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1975-2013, R 0 RS, R 1 RS, R 2 RS, R 3 RS, R 4 RS, R 5 RS, R 6 RS, R 7 RS Small Edition [42] [43] Seed7: Application, general, scripting, web Yes Yes No No Yes Yes Multi-paradigm, extensible, structured No Simula: Education, general Yes Yes No No No No discrete event simulation, multi-threaded (quasi-parallel) program execution Yes 1968 Small Basic
Python 3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python 2.7.18, released in 2020, was the last release of Python 2. [37] Python consistently ranks as one of the most popular programming languages, and has gained widespread use in the machine learning community. [38] [39] [40] [41]
Website, Playground, Learn, esjs on GitHub: Swahili: swap Swahili Programming Language; it is an educational programming language built over Nodejs, It was built as way to help Swahili speakers to easily grasp the concept of coding and programming in general. Swedish: Enkelt: A language with a completely Swedish-based syntax. Transpiled to Python.
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There is also an active R community around the tidyverse. For example, there is the TidyTuesday social data project organised by the Data Science Learning Community (DSLC), [16] where varied real-world datasets are released each week for the community to participate, share, practice, and make learning to work with data easier. [17]
R is a programming language for statistical computing and data visualization. It has been adopted in the fields of data mining, bioinformatics and data analysis. [9] The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data. R software is open-source and free software.
Moravec's paradox is the observation in the fields of artificial intelligence and robotics that, contrary to traditional assumptions, reasoning requires very little computation, but sensorimotor and perception skills require enormous computational resources.
Anaconda is a distribution of the Python and R programming languages for scientific computing (data science, machine learning applications, large-scale data processing, predictive analytics, etc.), that aims to simplify package management and deployment. Anaconda distribution includes data-science packages suitable for Windows, Linux, and macOS ...
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