Reposted from: Rookie Tutorial
TIOBE's programming language rankings for August 2023 have been announced. The official title is: Julia enters the TIOBE index top 20 for the first time ( Julia enters the TIOBE index top 20 for the first time ).
This is the first time in the history of Julia that it has entered the top 20 of the TIOBE index!
First released in 2012, Julia is a young language that supports a variety of platforms: macOS, Windows, Linux, FreeBSD, Android, and more.
Julia is a high-level general-purpose dynamic programming language, which was originally designed to meet the needs of high-performance numerical analysis and computational science, does not require an interpreter, is fast, and can also be used for client and server Web purposes, low-level system programming Or used as a specification language.
Julia is good at data science and mathematical computing. With the development of big data and artificial intelligence in the past few years, Julia is also known to more people, but the language competition in this field is fierce, and we know that among the top 20 Python, R, and MATLAB. But Julia also has its unique advantages. Julia is faster than Python, more suitable for writing large-scale systems than R, and cheaper than Matlab. Therefore, speed, scalability, and open source make Julia another choice for artificial intelligence enthusiasts.
Julia official website: https://julialang.org/
Julia's historical ranking change chart:
One of the design goals of the Julia language is to provide support for high-performance scientific computing and numerical computing, so it has been widely used in many application areas. Some commonly used areas are listed below:
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Scientific research: Julia is used in a variety of scientific research areas, including physics, chemistry, biology, and more. Researchers can use Julia for numerical simulations, data analysis, modeling, and simulation.
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Data science and data analysis: Julia provides powerful data processing and analysis functions, which can be used to process large-scale data sets, data cleaning, feature engineering and statistical analysis. Its high performance enables faster computation when dealing with large data sets.
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Machine Learning and Artificial Intelligence: Julia's performance makes it useful in the field of machine learning and deep learning as well. Many machine learning libraries and frameworks have been optimized for Julia, making it ideal for implementing and training complex models.
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Financial and quantitative analysis: Since the financial field needs to deal with large amounts of data and complex mathematical models, Julia's high performance makes it one of the tools for financial modeling, risk management and quantitative analysis.
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Engineering simulations and simulations: Julia can be used to perform engineering simulations and simulations such as aircraft design, material simulations, fluid dynamics analysis, and more. Its high performance and flexibility help speed up the simulation and analysis process.
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Big Data Processing: Julia's parallel computing and distributed computing capabilities make it suitable for large-scale data processing tasks, such as data mining, graph analysis, and distributed computing.
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Scientific visualization: Julia has a rich visualization library that can be used to create high-quality data visualization charts and graphs to better understand data and model results.
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Optimization and numerical solution: Julia has excellent numerical calculation and optimization libraries, which can be used to solve linear and nonlinear optimization problems, as well as mathematical problems such as ordinary differential equations.
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Medical image processing: Julia also has applications in the field of medical image processing, which can be used for image segmentation, feature extraction, and medical image analysis.
In summary, Julia's high performance and flexibility make it suitable for many fields of science and engineering, especially applications that need to deal with large-scale data and complex calculations. As time goes by, its application fields may be further expanded and deepened.
This month, the TOP 20 has not changed much. The top ten are:
Python，C，C++，Java，C#，JavaScript，Visual Basic，SQL，Assembly language，PHP。
Python, C, C++, Java, C#, JavaScript, Visual Basic, SQL, Assembly language, PHP historical change curve:
Programming languages ranked 21-50
Historical ranking of major programming languages (1988-2023)
Programming Language "Who's Who" (2003-2022)
TIOBE publishes a ranking of programming languages every month. This ranking is based on the number of global technical engineers, courses and third-party suppliers, including popular search engines and technical communities, such as Google, Baidu, Wikipedia etc.
The data reflects the changing trend of the current mainstream programming languages, which can be used as a reference for our study and work, but each language has its own suitable application scenarios, so don't care too much, after all, the quality of a language lies in how to use it.
Reference: https://www.tiobe.com/tiobe-index/