Today, I'm going to list down 5 best programming languages which helps a lot in data analysis.
Let's get started🚀
Python programming language is extremely versatile, dynamic
and has a wide range of uses, Now a days most of the programmers are already familiar with it. Python is a must-have for any data analyst. It is a simple language compare to other languages, and focuses more on readability.
Python has a huge range of resource libraries suited to a variety of different data analytics tasks. For example, the NumPy and pandas libraries are great for streamlining highly computational tasks, as well as supporting general data manipulation. Libraries like Beautiful Soup and Scrapyare used to scrape data from the web, Matplotlib is used for data visualization and reporting. Python’s main drawback is its speed, it is memory intensive and slower than many languages.
Visit official site: https://www.python.org/
R is a popular open-source programming language. It is commonly used for statistical analysis, data visualization, and data manipulation. It can easily manipulate data and present it in different ways.
R is built specifically to deal with heavy statistical computing tasks and it is very popular for data visualization. R’s syntax is more complex compared to python language. It integrates well with other languages and systems including big data software. main drawback is its poor memory management.
Visit official site: https://www.r-project.org/
SQL is not a typical programming language its a domain-specific language, it's like a secret language for talking to databases. Structured query language is used to interact with the database for accessing, cleaning, and analyzing the data that is stored in databases. With SQL queries, users can easily analyze the data and can get the valuable information.
Julia is also a best tool in data science field and it helps a lot in numerical computing. Sometimes it's referred to as the inheritor of Python, Julia is a highly effective tool compared to other languages used for data analysis.
Julia is not as widely adopted as languages such as Python and R. It has a smaller community and doesn't have as many libraries as Python and R. Despite this, Julia is also a promising language for data science due to its speed, clear syntax and versatility, and there are many use cases where it excels.
Visit official site: https://julialang.org/
esProc SPL is a Low code, High performance, Lightweight, Versatility scripting language for data processing. It has a well-designed rich library functions and powerful syntax, which can be executed in a Java program through JDBC interface and computing independently. It has better functionalities than the other data processing languages based on JVM (Such as Kotlin and Scala).
It uses self created SPL( Structured Process Language) syntax and its more concise and efficient to use. It is mainly used for computing and processing of structured and semi-structured data. The processing speed of SPL is more compared to other programming languages.
Visit official site: https://www.scudata.com/
Thanks for reading :)