Why use Python for Data Science?

You can use several different languages for data science, but Python is one of the most popular. Nearly any language is capable of analyzing data, but some languages and libraries are designed with certain expectations; for instance, the NumPy library provides tools for processing matrices so that you don’t have to write a matrix library on your own.

Python, as a language, has a few advantages over many others. First, it is famous for being relatively easy to read. While Python code may not make sense to someone completely unfamiliar with computer programming, it tends to be easier to parse than, say C or C++. That means Python is easier for other people to reuse, because they can read your code and understand what it claims to do, and they may even be able to add to it. Furthermore, Python has several strong purpose-built libraries geared specifically toward data science. Because existing Python data science libraries already provide many of the things data scientists often need to do, Python has earned a rightful place as a leading language in the field.

All other benefits of Python apply, such as the convenience of the pip package manager, the robust venv virtual environment interface, an interactive shell, and so on.

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Data science

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data,[1][2] and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data.Data science is a “concept to unify statistics, data analysis, informatics, and their related methods” in order to “understand and analyze actual phenomena” with data.[3] It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. Turing Award winner Jim Gray imagined data science as a “fourth paradigm” of science (empirical, theoretical, computational, and now data-driven) and asserted that “everything about science is changing because of the impact of information technology” and the data deluge.[4][5] Continue reading “Data science”