Python is one of the most popular and dynamic programming languages within and outside of the field of data science. As an essential open-source coding language, Python has gained popularity because of its easy-to-learn syntax and versatility, multitude of libraries and packages, and the dynamic and interactive community of Python users that you can find online.
Python is associated with several libraries which specialize in data collection, analysis, visualization, and sharing. Through these libraries and many other unique features, Python is an excellent programming language to learn for Data Scientists who want to develop their skills in automation and machine learning.
Data scientists with backgrounds in Python can apply their knowledge of the programming language to not only analyze information and data, but to create websites. Learning Python will give you the skills to work with software and other developer tools and build your resume as a Python Developer.
Within the world of business and finance, we see data analytics most commonly used for the purpose of making decisions, assessing risk, and detecting patterns within a system. Through developing knowledge of programming languages like Python, Financial Analysts will not only further develop their data science skills but also learn more about the burgeoning FinTech (Financial Technology) industry.
Once you know the basics of Python, there’s no end to where you can apply your programming knowledge. If you’re ready to see where else Python can take you, keep reading.
For every programming language, there are multiple libraries that accompany the language in order to assist users in creating their own programs based on code that was previously created and tested by other users.
While Python is considered an easy-to-learn programming language, how to learn it is another question altogether. A student's approach to learning Python often depends on how and where it will be used.
Many professions use Python for their daily tasks, such as Web Developers, Software Engineers, Full Stack Engineers, DevOps Engineers, Python Developers, and Data Analysts. This article will explore how Python is used in the field of data analytics.
Python has more than 137,000 libraries. Within Python, each library, or module, has a different purpose. This article will explore two Python libraries, Apache Superset and Matplotlib, to see which is best suited for data visualization purposes.
Described as the moment in which data becomes information, data visualization is one of the final and most important steps of the data science lifecycle. Incorporating communication, storytelling, and aesthetics, visualizing a dataset through charts and graphs is one of the easiest ways for data scientists to present their findings to an audience.
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