Data Science Programming Languages Popular In 2022

The impact of Data Science has spread far and wide, amidst almost all industry domains. Not only business domains, but Data Science has also been a wanted technology for data scientists/data engineers. They are keen to enhance their expertise by learning the desired skills and programming languages. There are many software languages that are apt for Data Science and its related technologies.

No wonder, the profession of a data scientist has its own increasing popularity and demand! Here is a statistic that shows the number of data scientists employed in companies worldwide in 2020 and 2021.

Acquiring programming skills with respect to Data Science is a must. Be it adhering to business requirements or having a successful career path, gaining expertise in these languages is indispensable. It is tough to imagine a successful Data Science implementation without having applied a programming language.

Here, we discuss the top Data Science Programming Languages that have been popular in 2022. Prior to that, let us briefly go through the fundamentals of what Data Science is.

What Is Data Science?

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from noisy, structured, and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. – Wikipedia

Data Science is a cutting-edge, revolutionary technology that prepares data for analysis, cleans, aggregates manipulates it, and executes sophisticated data analytics on it. Data scientists then analyze the results to obtain insightful and real-time information with which business owners can take necessary business decisions, with a futuristic view.

It is mainly used to garner detailed knowledge about different processes and behaviors of data, fast and accurate processing of large amounts of information, protecting the sensitivity of data, and taking data-driven business decisions.

Also Read: What Is Data Science? Understanding In 200 Words

Key Advantages:

• Business understanding and optimization
• Insight into historical information
• Increased revenue generation
• Better marketing and development of products
• Data security and privacy of information
• Real-time intelligence and interpretation of complex data
• Faster decision-making process

All these advantages can be achieved by implementing Data Science algorithms, processes, and logic through different programming languages required for Data Science. Here are the most prominent of them.

Top Data Science Programming Languages

• Python
• R
• C/C++
• SQL
• Java/JavaScript
• F#
• Scala
• Julia
• MATLAB
• SAS

Python:

Python is one of the best functional programming language for Data Science. Python is widely used in areas of machine learning, deep learning, and artificial intelligence. Python’s libraries are competent enough to support the automation of different tasks such as visualization, data modeling, collection, and analytics. It can be leveraged for the creation of libraries and tools from start. Python is used for web development, game development, data analytics and visualization, programming applications, finance, etc.

Python is believed to be a universal language since it allows its developers for any type of project creation be it ML programs or simplistic apps. Since it is easy to learn and transparent, it is best for novices. There are many libraries’ add-on modules that assist in resolving most of the issues. It is considered apt for projects which include quantitative and analytical calculations, data mining, big data analytics. Being versatile, Python has data structures, dynamic binding, dynamic typing that makes it fit for complicated application development.

Some of Python’s libraries for Data Science are TensorFlow, Keras, PyTorch, Numpy, Scikit-Learn, Theano, Pandas, etc.

Main Characteristics:

• Server-side/backend web and mobile app development
• Big data processing and system script writing
• Analytical and quantitative calculations
• Easy to code, free and open-source
• Object-oriented and high-level language
• GUI programming support
• Portable and integrated language

R Programming Language:

R is considered a powerful scripting language and an important choice in the world of programming languages for Data Science. All due to its salient features like easy learning, statistical graphics, and computing, mathematical modeling, handling of complex data bundles, data processing, and many more. R is considered apt for data science experts. Since it is open-source, cross-platform, and lets developers work with different operating systems, it is a preferred choice of many. It has inbuilt features statistical capabilities that let developers have a thorough data visualization experience.

Mainly for the data analysts, statisticians, marketers, it can be used while performing sentiment analysis to understand what the customers think about a product or service. R is an imperative language that extracts the raw data and assists in analyzing, processing, transforming, and visualizing information. A variety of machine learning algorithms, prediction models, image processing packages can be developed with R. Some of the important R packages are DBL, XLConnect, dplyr, shiny, xtable, etc.

Main Characteristics:

• Comprehensive language with OOPS elements
• Wide range of support libraries
• Encourages developers for writing their own packages and libraries
• Addon packages like RODBC, ODBC etc.
• Strong graphical capabilities
• Highly active community support
• Distributed computing

C/C++:

C and C++ are well-known programming languages that have carved a niche for themselves in the Data Science arena. These are simple, powerful, and low-level tools that offer a wider command over Data Science applications. They are multi-paradigm in nature and are a must in the Data Science basket. C is a function-driven language and C++ is an object-driven language. They act as a fundamental language for the execution of high-level programming languages. It holds a wider command over machine learning and Data Science applications.

C++, considered the fastest programming language, is well utilized in Big Data combined with other languages such as Java. It is simple yet powerful. It is best used when there are large datasets to be handled and development of games, desktop apps, search engine development. It offers the least response time and is apt for developing enterprise software, cloud systems, finance, banking software, etc.

Main Characteristics:

• Machine independent and compiler-based
• Structured programming language
• Dynamic memory management
• Fast speed and enriched library
• Integration and extendibility
• Intermediate level language
• Abstraction and encapsulation
• Inheritance and polymorphism

SQL:

Structured Query Language (SQL) is a domain-specific and standardized programming language utilized for managing relational databases, stream processing, and other operations on data. It modifies the database tables and index structures by handling rows of data through addition, update, and deletion. It works best for huge volumes of Big Data since it possesses transactional and analytical competencies and hence it is considered best for data scientists.

SQL is applied well for data management in online and offline applications. It is considered efficient for data science projects because of its speed, domain specification, and flexibility, though it is non-procedural. It finds, explores, and extracts data in relational databases with speed and accuracy. Since the design is optimum and so is the data search facility, access to multiple tables is easier and more effective.

Main Characteristics:

• Fast speed and direct data access
• Simplicity and flexibility
• Compliant workflow with data science
• Data selection from tables
• Statistical functions and joins
• Text mining and regular expressions
• Data bucketing
• Function heavy languages

Java/JavaScript:

Java is a class-dependent, object-oriented, high-performance language that is reigning on the top for more than two decades. It is considered the ideal choice for writing algorithms in machine learning and data science. Latest technologies like data science tools, IoT devices, and Big Data gels well with Java and hence has a large area of pertinency. It is used heavily by industry segments for mobile and web app development. It offers high-end security mechanisms for safeguarding the sensitive information of projects.

JavaScript is a simple to learn, versatile, and popular programming language of the World Wide Web, along with HTML and CSS. Most websites use JavaScript for the client-side functioning, for the creation of interactive web pages and visualizations. It has a wide range of libraries that can offer a variety of solutions for data science projects and can resolve issues related to Big Data.

Main Characteristics Of Java:

• Not limited to any processor or computer
• Memory management and class libraries
• Data mining and data analysis
• Dynamic, secured, and robust setup
• Multiple Java libraries for different functionalities

Main Characteristics Of JavaScript:

• Light-weighted and object-oriented programming
• Platform independence
• Interpreted language
• Asynchronous processing
• Functional style and prototype driven

F#:

F# is one of the best functional programming language for Data Science. Since it is open-source, general-purpose, interoperable, and multi-paradigm, it is considered perfect for data science applications. Data is transformed with functions since it is data-oriented and a mature framework. It is functional-first and appreciated for its readable code and effective syntax. Since it has effective execution, strong libraries, REPL scripting, and scalable data integration, it is considered ideal for data science applications.

F# helps in creating a variety of applications in functional areas like gaming, IoT, Web API, etc. It works perfectly for data-driven and domain-driven development. It is a strongly typed programming language that helps in resolving complicated issues by means of simplistic coding. It is concise, correct, convenient, and concurrent. It assists in the regular development of conventional business software solutions.

Main Characteristics:

• Immutable and light in weight
• First-class functions
• Type inference and automated generalization
• Pattern matching
• Access to vast libraries and device bases
• Algebraic data types
• Concise syntax

Scala:

Scala is a modern-day, strong, statically typed, and multi-paradigm language that is used for data science applications. It offers a perfect combination of object-oriented programming and functional programming into a single language. There are static types that help in reducing errors in complicated apps. It reveals certain common programming trends in an effective and crisp manner. It has seamless integration with Java and JavaScript. Scala works best on parallel procedures while executing big data arrays.

Data scientists can make the most of Scala on different operational processes. There are many competent libraries that data scientists can use – Smile, Vegas, etc. Scala is recommended with the bulk of data is big. It supports higher-order functions, anonymous functions, inner classes, generic classes, compound types, polymorphic functions, and nested functions. As the name suggests, it is a scalable language fit for the development of front-end applications.

Main Characteristics:

• Immutability and concurrency control
• String interpolation
• Case classes and matching patterns
• Concise and easy to understand
• Scalable, with concurrency control
• Ideal for high volume data sets
• Interoperability with Java

Julia:

Julia is a functional, high-level, general-purpose language that is meant for numerical and technical computing. It is fit for low-level systems programming, is dynamic, open-source, and easy to use. It is a functional language that functions well with functional recursive loops. It is a fast one meeting the needs of data modeling in an interactive environment. The working of Julia is fast and done by implementing C or Python language libraries.

The key feature of Julia is its numerical analysis technology apart from general programming methods. Financial projects make the best use of Julia because of its numerical and statistical capabilities. It is a data science-driven language that is fast in handling mathematical fundamentals like linear algebra and matrices. It is considered one of the fasted scripting languages as compared to its peers.

Main Characteristics:

• Solving mathematical issues at a faster speed
• Works faster with data
• Numerical analysis and scientific computing
• Dynamically typed language
• Good performance and multiple dispatches
• Interactive and compiled
• Supports metaprogramming

MATLAB:

MATLAB is considered an ideal programming language when it comes to the involvement of a series of mathematical functions. It comes in handy for the implementation of mathematical modeling, data analysis, and image processing. It is easy in its simulation scripts and has a wide library of functions fit for statistics, linear algebra, optimization, filtering, numerical integration, and so on.

MATLAB offers an easily created user interface because of its inbuilt graphics that can be of great use for the creation of data plots and data visualization. It is widely used for a variety of applications like deep learning and machine learning, image and video processing, test and measurement, computational finance and biology, control systems, etc. It is easy to integrate a package with other packages through a single line of code or a few lines of code. It gets you faster results as compared to its peers.

Main Characteristics:

• Huge library of mathematical functions
• Inbuilt graphics for data visualization and plots
• Easy transition to deep learning
• Insightful mathematical operations
• Execution of mathematical modeling
• Specialized in Big Data processing
• UI creation and implementation of algorithms

SAS:

SAS analytics software and solutions is considered apt for obtaining maximum value, taking confident decisions, getting faster outcomes, and open integration. Just like Python and R, SAS is also a popular data analysis programming language. It can flexibly work with statistics and hence it is considered an optimal choice for data scientists. Though it is not open-sourced, it offers a lot of advantages like functions for predictive modeling, advanced analytics, business analytics, etc.

SAS is best recommended where the demand for security and stability is more. It has strong data analysis attributes and hence is a popular choice for data engineers and scientists, business analysts, forecasters, statisticians. With its extensive approach to data transformation, it is considered a leader in business analytics, and it is more like a data management service and software. It gives an insight into data with a detailed view on business outcomes.

Main Characteristics:

• Robust data analytical capability
• Detailed control over data manipulation
• Customizable components for industry units
• Report output formats
• Data encryption algorithms
• Support for different data formats
• Transforms data into intelligence

Summing It Up:

As the world of Business Intelligence and Analytics widens, Data Science becomes an integral part of it, it is a tough call to take – which Data Science programming language to choose. But here are some hints that may prove helpful in choosing the language, though there are many other parameters that must be considered like resources, budget, the scope of work, organizational needs, skill level, etc.

• If you are a DBA, business analyst, data analyst, data engineer – SQL or Python would work well
• If you are a marketing scientist, statistician, BI developer – R would work well apart from Python or SQL
• If you are a data architect/data scientist – Java, Python, C, C++, JavaScript would be apt