Python is a popular programming language due to its simplicity, ease of use, open source licensing, and accessibility — the foundation of its renowned community, which provides excellent support and assistance in the creation of a plethora of packages, tutorials, and sample programs.

Python can be used to create a wide range of applications, from web and desktop GUI-based programs/applications to science and mathematics programs, as well as machine learning and other big data computing systems.
Let's look at how Python can be used in Machine Learning, Data Science, and Data Engineering.

Learning Machines

Machine learning is a relatively new and evolving system development paradigm that has quickly become a must-know for businesses and programmers. For more information, see our previous article on Machine Learning. Python is regarded as the best programming language for machine learning applications due to their complex, scientific computing nature. This is due to its extensive and mature collection of mathematics and statistics libraries, extensibility, ease of use, and widespread adoption among scientists. As a result, Python has emerged as the preferred programming language for developing machine learning systems.

Science of Data

Data science is the application of cutting-edge computer and storage technologies, data representation and transformation algorithms, and scientific methodology to solve a wide range of complex data analysis problems involving raw and structured data in any format. A Data Scientist is knowledgeable about solutions to various classes of data-oriented issues… as well as expertise in applying the necessary algorithms, statistics, and mathematical models to create the necessary solutions. Python is widely regarded as one of the most effective and popular tools for addressing data science-related issues.

Engineering of Data

Data Engineers lay the groundwork for systems and solutions based on Data Science and Machine Learning. Data Engineers are technology experts who begin with the data scientist's requirements. These requirements drive the creation of data platforms that use complex data extraction, loading, and transformation to deliver structured datasets that allow the Data Scientist to focus on solving the business issue. Again, Python is an essential tool in the Data Engineer's toolbox — one that is used on a daily basis to architect and operate the big data infrastructure that the data scientist relies on.

Python, Data Science, and Machine Learning Use Cases

Here are some examples of Python-powered Data Science and Machine Learning applications.
Netflix employs data science to better understand user viewing habits and behavioral drivers. This, in turn, allows Netflix to better understand user preferences and predict and recommend relevant items to watch.
To understand user preferences and shopping behavior, Amazon, Walmart, and Target rely heavily on data science, data mining, and machine learning. This aids in both forecasting demand to drive inventory management and suggesting relevant products to online users or through email marketing.
Spotify makes music recommendations to its users using data science and machine learning.
Spam programs detect and prevent spam emails by utilizing data science and machine learning algorithm(s).
This article provides an overview of Python and its use in Data Science and Machine Learning, as well as why it is important. The Solution Architects, Engineers, and Developers at Region Inc. can help you investigate Python-based solutions for your Data Science and Machine Learning applications. To learn more, please contact us.