Advantages of using Python for Data Science?
Python is one of the most widely used languages for data science. But, what is the reason for such a sudden growth of this language, which until recently was unknown?
To better understand why the Python language is being widely used in data science applications, we must first understand what that language is and what its characteristics are.
What is the Python language?
Python is a modern and versatile programming language, having its a focus to mathematics, with an emphasis on statistics, which makes it a good programming language for data scientists.
Python was created in 1991 aiming at simple and productive coding, thus, its writing style facilitates learning.
Due to these characteristics, Python is increasingly gaining popularity, especially in Big Data and data analysis. And, since data science is considered the profession of the future due to the constant evolution of technology, Python has emerged as an excellent option for the growth of data science.
Unlike most languages of its creation time, Python runs on interpreters, being thus considered a multiplatform, which eliminates the compatibility problem.
And it doesn’t just shine in data science but has its space guaranteed in graphical interfaces, machine learning, and the web.
Features of Python
When comparing Java code with Python code, for example, we notice the simplicity of Python. The first can be filled with strange commands for those who do not have much affinity with programming, often with special characters.
The second, in turn, is similar to the English language, thus facilitating its understanding. And even if you have no programming experience, you will most likely understand what the code is doing if you master English.
This is the great advantage of this language: it is easy to understand, it is easy to do. However, a detail must be mentioned: it is not easy to completely master Python.
The same is not true of low-level languages, which are difficult to learn but are easily mastered by programmers. What causes the difficulty in mastering Python is precisely what makes it easier: the simplification of commands, being placed in specific names.
So, if you don’t know the name of the function you need to use, for example, it can make your job very difficult.
Why Python is used in data science?
Data science works with a large amount of information, and its main function in companies is to help in decision-making. This means that it is part of the company’s business intelligence, directly influencing the direction it will take.
In that sense, as everything in the modern world is fast, the data scientist must be fast too. So, he doesn’t have time to program 30 classes in Java just to extract some information from a database.
Because of their ease in programming, using Python or even R is the best choice. However, as the first is more complete than the second, it ended up taking up more space among programmers dealing with data science.
Over the years, due to the recurrent use of the language, several repositories have been developed. Thus, it gradually became the powerful analysis tool that it is today.
It manages, for example, to perform compared to MatLab with its efficient libraries, the graphical analysis of Origin and the simplicity of R in one place and free of charge.
This makes it, by far, the best programming language to work with data analysis.
Large user community
Anyone who has programmed knows how difficult codes can be. Especially when we want to innovate, mistakes end up appearing. It’s okay to admit that because it’s part of the programming world.
However, problem-solving is important, and there are communities of programmers for that. These communities are equivalent to the social networks of developers of certain languages.
Furthermore, the language community is not restricted to one country. In fact, you can get in touch with developers around the world through StackOverflow and GitHub, for example.
The larger the language community, the greater the chances of finding a solution to your programming problems.
Python usage areas
As stated earlier, the Python language has wide application in the modern world. We will detail what a Python programmer can do using this language. See below:
Machine learning
The learning machine is on the rise in the world of computer science. In free translation, machine learning means the implementation of programming techniques that seek to reproduce human characteristics in the codes to solve problems.
In this way, robots were developed that can identify images, and patterns in data and have a preference for colors, for example.
It is currently used in large projects, such as facial recognition, galaxy recognition and even identifying feelings .
Business intelligence
The Business intelligence is a vital area for large. The main task of programmers using Python is to handle large databases, trying to interpret the information extracted from them.
It is an area that has grown a lot, and along with the market, the language has spread more and more.
Automation of tasks
You can automate a lot of office tasks using Python. There are libraries that can collect e-mails , categorize and extract information from them automatically.
This is not new in the programming world, but Python makes it very simple to do.
Web creation
There are two important names when it comes to the web : Flask and Django. With them it is possible to build the back end of web applications in an uncomplicated and practical way. In addition, these frameworks greatly speed up the more “verbose” part of the code.
