In recent years, Python has become a clear favorite technology amongst data science experts. To understand why, we need to dig a bit deeper into certain features of the language, which help make it indispensable to the field of data science and its operations and functions.
Python is an open-source language that can do much more than simply be used for web development and building applications on mobile devices, it can also be used to find solutions for complex mathematical and computational problems. It is an object-based language that offers functionality and makes use of top-of-the-line libraries which have the ability to deal with all contemporary data science applications in an efficient way.
One of the key benefits of Python as a programming tool is that it beats all others hands down due to the basic syntax it uses. All over the globe, people who do not have programming backgrounds are able to learn Python - academic researchers and scientists alike make use of its quick and proficient prototyping, while statisticians, physicists, and mathematicians all swear by its simplicity to help tackle complex problems. Since Python supports functional, structured and object-based styles of programming, tasks are easy to execute, making it a good language for beginners to start with.
You can see why Python is taking the world of data science by storm. Python can quickly correlate massive volumes of data, statistics, and registers, and help make sense of it. It also enables data scientists to create a CSV output, needed for simple reading of data in a spreadsheet. The language has proved more efficient and effective than C++ and Java, two programming languages that were widely used in the community of data science previously.
In terms of machine learning libraries as well as for machine learning-based project development, Python is often seen as a better option than Ruby. It can be used more successfully to roll out programs and get prototypes to run the way experts want, helping projects to be completed more efficiently. Python APIs have deep learning frameworks that are versatile and productive, their libraries are easily available and technologies like machine learning and data science become easier to adopt using Python.
Scalable to a high degree and faster than Java and R, solving issues, sorting data and untangling problems is what Python does best. Additionally, it offers an array of visualization choices for layouts and graphics, that are core to presenting the insights gleaned from data analytics. For instance, the use of Matplotlib offers the ideal foundation for the building of other libraries. Packages are available to build layouts in graphics with charts, and ready plots for web design and data science to merge in the best possible way.
Other strengths of Python for analysts and data engineers are its GUI (Graphical User Interface) and UX (User Experience) capabilities. A variety of libraries such as pyglet and pygame provide quick and easy ways to develop GUI applications, which coupled with support from the lively Python community, can help you easily create a visual representation of your underlying data. You can also use these capabilities to test and deploy advanced statistical models, while keeping things as simple as possible on the programming side.
From parallel processing, scraping data that is not wanted, pictorial data representation, to machine learning for computations, Python is useful in all phases of the data science process. Today’s massive volumes of data mean many organizations are quickly realizing that they need talented analytical professionals who have specific skills in scientific methods, statistical approaches, data analysis, and other data-centric techniques, in order to uncover new opportunities and make smarter, data-driven decisions.
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