If you’re hoping to venture into artificial intelligence-based applications such as machine learning, it’s important to understand the backend development process of these programs. These initiatives allow computers to garner knowledge on trends in a wide suite of industries based on large amounts of data without any human intervention.
Take your most recent online suggested product, for instance. That item was not recommended to you randomly, but rather was a formulated suggestion based on purchasing trends of consumers with similar tastes. This wouldn’t be made possible without the dedication of many talented programmers who would likely be utilizing Python as their programming language of choice on the backend of these projects. This post will detail why that is.
First and foremost, Python provides an extensive amount of prewritten code to be utilized through numerous open-source libraries to expedite the development process of many applications. Some of these libraries include: Keras, Apache Spark, PyTorch, Theano and more. Having this prewritten code on demand creates an easier experience for novice level programmers and experts alike. Each individual library includes different tools to create more engaging ways to present their findings through visually pleasing tools, such as charts and histograms.
In addition to large open-source libraries, Python is a language that is easily compatible with other programming languages that are often used in large-scale data interpretation projects such as C and C++. This ease of integration creates more opportunities for programmers to achieve their goals rather than bottlenecking them to one specific solution. This is made abundantly clear through the numerous operating systems that support the language: Windows, MacOS, Linux, etc.
Beyond that, Python is an excellent starting point for those new to programming. It provides an excellent base understanding of many data science-driven operations and applications. It also can be built upon over time with ease. This is in part due to the straightforward syntax of how easy it is to read and understand at a base level, and how simply it can be edited and shared with others. This, in addition to its prowess in drawing information from data and providing insight into business strategy, is what makes Python the most suitable language for data science applications.
For more information on the advantages of Python in machine learning and data science, see the resource attached courtesy of Accelebrate.