Quer ser um cientista de dados? Aprenda Python!

Want to be a data scientist? Learn Python!

May 23, 2024 Roberto Magalhães

There are so many programming languages out there that it's hard to be surprised when people argue about which one is best. This is especially true when developers discuss which language is best to work with the latest trends – since there is no default option, everyone has a say in this. Data science is not (…)

Python, R, Java, SQL, and even Scala are 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 large amounts of data. However, if you were to survey a wide range of data scientists, you would find that most of them point to Python as the best language to work in the field of data science. Why? There are several reasons for this, some of which are listed below.

But first, let's review some data science basics.

Which programming language to use in data science?

We now live in the age of data, where companies can gather vast amounts of information about their customers, markets, competitors and even entire industries. Using advanced data science algorithms, companies can sift through this data and analyze it to gain valuable insights for their strategies, from understanding seasonal fluctuations to discovering market gaps for a new product.

Naturally, creating algorithms to perform this complex task is far from easy. First, there is the fact that raw data (that is, unprocessed data collected through multiple channels) is full of noise and irrelevant information. And then, there is a need to define actions to be followed by the algorithm (defining the scope of the algorithm and its precise steps for analysis).

Doing so requires dealing with different variables and being able to establish a training set of data accurately enough to get good results. So, given that the entire process of creating the platform that analyzes the data is a challenge in itself, it is clear that software engineers would turn their heads towards a powerful yet easy-to-use programming language. In other words, this is why you will see so many Python Developers in the data science world.

Now, let's briefly review what makes Python an excellent ally for software development companies specializing in data science.

Why Python is a Favorite for Data Science

Of course, Python isn't the only programming language that faces the challenges inherent in data science. However, anyone venturing into the field for the first time would benefit greatly from working with her. The first and most notable advantage of Python for data science is its ability to create applications capable of training machine learning models and cleaning data. Both tasks are probably the most challenging aspects of data science, and Python allows developers to do this easily.

What's more, Python's open source nature has ensured a thriving community around it, full of pre-existing solutions to solve many data science-related problems. Using Python allows you to use tools and frameworks created by other Python developers, including some for embedding statistical code and integrating data with web-based applications. All of this makes it much easier to develop Python-based solutions for data science.

Among these pre-built solutions, there are already many data science libraries available in the Python community. Libraries like StatsModels and Scipy are some of the favorites among developers dedicated to data science. Fortunately, the Python community works on new libraries every day, providing additional pre-made functionality for aspiring data scientists.

Community-related benefits imply that whenever you encounter a problem in data science development with Python, you can turn to online resources for help. There are many forums, specialized sites, and subreddits where you can ask experienced Python developers for help with specific problems. Because the Python community is large, you're likely to find the answers you're looking for.

This is not everything. Python is highly regarded as one of the easiest languages to learn. So it doesn't matter if you're a beginner in the world of software development – you can still learn Python and leverage its libraries to quickly get up to speed with the data science community. The language is a much more accessible alternative to other languages used in data science, especially R and MATLAB.

Finally, it is worth mentioning that Python offers notable scalability for all types of projects. This is nothing to scoff at, as data science projects and platforms often need to handle large amounts of data and users simultaneously. Python offers superior performance and fast response to concurrent tasks, which makes it perfect for developing algorithms for data science (which, by definition, need to be powerful enough to analyze massive sets of data in a short time).

Data science and more

If you are an aspiring data scientist, you need to learn Python. Given the inherent benefits of such projects, their gentle learning curve, and the number of pre-built tools and libraries to help you with any project, data scientists have made Python their programming language of choice.

But that is not all. There is an additional advantage to Python that any software developer can take advantage of. Since Python is multipurpose, it is widely used for more than just data science. So, learning it today will not only open doors to the field of data science but also other exciting opportunities, from web development to game programming.

Look no further then. If you're starting your career in data science, you need look no further than Python to find the perfect programming language to handle every project you can imagine.

If you liked this article, check out one of our other Python articles.