systems. This melting pot has brought together diverse talent, who have different skill sets and perspectives, creating a unique ecosystem that fosters innovation and collaboration.
Students’ First Language
However, where Python's intuitive and general-purpose syntax really shined was in the educational setting. Python dominance became clearer and clearer as more and more computer science instructors and professors chose Python as their students’ first programming language. Those high school and university classes produced an “army” of Pythonistas well beyond the close-knit groups of statistics departments, and to which R couldn't compete. Nowadays not only CS students learn how to code but also physicists, mathematicians, engineers, and biologists. The students enrolling in those curriculums likely learned how to program in Python because all of them fundamentally need to deal with data. Consequently, Python established itself as the go-to language in academia, leading to a cycle that fueled its growth in data science. It is important to note that the originator of Python, Guido van Rossum, started the Computer Programming for Everybody (CP4E) initiative, funded by DARPA, back in 1994. So there are certainly deep accessibility and educational roots for the Python community.
The "Data Melting Pot"
This "data melting pot" described in the previous section has been instrumental in defining what data science is today. In a sense, software engineers helped statisticians to scale their models, and statisticians helped software engineers to embed models in their applications. Then people from other fields contributed in their own specific ways, with biologists suggesting evolutionary optimization algorithms to improve ML model tuning, or mathematicians defining new optimization routines to train models. The result was a field that combined statistical analysis, machine learning, and computer science to create new methods for analyzing data and making predictions.
Python's ability to attract a diverse audience has been critical to the success of data science. Data scientists need to be able to work with data, analyze it, and communicate their findings effectively. The ability to attract people with different backgrounds, perspectives, and skill sets has made Python an ideal language for data science.
Open Source Community, Industry, and Startups
The open exchange of diverse experiences made it valuable to share the libraries built in their specific field with the community. Python's data science open source community has played a critical role in the growth of the language. Python has several popular data science libraries and frameworks, including NumPy, pandas, and scikit-learn. Python's open source community has made it easy to contribute to these libraries, which has helped to foster innovation and collaboration. These libraries have been instrumental in defining data science and making it accessible to a broader audience.
The acceptance of open-source software in large organizations, coupled with the post-"Windows v. Linux wars" of the 2000s, paved the way for a new approach to data management and software development that emphasizes community collaboration. Python's existing reputation as a production-ready language made it easier to garner support from these organizations. Its simplicity and flexibility also made it a popular choice for companies seeking to implement machine learning or data analysis, as it could be easily integrated into their existing systems.
Einblick itself is the product of this phenomenon. As a company, our mission is to save data scientists’ time and remove the pain points of the existing tools and workflows. Our product has been made possible by Python's flexibility and general-purpose nature, as well as its strong data science community. Einblick has built a stack that combines several popular Python libraries and frameworks, including TensorFlow, Keras, and PyTorch, to create a powerful machine learning platform. Einblick's stack is an excellent example of how Python's flexibility and strong community have enabled innovative companies to thrive.
Einblick is an AI-native data science platform that provides data teams with an agile workflow to swiftly explore data, build predictive models, and deploy data apps. Founded in 2020, Einblick was developed based on six years of research at MIT and Brown University. Einblick is funded by Amplify Partners, Flybridge, Samsung Next, Dell Technologies Capital, and Intel Capital. For more information, please visit www.einblick.ai and follow us on LinkedIn and Twitter.