Main image of article Python is Key to the Future of Data Science

If you read through the latest edition of GitHub’s State of the Octoverse—a comprehensive report on the code respository’s biggest trends—you might pick up on something interesting. Although it’s already a well-established programming language, Python is continuing to grow at the rate of an up-and-coming one, gaining 151 percent in usage since 2018.

Part of that continued rise is directly attributable to data science, GitHub added in its report. “Behind Python’s growth is a speedily-expanding community of data science professionals and hobbyists—and the tools and frameworks they use every day,” it stated. “These include the many core data science packages powered by Python that are both lowering the barriers to data science work and proving foundational to projects in academia and companies alike.”

Indeed, data science and machine learning repositories on GitHub have enjoyed extreme growth. Developers (and the companies they work for) clearly feel that analytics and machine learning are the keys to the future, and Python is playing a significant role in that. “Among the most popular (based on star counts) public repositories labeled with the topic, over half of them are built on numpy, and many of them depend on scipyscikit-learn, and TensorFlow,” GitHub added. “We’ve also seen non-code contributions from the data science field, including academic papers.”

Learning Python

Its importance to data science, itself a rapidly burgeoning field, makes Python worth studying. But where to start? If you’re totally new to the language, a good beginning point is this handy documentation available via the Python Software Foundation. From there, check out Microsoft’s “Python for Beginners” video series, which features 44 videos (all of them super-short, between five and 13 minutes in length) that cover various aspects of coding. 

Once you become a little bit more adept, you can begin focusing on writing faster Python (via Functions, Lists, and more), debugging, and other more advanced skills. A variety of tutorials and books (some of which will cost a monthly fee) can help you with Python in the context of data analytics and other fields.

Whether you intend to use Python for backend web development, data analysis, machine learning, artificial intelligence (A.I.) applications, or something else entirely, the best way to start learning is to simply play around. You can download the language’s most recent stable version on the Python downloads page (and if you can’t decide between versions 2 and 3, this page will help you choose). If you want to interact with others on their Python journey, there are also a variety of meetups around the world (you can also check out some data science certifications to expand your skills even further). Good luck!