There are so many programming languages out there that it’s hard to feel surprised when people argue about which one is the best. That’s especially true when developers discuss which is the best language to work with newer trends – since there’s no default option, everyone has their say about it. Data science isn’t an exception.
Python, R, Java, SQL, and even Scala are all programming languages that data scientists use daily. All of them are perfect choices for data science projects, as they have their strengths and weaknesses when dealing with vast amounts of data. However, if you were to survey a wide range of data scientists, you’d find that most of them point to Python as the best language to work in the data science field. How come? There are several reasons why, some of which are listed below.
But first, let’s review some basic notions around data science.
What Programming Language to Use in Data Science?
We’re now living in the age of data, where companies can gather vast amounts of information on their clients, markets, competitors, and even entire industries. Using advanced data science algorithms, businesses can sift through those troves of data and analyze them to get valuable insights for their strategies, from understanding seasonal fluctuations to uncovering market gaps for a new product.
Naturally, coming up with the algorithms to take on that complex task is far from being easy. First, there’s the fact that raw data (that is, the unprocessed data as gathered through multiple channels) is full of noise and irrelevant information. And then, there’s the need to define actions for the algorithm to follow (by defining the algorithm’s scope and its precise steps for analysis).
Doing so requires handling different variables and being able to establish a data-training set accurately enough to get good results. So, given that the whole process of creating the platform that analyzes the data is a challenge in itself, it’s clear that software engineers would turn their heads towards a powerful yet easy-to-use programming language. In other words, that’s why you’ll see so many Python developers in the world of data science.
Now, let’s briefly review what turns Python into such an excellent ally for software development companies specializing in data science.
Why Python is the Favorite for Data Science
Naturally, Python isn’t the only programming language to tackle the inherent challenges to data science. However, anyone venturing into the field for the first time would greatly benefit from working with it. The first and more notorious advantage of Python for data science is its ability to create applications capable of training machine learning models and cleaning data. Both of those tasks are probably the most challenging aspects of data science, and Python allows developers to do so easily.
What’s more, Python’s open-source nature has secured a thriving community around it, filled with pre-existing solutions to tackle many data science-related problems. Using Python allows you to use tools and frameworks created by other Python developers, including some to incorporate statistical code and integrate data with web-based apps. All of this makes it far easier to develop Python-based solutions for data science.
Among those predeveloped solutions, there’s plenty of data science libraries already available in the Python community. Libraries like StatsModels and Scipy are some of the favorites among developers devoted to data science. Fortunately, the Python community works on new libraries daily, providing further pre-made functionality to aspiring data scientists.
The community-related benefits imply that, whenever you find an issue in your data science development with Python, you can turn to the online resources for help. There are many forums, specialized websites, and subreddits where you can ask seasoned Python developers for help with particular issues. Since Python’s community is large, chances are you’ll find the answers you’re looking for.
That’s not all. Python is highly regarded as one of the easiest languages to learn. Thus, it doesn’t matter if you’re a beginner in the software development world – you can still learn Python and leverage its libraries to get up to speed with the data science community quickly. The language is a far more accessible alternative to other languages used in data science, especially R and MATLAB.
Finally, it’s worth pointing out that Python offers notorious scalability for all kinds of projects. That’s nothing to scoff at, as data science projects and platforms often have to deal with massive amounts of data and users simultaneously. Python offers superior performance and fast response to concurrent tasks, which makes it perfect for the development of algorithms for data science (which, by definition, need to be powerful enough to analyze massive data sets in no time). Partnering with a Python outsourcing company can further help leverage these capabilities effectively.
Data Science and Beyond
If you’re an aspiring data scientist, then you need to learn Python. Given its inherent benefits for such projects, its gentle learning curve, and the amount of predeveloped tools and libraries to help you in any project, data scientists have turned Python into their preferred programming language.
But that’s not all. There’s an additional advantage in Python that any software developer can leverage. Since Python is multipurpose, it’s widely used for more than just data science. Thus, learning it today won’t just open doors to the data science field but also to other exciting opportunities, from web development to game programming.
Look no further, then. If you’re starting your career in data science, you don’t need to search beyond Python to find the perfect programming language to tackle all the projects you can imagine.
If you enjoyed this article, check out one of our other Python articles.
