In this post we will know the Best Programming Languages to learn Data mining and analytics. Who want to start their career towards Data Mining then this article will beneficial to choose which programming language better one.
Data mining is the process of extracting raw data into useful information. By using software, businesses can learn more about their customers, check patterns in large batches of data and develop more effective marketing strategies as well as increase sales and decrease costs. Data mining depends on a collection of data and computer processing.
Data mining tools are used to precisely predict future behaviors and drifts thus allowing businesses to make informed decisions. There are several techniques for data mining and these include looking for incomplete data, dynamic data dashboard, and database analysis.
There are several languages are used for data mining but the following are main programming languages. They are
1.R
R is a programming language and software environment for statistical analysis, graphics representation and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team.It is the most popular data analytics tool as it is open-source, flexible, offers multiple packages and has a huge community. But apart from being used for analytics. banking analyst used excels files, but now R is increasingly being used for financial modelling particularly as a visualisation tool. R is the best language for data modelling although its power becomes limited when a company produces large scale products
Why R?
2. Python:
Python is also a suitable programming language for data mining with more practical capabilities and fast data mining capabilities to make a good product. It can be used for statistical analysis that was initially the forte of R. It has emerged as an excellent option in the processing of data creating a trade-off between sophistication and scale.
In many banks, they are using Python to build the interface and new products. Python is broad and flexible, so people easily assemble to it. But still it is not the highest performance language, and occasionally it powers large scale infrastructure.
3. Julia:
Julia is a high-level, high-performance dynamic programming language for numerical computing. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.
The majority of data mining today is conducted through Java, MatLab, R, and SAS. There is still a gap which is filled by Julia. Julia is widespread industry adoption, and it is high level, fast and expressive language.It is more scalable than Python and R.
Data mining is the process of extracting raw data into useful information. By using software, businesses can learn more about their customers, check patterns in large batches of data and develop more effective marketing strategies as well as increase sales and decrease costs. Data mining depends on a collection of data and computer processing.
Data mining tools are used to precisely predict future behaviors and drifts thus allowing businesses to make informed decisions. There are several techniques for data mining and these include looking for incomplete data, dynamic data dashboard, and database analysis.
There are several languages are used for data mining but the following are main programming languages. They are
1.R
R is a programming language and software environment for statistical analysis, graphics representation and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team.It is the most popular data analytics tool as it is open-source, flexible, offers multiple packages and has a huge community. But apart from being used for analytics. banking analyst used excels files, but now R is increasingly being used for financial modelling particularly as a visualisation tool. R is the best language for data modelling although its power becomes limited when a company produces large scale products
Why R?
- R is a programming and statistical language.
- R is used for data Analysis and Visualization.
- R is simple and easy to learn, read and write.
- R is an example of a FLOSS (Free Library and Open Source Software) where one can freely distribute copies of this software, read it’s source code, modify it, etc.
2. Python:
Python is also a suitable programming language for data mining with more practical capabilities and fast data mining capabilities to make a good product. It can be used for statistical analysis that was initially the forte of R. It has emerged as an excellent option in the processing of data creating a trade-off between sophistication and scale.
In many banks, they are using Python to build the interface and new products. Python is broad and flexible, so people easily assemble to it. But still it is not the highest performance language, and occasionally it powers large scale infrastructure.
3. Julia:
Julia is a high-level, high-performance dynamic programming language for numerical computing. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.
The majority of data mining today is conducted through Java, MatLab, R, and SAS. There is still a gap which is filled by Julia. Julia is widespread industry adoption, and it is high level, fast and expressive language.It is more scalable than Python and R.
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