Скачать или смотреть Data Science Battle: Python vs. R Explored

What is Python?
Python is a general-purpose, object-oriented programming language that emphasizes code readability through its generous use of white space. Released in 1989, Python is easy to learn and a favorite of programmers and developers. In fact, Python is one of the most popular programming languages in the world, just behind Java and C.

Several Python libraries support data science tasks, including the following:

Numpy for handling large dimensional arrays
Pandas for data manipulation and analysis
Matplotlib for building data visualizations

Explore the basics of these two open-source programming languages, the key differences that set them apart and how to choose the right one for your situation.
If you work in data science or analytics, you're probably well aware of the Python vs. R debate. Although both languages are bringing the future to life - through artificial intelligence, machine learning and data-driven innovation - there are strengths and weaknesses that come into play.

In many ways, the two open source languages are very similar. Free to download for everyone, both languages are well suited for data science tasks - from data manipulation and automation to business analysis and big data exploration. The main difference is that Python is a general-purpose programming language, while R has its roots in statistical analysis. Increasingly, the question isn't which to choose, but how to make the best use of both programming languages for your specific use cases.

What is Python?
Python is a general-purpose, object-oriented programming language that emphasizes code readability through its generous use of white space. Released in 1989, Python is easy to learn and a favorite of programmers and developers. In fact, Python is one of the most popular programming languages in the world, just behind Java and C.

Several Python libraries support data science tasks, including the following:

Numpy for handling large dimensional arrays
Pandas for data manipulation and analysis
Matplotlib for building data visualizations
Plus, Python is particularly well suited for deploying machine learning at a large scale. Its suite of specialized deep learning and machine learning libraries includes tools like scikit-learn, Keras and TensorFlow, which enable data scientists to develop sophisticated data models that plug directly into a production system. Then, Jupyter Notebooks are an open source web application for easily sharing documents that contain your live Python code, equations, visualizations and data science explanations.

What is R?
R is an open source programming language that's optimized for statistical analysis and data visualization. Developed in 1992, R has a rich ecosystem with complex data models and elegant tools for data reporting. At last count, more than 13,000 R packages were available via the Comprehensive R Archive Network (CRAN) for deep analytics.
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Popular among data science scholars and researchers, R provides a broad variety of libraries and tools for the following:

Cleansing and prepping data
Creating visualizations
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