Best data science programming languages every data scientist should know and master in 2021. Data scientists should learn and master at least one language as it is an essential tool to realize various data science functions.
Data science is still an emerging field and thus has a high-demand and lucrative job market. But for anyone looking to break into the data science industry, getting started can be daunting.
Learning data science can lead to a very lucrative career with a vast amount of employment opportunities. Learn more about data science by reading our guide on “how to become a Data Scientist.”
⇒Join us on Telegram for more Sure and Accurate football-winning tips every day...click here
There are many languages to consider learning for an aspiring data scientist. Below we highlight some of the best programming languages for data scientists in 2021.
Python programming language
Python is the most widely used data science programming language in the world today. It is an open-source, easy-to-use language that has been around since the year 1991. This general-purpose and dynamic language is inherently object-oriented. It also supports multiple paradigms, from functional to structured and procedural programming.
Python is best used for automation. Automating tasks is extremely valuable in data science and will ultimately save you a lot of time, and provide valuable data. Python can also support very important tasks, such as data collection, analysis, modeling, and visualisation which are all key factors to work with in big data.
JavaScript for data science
JavaScript is the most popular programming language to learn. It is most commonly used for web development due its capability of building rich and interactive web pages. That being said, it also finds a home in the data science world. JavaScript is an amazing choice for creating visualizations, which is an excellent way to convey big data.
This versatile language is capable of handling multiple tasks at once. It is also useful in embedding everything from electronics to desktop and web applications. Popular processing frameworks like Hadoop run on Java. And it is one of those data science languages that can be quickly and easily scaled up for large applications.
SQL for data and statistics
SQL is a very important language to learn in order to be a great data scientist. It is so important because a data scientist needs SQL in order to handle structured data. SQL gives you access to data and statistics which makes it a very useful resource for data science. This domain-specific language is extremely convenient for storing, manipulating, and retrieving data in relational databases.
A database is necessary for data science, thus making using a database language such as SQL a necessity. Anyone dealing with big data will need to have a sound knowledge of SQL in order to query databases.
R programming language
R is a high-level programming language built by statisticians. The open-source language and software are typically used for statistical computing and graphics. But, it has several applications in data science as well and R has multiple useful libraries for data science. R can come in handy for exploring data sets and conducting ad hoc analysis.
R is best used in the world of data science. It is especially powerful when performing statistical operations. R is a powerful scripting language. This being so, means that R can handle large and complex data sets. This combined with it’s ever growing community makes it a top tier option for an aspiring data scientist.
Julia – data science programming language
Julia is another language rising in popularity. It is a multi-purpose programming language that is designed for numerical analysis and scientific computing. Its popularity has risen due to its focus on performance. This has made it a top choice among high-profile businesses focusing on time-series analysis, risk analysis, and space mission planning.
Julia is a data science programming language that has been purpose-developed for speedy numerical analysis and high-performance computational science. It can quickly implement mathematical concepts like linear algebra. Julia is best used for data visualization, operations on multi dimensional datasets, and deep learning due to its built-in support for a package manager.
