Python is one of the oldest mainstream programming languages and it’s gaining even more ground with the recent developments in the world of analytics. Companies are obsessed with big data right now and there’s a reason for that. It’s the ultimate business asset that creates business opportunities out of thin air.
Python is a major interface between data and its consumers or, rather, beneficiaries. It’s a general purpose programming language, but it found a stronghold within the analytics world because the barrier for entry is low and many people exposed to BI and analytics have had experience with Python in the past.
But why exactly is Python is so good for analytical applications? This is a breakdown that might be useful for less tech-savvy decision makers who want to learn more about Python for their hiring and operational decisions.
Python is More Accessible
The ecosystem around Python is incredibly strong, and to confirm this one simply needs to take a look at the Redmonk rankings. This combined ranking includes Stackoverflow discussions, as well as Github contributions to measure the popularity of a programming language based on how often people ask questions about the language and how active the open source contributor community is.
R is another huge programming language leading the analytics niche simply because it was originally developed for scientific and analytical purposes. While it gained a lot of momentum during the past 5 years, it’s starting to lose ground and Python has played an important role in that.
Of course, Stackoverflow and Github are subjective measures, especially when we know that Python is more versatile than, say, R. So, why are these metrics important for analytics and any sort of data processing initiatives?
The ecosystem around Python allows for the implementation of analytics initiatives to be a lot faster. You can repurpose engineers who have Python experience or have marketing and sales people be certified in Python so they can perform rudimentary tasks within the analytics processes. This also means that if you have a robust business operations structure, it’s likely better to hire a Python specialist, as his expertise could also be repurposed, in case if your analytics initiative flops.
Python Is More Flexible
As mentioned before, Python is a general purpose programming language and is great for building analytics tools and applications that can be both customer-facing and internal. At the same time, languages like R are less flexible because they were envisioned with a more specialized purpose in mind.
This is also the reason why Python specialists are relatively more accessible. There are more of them, and they tend to have a wide variety of expertise and experiences.
Python is Fancy
Deep learning is another huge tool for analytics that’s gaining ground very quickly. It has become the machine learning Joker that can help businesses predict outcomes, based on historical data. And if you’re looking into the accessibility of deep learning – Python is the clear winner here. In fact, it has more deep learning libraries than any other language that’s currently being used for analytics, while on the other hand, the situation is drastically different with R.
Everyone Knows Python
Python’s accessibility is why it’s the leading introductory programming language in Computer Science departments around the United States. It’s basically Photoshop for programmers.
This might be very important for the future of your company, and as we’ve seen from Redmonk’s data: Python is actually gaining more ground. This means that its framework is much more accessible than other languages, like R. In a couple of years, it’s going to become even more abundant, which will probably lower the cost of hiring a Python specialist even more, which will in turn make analytics implementations more affordable. Especially with all of the combined knowledge within the Python community that Redmonk rankings indicate.
Python Works for Teams
This language is commonly used within different business divisions (business operations, marketing, logistics, sales, etc.) for a variety purposes, and as a result professionals within a company may have been exposed to Python at some point. As Python is semantically very easy, it sometimes mimics real languages.
This is why creating an analytics project from scratch is easier with Python expertise. If other departments have experience with Python, then it’s a lot easier to integrate them into the development process for your analytics project.
Python has a massive following of dedicated community members and people who simply use the language as an aid for their specific tasks that might not be directly related to programming. As a result, there are many global events that revolve around this language. In fact, there are over 40 PyCon events held every year, each one attracting thousands of Python specialists and enthusiasts.
This means that there’s an endless pool of human knowledge around Python that many of the other languages can’t match. And many of the latest events include a lot of focused presentations that revolve around analytics applications of Python and its robust analytical ecosystem.
Python development options are abundant when it comes to machine learning capabilities. Machine learning simplifies data analytics and data science, thus allowing Python users to be able to brag about fully exploiting its capabilities.
This is one of the reasons why Python is considered to be better than R for machine learning applications.
Python is flexible to the point that it has become a great choice for production use. This means that it works perfectly in cases when you need to integrate analytics and data management within a web application. Python offers continuity throughout software processes, unlike other strong data analytics contenders, like R, which are amazing data analytics tools, but are limited to that domain.
This is why Python is one of the leaders among programming languages that deal with analytics. It has an abundant community, a ton of dedicated contributors and is even used by marketing, sales and other professionals. This makes it easier to integrate it within a company or a specific business process.