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The campaigns led to endorsements from politicians, the inclusion of programming in state school curricula, and the proliferation of coding bootcamps. Learning to code has a long history in the U.S., with moments of enthusiasm and anxiety about computational literacy and the best methods to learn programming skills.
Between 2000 and 2010, computer book and magazine publishers declined significantly as providers of programming instruction, as programmers moved to Internet resources to expand their access to information. This shift brought forward new digital products and mechanisms to learn programming skills.
CodeMonkey is an educational computer coding environment that allows beginners to learn computer programming concepts and languages. [2] [3] [4] CodeMonkey is intended for students ages 6–14. Students learn text-based coding on languages like Python, Blockly and CoffeeScript, as well as learning the fundamentals of computer science and math. [5]
BASIC (Beginners' All-purpose Symbolic Instruction Code) [1] is a family of general-purpose, high-level programming languages designed for ease of use. The original version was created by John G. Kemeny and Thomas E. Kurtz at Dartmouth College in 1963.
Treehouse or (Teamtreehouse) is an online technology school that offers beginner to advanced courses in web design, web development, mobile development and game development. [3] Its courses are aimed at beginners looking to learn computer coding skills for a career in the tech industry.
Lisp is the second oldest family of programming languages in use today and as such has many dialects and implementations with a wide range of difficulties. Lisp was originally created as a practical mathematical notation for computer programs, based on lambda calculus, which makes it particularly well suited for teaching theories of computing.
[7] [8] [9] The field of computer science education encompasses a wide range of topics, from basic programming skills to advanced algorithm design and data analysis. It is a rapidly growing field that is essential to preparing students for careers in the technology industry and other fields that require computational skills.
The library NumPy can be used for manipulating arrays, SciPy for scientific and mathematical analysis, Pandas for analyzing table data, Scikit-learn for various machine learning tasks, NLTK and spaCy for natural language processing, OpenCV for computer vision, and Matplotlib for data visualization. [3]
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