Common tools for data analysis
There are currently three mainstream data analysis languages: Python, R, and MATLAB. in:
- Python has a rich and powerful library. It is often called a glue language. It can easily connect various modules (especially C/C++) made in other languages. It is an easier to learn and more rigorous programming. language;
- The R language is a language and operating environment for statistical analysis and graphing. It belongs to a free, free and open source software of the GNU system;
- The role of MATLAB is to perform matrix operations, draw functions and data, implement algorithms, create user interfaces, and connect programs in other programming languages. It is mainly used in engineering calculation, control design, signal processing and communication, image processing, signal detection, financial construction model design and analysis.
Data analysis can be performed in all three languages: Python, R, and MATLAB. Table 1 compares these three data analysis tools in five aspects: language learning difficulty, usage scenarios, third-party support, popular fields, and software costs.
| data analysis tool | Python | R language | MATLAB |
|---|---|---|---|
| Difficulty level of language learning | Unified interface, smooth learning curve | Numerous interfaces and steep learning curve | Large degrees of freedom and gentle learning curve |
| scenes to be used | Data analysis, machine learning, matrix operations, scientific data visualization, digital image processing, web applications, web crawler, system operation and maintenance, etc. | Statistical analysis, machine learning, scientific data visualization, etc. | Matrix operations, numerical analysis, scientific data visualization, machine learning, digital image processing, digital signal processing, simulation, etc. |
| Third Party Support | With a large number of third-party libraries, it can easily call other programming languages such as C, C++, Fortran, java, etc. | Has a large number of packages capable of calling C, C++, Fortran, Java and other programming languages | Has a large number of professional toolboxes, and added support for C, C++, Java in the new version |
| popular field | industry | economic circle | academia |
| software cost | open source free | open source free | Commercial charges |