A programming language is an important tool in data science. Sometimes it is a problem itself that inducts which programming language you should use.
If you have to choose in which language you should implement the research, you have to know the possibilities that offer each programming language.
In this article, there is a list of the best programming languages for data science, with its pros/cons, so you make the right decision.
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Python is the most known programming language for data science, and it has a reason why: due to the wide range of uses. Python is the best for machine learning, deep learning, artificial intelligence.
The main reason for learning Python for Data Science is its powerful libraries like MatPlotLib, NumPy, Pandas, TensorFlow, Scikit-learn, Keras, and much more.
Using libraries like Pandas, you can easily clean your data and transform them into the format you need. With TensorFlow, you can do serious Machine learning and create a powerful model.
Python offers solutions for crucial tasks, such as data collection, data analysis, data visualization. Each of these tasks is crucial in data science.
The large community of Python programmers is another reason why you should try to learn Python. There will be someone to help you in solving the problem.
Pros: The libraries. You will always find a library that works exactly for your problem. All you need is to know is how to import it, and you are done.
The community of developers is also a huge help in problem-solving.
Cons: The biggest complaint about Python is the speed it runs. Python is relatively slow compared to other programming languages.
R is one of the most popular languages used in data science. R is very easy to learn. It creates a user-friendly environment for statistics and data visualization.
These are the reasons why so many scientists choose R for data science, big data, and machine learning.
R can handle large and complex data sets. It is compelling when performing statistical operations.
Pros: R has numerous advantages. It is open-source. It offers multiple packages for data analysis and data visualizations, for building high-quality graphics as well as various machine learning operations.
Cons: The biggest downside of using R is security. R lacks basic security. The application written in R cannot be a web application.
JavaScript is the most popular language. It is the most used for web development. Besides this, the interactive pages create a friendly environment to build the data visualizations.
This is the reason why we list JavaScrips among the programming languages for data science.
In fact, JavaScript excels at data visualization. Libraries such as D3.js, Chart.js, Plotly.js, and many others make powerful data visualization and dashboards really easy to build.
Another tool to choose among JavaScript isTensorflow.js. This brings machine learning to JavaScript developers — both in the browser and server-side.
Pros: JavaScript is amazing for creating visualizations, which can be very helpful when working with big data.
Cons: Unfortunately, JavaScript doesn’t have the range of data science packages built-in functionality compared to some of the more popular data science languages.
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SQL is the language that handles structured data. For this reason, it becomes a very practical resource for data science.
A database is an important component in data science. Therefore, a database language such as SQL is a necessity. Every data scientist who has to deal with queries and relational databases should know SQL.
Pros: SQL is a non-procedural language. This makes SQL much easier because you don’t have to be a programmer to write SQL queries.
Cons: SQL has not a user-friendly interface. Therefore, many researchers find it confusing and try other options.
MATLAB is a powerful tool for mathematical and statistical computing that allows the implementation of algorithms and user interface creation.
The graphical representations are easily generated with MATLAB, due to its built-in graphics for creating data plots and visualization.
MATLAB is the favorite programming language of mathematicians because it is so much used in academia, for teaching linear algebra and numerical analysis.
Pros: MATLAB offers a huge library of predefined functions that provides tested and prepackaged solutions to many primary technical tasks.
Cons: It is also not free and can be a costly program to use compared to a traditional compiler.
Conclusion: whatever programming language you choose to learn for data science, learn also Python. I suppose you will need it…
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