Emerging technologies like AI, data science and machine learning are all about working with intelligent models that need good algorithms to run. For instance, logistic regression or support vector machines.To understand these algorithms and how they work, one must be adept at programming languages.
Here, we discuss 11 crucial programming languages for data scientists.
C/C++ are usually the first languages one learns when entering the world of programming. These languages give learners insights into the basics of programming and how it works. In machine learning and data science, libraries and frameworks are essential to tackle complex computational tasks. Popular languages like C and C++ have a number of interesting libraries that makes it easy and accessible for data scientists to work on complex computational projects.
Click here for libraries in C/C++ for machine learning and data science.
Introduced in 2015, Julia is one of the popular languages ideal for data science, scientific computing, parallel computing, data mining, machine learning, among others. This language uses multiple dispatches as a paradigm and can be used as a general-purpose programming language where you can easily code and write software in the application domains.
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Java is one of the oldest languages used for various enterprise development purposes. As one of the oldest languages, it comes with a great number of libraries and tools for ML and data science. Java has a number of libraries and tools — the popular ones being Weka, Java-ML, Deeplearning4j — which are leveraged to solve most of the cutting edge machine learning problems. Also, Java is 25 times faster than Python.
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Programming language like Common Lisp helps in creating flexible computational models. Programs that analyse the sequence data, graph knowledge, and tabular data can be written easily, and can be made to work together naturally in Lisp. The language allows the computer program to examine, introspect, as well as modify its own structure and behavior at runtime, making it ideal for artificial intelligence and machine learning applications. This language is suitable for bioinformatics and computational biology research.
Developed by MathWorks, MATLAB is a multi-paradigm programming language and numeric computing environment for complex computations. As data science is all about large swathes of data and numbers, MATLAB is ideal to gain insights from the data and visualise them. MATLAB code can be integrated with other languages, enabling developers to deploy algorithms as well as applications within web, enterprise, and production systems.
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Python is a general-purpose coding language. The main reason why this language is so popular among the developers is its plethora of libraries and frameworks, which help in performing complex computational tasks. The language has interfaces to many system calls and libraries, as well as to various window systems, and is extensible in C or C++.
R is a popular statistical language that has gained much traction over the last few years when it comes to processes of data analytics and visualisations. With better data visualisation techniques, this programming language offers an essential role in statistical methods. The language can be used for effective data analysis and gain meaningful insights from it.
SQL or Structured Query Language has become one of the go-to languages for the aspiring data scientists. Most organisations use this language for analysing the tons of raw and unstructured data. The language is used for both data management and data analysis. Due to the speed advantages of SQL, recruiters expect developers to be proficient in it.
Scala or SCAlable LAnguage is a Java-like programming language designed to express common programming patterns in a concise, elegant, and type-safe way. The language is excellent for large-scale projects. It provides a lightweight syntax for defining anonymous functions and supports higher-order functions as well as allows functions to be nested apart from supporting multiple parameter lists.
SAS, previously known as the Statistical Analysis System is a popular programming language that provides users with a host of product components, including asset performance analytics, analytics for IoT, decision making, and more. One of the best features of this language is allowing data in any format, ranging from SAS tables to Excel worksheets. SAS can also manage and manipulate data to obtain important information.
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A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.