10 Best Data Science Programming Languages to Know In 2023/data-science-insights/10-best-data-science-programming-languages-to-know-in-2023

10 Best Data Science Programming Languages to Know In 2023

10 Best Data Science Programming Languages to Know In 2023

If you think about beginning your data science career, coding is the crucial step every aspiring data scientist should consider learning. To select the best programming language, you should understand the usage, complexity, and scope of each language. 

With programming languages, data scientists become able to send instructions and interact using computers. Let it be developing a mobile application, upgrading data science skills, or learning a programming certification, everything demands you to pick up the right programming language. 

In this article, let’s discuss the best programming languages you can go for in 2023 and their unique characteristics.

Why Should Data Science Professionals Choose Programming languages?

Data science education helps you to gain a lucrative career option with numerous job opportunities in the field. The growing demand for data science professionals has made this the perfect time to begin your journey to landing a data scientist job. If you learn any major programming languages that organizations demand, you can get a higher chance of being chosen by the top organizations.

What are the top coding languages you require for data science? Let’s take a deep dive into these much-needed programming languages for a successful data science career.

10 Major Data Science Tools for a Stunning Data Science career

   1. Python

Python is ranked as #1 in many popularity indices like TIOBE Index, with its recognition increasing in recent years. This is a general-purpose open-source programming language with a wide range of applications in data science, web development, and gaming industries. 

Python has rich libraries and powerful packages to execute data visualization, data preprocessing, statistical analysis, machine learning and deep learning model deployment, etc. It is one of the easiest coding languages beginners can learn and execute. 

Here are the major libraries used for Machine learning and data science applications: 

  • NumPy: Used for advanced mathematical functions and arrays.
  • Pandas: Used to perform database manipulation called DataFrames.
  • Matplotlib: Python library to implement data visualization.
  • Scikit-learn: Ideal to develop machine learning algorithms.
  • TensorFlow: Framework from Google, used to develop deep learning and machine learning algorithms.
  • Keras: The open-source library that trains neural networks.

   2. JavaScript

One of the most preferred languages, based on the Stack Overflow developer survey of 2021, JavaScript is widely used to build interactive web pages. Though many Javascript users engage in the web development sector, it has recently got attention in the data science segment.

JS supports the major libraries for deep learning and machine learning, like Keras and Tensorflow, and data visualization tools such as D3. This is the effective language option for front-end and back-end programming experts with prominence libraries that incorporate machine learning capabilities.

   3. SQL

The world has its data majorly stored in SQL databases. SQL or Structured Query Language is highly domain-specific and lets programmers interact, edit and extract information from the existing database. If you aspire to be a data scientist, you should have hands-on experience in SQL databases.

All huge tech companies like Uber, Airbnb, and Netflix use SQL to build their high-performance databases and perform data analyses. This has a simple yet declarative syntax making it easier to learn than other languages.

   4. R

R is the coding language that ranks 12th in terms of the TIOBE index and this is highly in demand as per many other popularity indices. R is the prominent option for many data science aspirants.

This is a major added language in many popular data science certifications since it plays a crucial step towards machine learning, data processing, data manipulation, data visualization, and statistical computing. Though you can execute R directly over a command line, many use Rstudio, the strong third-party interface that incorporates processes like data viewer, data editor, and debugger.

   5. Java

Java is highly used in web and mobile applications, and also modern web technology development like IoT, Big Data, AI, and Blockchain. Java offers other capabilities like data binding and exceptional data security.

Google leverages the Java programming language to create and develop the application Google Docs. It ranks 3rd in terms of the TIOBE index and delivers high performance with seamless efficiency. This is an object-oriented and open-source language. Java provides a solid framework with Java virtual Machines for major tools like Scala, Hadoop, and Spark.

   6. Julia

An emerging language in the marketplace, and one of the youngest languages released in 2011, Julia has grabbed attention through its excellent numerical computing features. It is defined as the inherited version of Python, and highly effective technology compared to the other data analytics languages. Though Julia has not completely evolved to be the best language that supports the financial industry, it still doesn’t hold the maturity to beat the best data science languages. It grows a small community and has fewer libraries compared to R or Python.

   7. Scala

Scala is a mandatory language when it comes to data science. It is one of the top languages that support Big Data and Machine learning.  Scala was released in 2004, which is a multi-paradigm programming language and an alternative to Java. Scala supports Java Virtual Machine and powerful interoperability with Java. You can use Scala for distributed data projects. For instance, the Apache Spark cluster has its framework executed in Scala.

   8. C/C++

You might be already familiar with C and C++, the most useful languages that address intensive data science tasks. C and C++ are faster compared to other programming languages. This helps every data science professional to develop high-end machine learning and big data applications. Many core elements of well-known ML libraries like Tensorflow and PyTorch, use C++ coding. They are low-level programming languages and complex to learn, however mastering them can add more value to your data science career.

   9. SAS

SAS or Statistical Analytical System, is a software ecosystem designed to manage numerical computing and business intelligence. SAS is widely adopted in many industries, making a huge market for SAS experts. Though not as popular as R and Python, this statistical programming language benefits from reading data from spreadsheets or databases and displaying results in various documents like graphs, HTML, PDF, tables, RTF, etc. SAS languages run with the help of compilers you can deploy on the mainframe, UNIX, Windows, and Linux systems.

   10. MATLAB

MATLAB is a programming language focused on numerical computing purposes. Launched in 1984 for scientific research, it offers powerful features that manage advanced statistical and mathematical functions to aid data science operations. However, compared to the other free programming languages, MATLAB requires you to pay a huge amount of money for a license.

Final Note

Every programming language is unique in its own way. You can determine and choose the language depending on your objectives to land as a data scientist. General purpose languages such as Python, R, and Java are life-savers– learning them can definitely let you make your career in data science secure. To get hands-on experience with these programming languages, data science certifications make a great option. A reliable data science certification course provider can let you polish or build skills in programming and become a versatile data scientist for a bright career ahead.

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