These are the most popular programming languages that data scientists should know in 2021!

Programming languages are formal languages that comprise sets of instructions that help the machines to produce various kinds of outputs. These languages are used in computer programs to implement algorithms and have multiple applications. Programming skills are critical for any tech domain. There are several programming languages for data science as well. Data scientists and other data professionals should master these languages to efficiently leverage these advanced technologies. In this article, we list down 10 important programming languages that data scientists should know.

• Python: Python is a general-purpose programming language that can open new doors not only in data science but also in web and software development. It is an open-source programming language, groups data, and functions together for flexibility and composability. Python supports multiple data structures and uses plain English syntax, making it a great programming language for beginners.

• Javascript: Javascript is another object-oriented programming language used by data Hundreds of Java libraries are available today for covering every complex issue that a programmer may come across. This versatile language is capable of handling multiple tasks at once. Javascript is also useful in embedding everything from electronics to desktop and web applications.

• SQL: Learning SQL, or any structured query language, is vital for manipulating structured data. Large-scale datasets can contain millions of rows, making it difficult to find the exact information. SQL as a query language allows the users to adjust, locate, and check massive datasets. As a domain-specific language, it is most convenient to manage relational databases.

• Scala: Scala was created in 2003 and is considered a modern and elegant programming language: Initially, it was designed to address the issues with Java. Its applications range from web programming to machine learning. In modern-day business processes, Scala supports object-oriented and functional programming.

• Julia: It is another specialized language that is specifically designed for computations and numerical analysis. Although purpose-built, Julia provides versatility and supports both parallel and distributed computing, and is incredibly fast. The primary feature of Julia is fast performance; hence, it is perfect for data visualization, numerical analysis, deep learning, or interactive computing.

• R: R is a high-level programming language built by statisticians. This open-source language and software were initially built for statistical computing and graphics. It has several applications in data science as well as multiple useful libraries. R also comes in handy for exploring data sets and conducting ad hoc analysis.

• C & C++:Learning C and C++ offer excellent capabilities for building statistical and data tools. These languages can translate well in Python to promote performance-based applications. It is surprisingly useful when it comes to compiling data quickly. These programming languages can be built into highly functional tools and allow for serious fine-tuning.

• MATLAB: MATLAB as a programming language is specific to mathematical and statistical computing. It offers in-built tools for dynamic visualizations and also offers a deep learning toolbox that transitions well. It mainly allows the users to ease challenging mathematical processes. MATLAB provides in-built graphics for custom plot points and visualizations.

• SAS: SAS is a software suite that is commonly used to perform statistical modeling for disciplines like data management, business intelligence, multivariate analytics, and predictive analytics. The language has established itself as the best in the analytics industry. Users can utilize SAS to access data in multiple formats and also manage and manipulate it.

• TensorFlow: It is one of the leading libraries in numerical computing. It has an ML-based framework that is used to tackle massive datasets. TensorFlow works extremely well with distributed computing. The users can break down the graph into chunks and run them in parallel GPUs and CPUs.