Developing Data Science Applications with Python

Artificial intelligence (AI) has provided a means for organizations all around the world to gain some form of competitive advantage in their industries. This is largely in part due to the wide suite of applications that Artificial Intelligence encapsulates. One of the most prominent today is machine learning (ML), a type of AI that allows computers to learn without being explicitly programmed or requiring programmers to intervene. While other languages may be used, the most popular programming language for machine learning applications is Python.


There are a number of unique attributes that make Python the clear-cut choice for developing machine learning applications. The first being Python’s simplicity. Python has a straightforward syntax, which means, on average, it takes less time to learn the basics of the language. This means that programmers can jump right into working with the large amounts of data they have to analyze.


Another key attribute that makes the job of a beginner Python user much more simple is the collection of extensive libraries that include the basic prewritten code which produces certain functions and actions already stored and ready to be used. That means programmers can import Python’s base level codes without having to program them from scratch. Some of the libraries include pandas, Keras, TensorFLow, scikit-learn and many more. In addition, these libraries provide the programmer with data representation tools such as charts, histograms and other visually appealing pieces that can make presenting insights and conclusions more engaging.

Developing Data Science Applications with Python

These preexisting libraries of code, in tandem with Python’s flexibility, means programmers have more options. Programmers can default to the programming style that they are most comfortable with, sometimes even combining styles and using Python with other languages to reach the desired result. This is made possible through its ability to work on diverse platforms, including Unix, Linux, macOS, Windows and others. If you ever need to transfer your work to another platform, it’s relatively simple: you can modify certain lines of code to make sure that it will continue to work on the new platform or operating system.


Circling back to its simplicity, Python is also an easy language to read and thus easier for beginners to understand what the code means as well as copy and change it, if necessary. Beginners are also met with a strong sense of community support. Python is an open-source language which means there are numerous external resources that Python programmers use to ask questions, discuss issues and help one another all free of charge.


All of these attributes in tandem exemplify why Python is the most suitable language for data science and machine learning applications. Data science involves drawing information and insights from relevant data, and turning those into business strategies, which is made exponentially easier through the use of machine learning applications.


Author Bio:  Anne Fernandez – Anne joined Accelebrate in January 2010 to manage trainers, write content for the website, implement SEO, and manage Accelebrate’s digital marking initiatives. In addition, she helps to recruit trainers for Accelebrate’s Python Training courses and works on various projects to promote the business.