Top 5 Data Science Programming Languages

Top 5 Data Science Programming Languages

If you are interested in becoming a Data Scientist, you may need to become proficient in several Data Science programming languages because a single language can’t solve problems in all areas. Here are the 5 best data science programming languages to Learn in 2021.

Are you an aspiring Data Scientist? If yes, to get started, you will need to identify the top programming languages to learn for data science. Alternatively, do you want to hire data scientists for your project? Then, you would need to interview them. Therefore, you need to know the top programming language skills to look for. Read on as we identify the top 5 data science programming languages. We also discuss their pros and cons in the context of data analytics projects.

Top 5 Data Science Programming Languages

Here are the top 5 data science programming languages you should learn in 2021.

Best Data Science Programming Languages

1. Python

Most experts consider Python as a fundamental language to become a data scientist. As a result, this open-source and free programming language enjoys considerable popularity.

Key features of Python

Python offers the following features:

  • It is a high-level data science programming language.
  • Python strongly supports object-oriented programming. It uses the fundamental concepts of object-oriented programming like classes, object encapsulation, etc.
  • Python is an interpreted language, and its execution happens line by line. You do not need to compile Python. The source code of Python is converted into an intermediate form named "bytecode."
  • Python offers portability. You can run the same Python code on all key operating systems like Windows, Mac, Linux, and Unix.
  • Python is a dynamically typed programming language. You do not need to specify the type of a variable in advance since it is decided at runtime.
  • Python offers extensibility and integration with other popular programming languages like C and C++.
  • You can use a wide range of Python libraries. TensorFlow, Scikit-Learn, and NumPy are a few prominent examples. AI (Artificial Intelligence) and ML (Machine Learning) programmers use Python widely, and these libraries help them noticeably.
  • Python offers a wide range of packages too. E.g., Python packages like PyQt and Tkinter help you create a GUI (Graphical User Interface).
  • You can use Python for multiple purposes like Artificial Intelligence /Machine Learning programming, data science programming, scripting, web development, etc. Python is a versatile programming language.

Pros and Cons of Python as a Data Science Programming Language

Python offers many advantages to data scientists. These are as follows:

  • Popularity: Python enjoys a high degree of popularity among data scientists and AI/ML developers. This popularity translates to plenty of resources to learn this language.
  • Versatility: Python works very well for data science as well as AI/ML. Its wide range of useful libraries makes it a versatile language.
  • Automation: Data scientists need to automate many of their tasks, and Python helps considerably here.
  • Data visualization: Data scientists can use many open-source tools for data visualization. It helps significantly in a data science project.
  • The ease of learning: Python does not have a steep learning curve and can be considered one of the easiest data science programming languages.
  • Community support: Python enjoys considerable community support, and this helps data scientists in troubleshooting.

Python has one disadvantage for data science projects. This interpreted language is slower than several other popular languages. Data science projects need large-scale computation, and the speed of Python can be a drawback.

Key resources to learn Python

The following are a few important resources to learn Python:

  • The website: The website contains all of the official documentation concerning Python. You can find many tutorials, guides, videos, and podcasts. You can access the Python developer community too.
  • The website: The website offers extensive learning resources. Many of them are interactive tutorials, and they focus on practical learning. This website offers tutorials for the advanced level too.
  • The website: The website offers extensive Python tutorials covering all aspects of the language. Beginners find this website very useful.

2. SQL

Data scientists need to know SQL (Structured Query Language) very well. SQL is not a procedural programming language, and it is used for querying RDBMSs (Relational Database Management Systems).

Key features of SQL

The key features of SQL are as follows:

  • SQL is a query language for relational databases, and it is not a procedural language. E.g., you cannot code operations like loops in SQL.
  • It offers DDL (Data Definition Language) commands, e.g., to create tables.
  • SQL provides DML (Data Manipulation Language) commands. These are for inserting, updating, and deleting rows in tables.
  • This query language offers DQL (Data Query Language) commands to retrieve data.
  • You can use the DCL (Data Control Language) commands in SQL to grant or revoke rights/permissions.
  • SQL offers TCL (Transaction Control Language) commands to manage the status of transactions in a SQL database.
  • SQL is a statically typed language.
  • SQL produces outputs in the form of tables with rows and columns.
  • You can embed SQL commands in procedural languages like C, COBOL, Java, etc.
  • It offers features like stored procedures and triggers.
  • Advanced SQL features include recursive queries. The advanced features of SQL allow you to use it for decision support systems and data mining.

Pros and Cons of SQL for Data Science Projects

The most significant advantage of SQL for a data science project lies in the fact that you use it along with an RDBMS, and you have plenty of choices there. These include both traditional RDBMSs and open-source SQL databases.

Among traditional RDBMS solutions, you can use Oracle or Microsoft SQL Server. Both are enterprise-grade solutions, offer performance and scalability. In addition, you can get comprehensive technical support for both.

MySQL and PostgreSQL are leading open-source RDBMSs. Both offer advanced features. MySQL and PostgreSQL offer performance and scalability, and you get robust community support.

The other advantages of SQL are as follows:

  • Popularity: RDBMSs like MySQL and PostgreSQL enjoy a high degree of popularity. You will easily find developers that have used either of them. That naturally implies that these developers know SQL.
  • Ease of use: SQL is a query language, and it is not a procedural language. You do not need to learn the complex logical operations of a procedural programming language, making SQL easy to use and understand.
  • Sound design: As we stated, you need to use SQL along with an RDBMS. An RDBMS needs to follow specific basic universal rules. E.g., it needs to have a table with predefined columns. Furthermore, the data types of columns need to satisfy basic rules. A foreign key in one table must be a primary key in another table to take another example. These design principles in RDBMS offer clarity. This foundational strength helps in data modeling.

The main disadvantage of SQL is the steep learning curve concerning in-depth RDBMS concepts. E.g., you might find it hard to learn the concept of normalization and its different degrees. You might end up coding sub-optimal SQL queries without in-depth knowledge. It can result in performance issues, which is a bottleneck in a data science project.

Key resources to learn SQL

The following are a few essential resources to learn SQL:

  • The website: The website is a wiki-based website providing interactive tutorials for SQL. Beginners find these self-explanatory tutorials helpful.
  • The SQL tutorials on the website: The website provides SQL tutorials. It presents these interactive tutorials as a series in its course. Learners benefit from its broad scope and helpful user interface.
  • The website: The website offers an easy-to-use interface. It provides simple instructions and interactive exercises, which help learners.

3. R

R is one of the important data science programming languages. This free and open-source project provides a programming language as well as a software environment for statistical computing.

Key Features of R Programming Language

R offers the following features:

  • R is an object-oriented language.
  • This data science programming language can work on all popular operating systems like Windows, Mac OS X, and Linux.
  • R is an interpreted language.
  • You can use R with programming languages like C, C++, or FORTRAN for computation-intensive tasks. You can use it with languages like Java, .Net, and Python to manipulate objects.
  • This programming language provides operators for calculation on arrays. It offers operators for lists, vectors, and matrices too.
  • It offers capabilities that are useful for statistical analysis and data manipulation. A few examples are conditional statements, loops, user-defined recursive functions, and input-output facilities.
  • R provides robust data handling and storage capabilities.
  • R can handle both structured and unstructured data.
  • R utilizes vectors and vector arithmetic to process large data sets efficiently.
  • Data science projects often involve a process called "data wrangling." This process cleans complex and inconsistent data sets, which enables computation and analysis. Data wrangling takes time. R offers tools that make data wrangling easier.
  • R has an extensive collection of data analysis tools.
  • R offers capabilities of graphical analysis of data. It can help you to create static graphics. R includes vast libraries that provide interactive graphic capabilities.
  • This programming language has a wide range of packages. You can access them in CRAN (Comprehensive R Archive Network). These packages serve many purposes, e.g., interactive graphics, quantitative analysis, and ML development.
  • R makes reporting more straightforward. E.g., you can use the "R Markdown" feature to combine plain text with code in a report.
  • The R package named "R Shiny" helps to create web apps.
  • Packages like "ddR" and "multiDplyr" help one to use R for distributed computing.
  • R Packages like Roracle, Open Database Connectivity Protocol, and RmySQL enable R to interact with databases.

Pros and Cons of R for Data Science Projects

In addition to being a free and open-source language, R offers the following advantages:

  • Community support: Data scientists can get robust community support for R.
  • Versatility: As we have explained, several powerful features of R make it suitable for data analysis, statistical computing, graphical analysis, ML, and reporting. This makes R very ideal for data science projects.
  • Extensibility: The highly extensible nature of R makes it an ideal choice for data scientists. They can accomplish a lot with R.
  • Easy to learn: You do not need to contend with a steep learning curve when using R.

R has a notable disadvantage, which is its comparative lack of robust security features.

Key resources to learn R

The following are a few important resources to learn R:


MATLAB, which stands for "matrix laboratory," is a proprietary programming language from MathWorks. It also works as a numeric computing environment. This makes it an important tool for data scientists. Note that MATLAB is not free or open-source.

Key Features of MATLAB

  • This high-level programming language supports large-scale numerical computational workloads.
  • MATLAB is an interpreted language.
  • MATLAB offers an interactive environment.
  • MATLAB provides tools for building custom graphical user interfaces (GUIs).
  • This data science programming language has an extensive library of mathematical functions for linear algebra, statistics, Fourier analysis, etc.
  • It provides capabilities for data visualization. This language has built-in graphics capabilities. It provides tools for creating custom graphs and charts.
  • MATLAB offers integration features. Therefore, you can integrate MATLAB-based algorithms with languages like C, .NET, and Java.

Pros and Cons of MATLAB as a Data Science Programming Language

MATLAB offers the following advantages:

  • Mathematics-focused: Its powerful collection of mathematical functions makes it ideal for use in educational institutions.
  • A complete platform: MATLAB offers a complete environment/platform for numerical computational tasks.
  • Fitment in technical data analysis: The extensive collection of predefined functions in MATLAB makes it suitable for scientific and technical data analysis purposes.
  • Ease of use: MATLAB offers a tool to create a GUI for a data analysis program.

MATLAB has a few disadvantages. These are as follows:

  • This is not a free or open-source tool. Depending on your project, you might find it expensive. Plan your budget accordingly.
  • This interpreted language is slower than compiled languages. That can be a disadvantage in data science projects with large-scale computational workloads.

Key resources to learn MATLAB

The following are a few essential resources to learn MATLAB:

  • The edX MATLAB courses: The edX MATLAB courses cover all aspects of MATLAB, including advanced mathematical functions.
  • The MathWorks MATLAB courses: The MathWorks MATLAB courses cover MATLAB fundamentals, programming techniques, data processing, visualization, etc. They also cover ML and DL (Deep Learning) with MATLAB, useful for data science projects.
  • The MIT open courses on MATLAB: Beginners can access the MIT open courseware video tutorials on MATLAB.

5. SAS

SAS ("Statistical Analysis Software," previously "Statistical Analysis System") is a proprietary statistical software suite from SAS Institute. As a data science programming language it can be used for data management, advanced data analytics, predictive analytics, and business intelligence. Unfortunately, SAS is not free or open-source.

Key Features of SAS

SAS offers the following features:

  • It is a 4th generation programming language (4GL).
  • It is an interactive language.
  • SAS is an easy to learn programming language.
  • SAS offers in-built libraries that reduce the coding effort on the part of developers.
  • This language supports different data formats.
  • You can use SAS with SQL and major procedural programming languages.
  • It offers solid statistical and analytical capabilities.
  • You can use SAS Studio, an easy-to-use interface for performing data analysis. It works with most web browsers.
  • SAS provides "SAS Management," which monitors and manages the analytics environment.
  • You can use its reporting capabilities.
  • SAS offers security features, including data encryption algorithms.

Pros and Cons of SAS as a Data Science Programming Language

SAS offers the following advantages to data scientists:

  • Statistical capabilities: SAS is suitable for statistical analysis tasks.
  • Analytical capabilities: SAS's predictive and advanced data analytics capabilities make life easier for data scientists.
  • Popularity: SAS is a well-known product. Many data science companies already use it for many years. This popularity works well for it in data science projects.
  • Ease of learning: SAS does not involve a steep learning curve since its syntax is easy. Aspiring data scientists can learn it quickly.

SAS has the following disadvantages:

  • SAS is not free. You need to pay for its license. Depending on your project requirements, you might find SAS expensive.
  • SAS does not have the kind of graphics capabilities that the market-leading analytics software solutions have.

Key resources to learn SAS

The following are a few essential resources to learn SAS:

  • Video tutorials from the SAS Institute: The SAS Institute provides an extensive collection of video tutorials covering all aspects of SAS.
  • The SAS tutorial from "Listen Data": "Listen Data" provides a comprehensive SAS tutorial suitable for beginners and advanced learners.
  • The SAS learning resources from "Analytics Vidhya": "Analytics Vidhya" offers a comprehensive set of SAS courses.


A data scientist needs several skills. In this article, we reviewed the top data science programming languages that a data scientist should learn. First, we covered both open-source and proprietary languages. Next, we examined their features, advantages, and disadvantages. Finally, we reviewed a few learning resources for these languages.

Top 5 Data Science Programming Languages

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