Economics is considered as the branch of social science which aims at the satisfaction of needs and wants with the allocation of available resources. It is termed the study of scarcity and choice. Economists have this heavier task called big data analysis. They have to allocate the available resources to the needed ones. For big data, analysis coding can support economists. These big data cannot be managed in a spreadsheet file. For this they needed an updated and systematic Database system. so for this purpose coding will help the economists.
Coding can be termed as a process of giving commands to the computer that it can understand to perform a specified task. The computer knows only BInary or Machine language. So coding is completely done in a way that the computer can understand. So from basic shut down and power on to the analysis of data, creating websites, mobile apps, controlling automated cars everything is done by coding. So coding has a continuous effect on information technology which is widely used today.
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The economists may not deal with big data in the past as much as the data scientists did. But now in this technologically advanced world where everything has digitalized the need for big data analysis and consumer-friendly web services are inevitable for economists. Also, it makes the future of this sector safe. The future will be technologically highly productive. So to keep up with that and to stay updated the economists must be adept with the coding. The main objectives for coding supports in economics are:
- To easily manage big data
- To keep accurate data
- To maintain efficiency in the field
- To ensure productive results
- To allocate resources to the needy effectively
- To maintain well organized and systematic workflow
- To forecast future prospectus
- Time management
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A large number of programming languages are used in the economic field. But most of them are general-purpose programming languages. Most prominently four languages are preferred for economists. These are numerical programming languages. They are:
- JULIA
- Matlab
- Python
- R
In economics, resource allocation aims to know which is best and which is worst. For this purpose, these four programming languages help a lot.
- Julia
This is mainly used for data science. Julia covers some of the downside parts of python so that it will be a convenient tool for economists. Data mining is also possible with Julia. The feature which is the same as python for Julia is the simple syntax. The main advantage of Julia is its speed. Another key feature of this programming language is Automatic memory management. Julia is best apt for statistics which is an integral part of economics. Julia is created for addressing the downsides of other programming languages which offers the programmer a convenient tool.
- Python
Python cannot be said as a programming language made for data science but it provides so much help to data scientists. Even though the startup time for python is might be slower than Julia its run time is modest and take only less time to run programs. Among these languages, python is more popular and convenient. Python is adapting to new day requirements makes it faster.
- Matlab
Matlab is a numerical programming language that helps data analysts and engineers. It is an interactive programming language. Matlab is mainly used to analyze big data. Matlab also provides a great library of mathematical functions to users. Applications like computational finance, Test and Measurements, and control systems are run by Matlab.
- R
This programming language mainly aims at statistical computing. This is mainly used by data analysts, statisticians, data miners, etc.R programming language includes linear and non-linear modeling. Like the python community, the R community is exceptional for contributing packages.
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In this tech-savvy world, all industries are striving to cope with changes and achieve their goals. So in the field of economics also updates are coming. Usual old spreadsheets cannot manage big data and coding is the next big step for them to survive in the modern world.
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