Top programming language for data science

Top programming language for data scienceUntitled Part 1

Data science is a multidisciplinary field that requires a broad set of skills from mathematics and statistics to programming, machine learning, and data visualisation. The most popular programming languages for data science are Python, R, SQL, Julia, and JavaScript. These languages offer different strengths and weaknesses, so the best language for you will depend on your specific needs and goals. The most popular languages for are:

1. Python - Python is a versatile programming language that is simple to understand and master. It is a powerful tool for data analysis and machine learning as it contains a wide library of data science modules and packages.Moreover, it is easy to learn even for the beginners as it is a beginner friendly language. Python is also blessed with a large number of communities so if any one gets stuck in the middle can easily find help and support.

2. R - It is another programming language primarily used for data science. For data cleansing, statistical modelling, and data visualisation, it provides a large range of statistical operations that are employed by many data scientists. It is an open-source language so anyone can have access to the language and like python, it has a large and active community that is always ready to help you out whenever required.

3. SQL - SQL (Structured Query Language) is a database language that is used to interact with relational databases. So, anyone who wants to interact and play with large sets of data must know the concepts of SQL. So, it's one of the most favourite languages for data scientists who need to access and manipulate large datasets. It is also widely used to perform data analysis tasks and also used to generate reports. After connecting it with some data visualisation softwares such as power bi and tableau, we can also perform graphical representation of the data or reports that we have generated with SQL.

4. Julia - For numerical computation, Julia is a high-level, high-performing dynamic programming language. It is a fantastic choice for data science since it is likely to be rapid, expressive, and adaptable. Moreover, it can be much faster than python and R and can be used for a variety of tasks including machine learning, data visualisation and numerical computing.

However, it may be lesser compared to python and R in terms of online community and support.

5. Java script - JavaScript is a general-purpose programming language that is primarily used for creating interactive web pages. But it's also gaining popularity in data scientific fields like machine learning and data visualisation. D3.js, Numjs, Tensorflow.js are some of the libraries used for data science purposes.

However, if someone is serious about data science and machine learning, he/she must opt for python or R as they are more widely used and have more libraries present in it for this purpose. One more thing to know is that python and R works faster than javascript which makes them more considerable for data science as compared to javascript.

6. Scala - Scala is a general-purpose programming language that is designed to be concise, expressive, and scalable. It is a programming language for a variety of tasks, including data science, machine learning, and big data. There are a number of libraries available for Scala for data science, including Spark, MLlib, and Breeze. However, Scala has a steeper learning curve than some other languages, such as Python and R. This can be a challenge for beginners.

Conclusion

Though python and R are widely used in this data science and machine learning domain, the best way to choose a is to experiment with a few different languages and see which one you are most comfortable with. There is no one right language for everyone as someone may find python easier and another person may find some other language suitable to them, and the best language for you will depend on your specific needs and goals.