10 Best programming languages for Data Science to learn in 2023

with expanding volume and complexity of big business data, and its focal job in direction and vital preparation, are driving associations to put resources into individuals, cycles and advances they need to figure out and gain experiences from their data assets.

According to Market research Business IDC estimated that more than 64 zettabytes of data — the equivalent of 64 billion TB — was created, captured, copied and consumed during 2020


Python is open-source, interpreted, high-level language and provides a great approach for object-oriented programming. It is one of the best languages used by a data scientist for various data science projects/applications. Python provides great functionality to deal with mathematics, statistics, and scientific function.

  1. Python is a relatively simple and easy to learn programming language. Python’s main advantage is that it is simple and straightforward, which makes it appealing to anyone.
  2. Libraries and tools One of the primary responsibilities of Data scientists is to analyse data, and data can take many forms in the real world.
  3. Notebook Jupyter. Another reason Data Scientists like Python is the Jupyter Notebook, which allows you to code and collaborate with other Data Scientists via a web browser.
    Help from the
  4. Community. Another reason for Python’s popularity among people learning Data Science in the community that I discovered.
  5. Pandas
    If you want to work with Python, you must be familiar with Pandas. It includes a high-performance data structure known as gritty of Pandas


Java is probably the best language to learn for big data for a number of reasons; MapReduce, HDFS, Storm, Kafka, Spark, Apache Beam, and Scala (are all part of the JVM (Java Virtual Machine) ecosystem. Java is by far the most tested and proven language.

Although Java may appear to be unrelated to data science, there are many frameworks, including Hadoop, that run on JVM and are an essential component of the data stack. Hadoop is a software method for processing and storing large amounts of data in distributed structures. Because of its increased processing power, it can process large amounts of data and handle virtually infinite tasks at once.


JavaScript just doesn’t have the range of data science packages and inbuilt functionality compared to languages such as R and Python. … js is not suited for computationally intensive, CPU-bound tasks. For such tasks, languages such as Python, Java or Scala have the upper hand over JS.

JavaScript is undeniably better than Python for website development for one simple reason: JS runs in the browser while Python is a backend server-side language. While Python can be used in part to create a website, it can’t be used alone. … JavaScript is the better choice for desktop and mobile websites.


SQL (or Structured Query Language) is a powerful programming language that is used for communicating with and extracting various data types from databases. A working knowledge of databases and SQL is necessary to advance as a data scientist or a machine learning specialist.

1. Simple to Understand and Apply

2. Getting to Know Your Dataset

3. Compatibility with Scripting Languages

4. Handle massive amounts of data

5. A Pathway to Jobs in Data Science


Learning C/C++ offers excellent capabilities for building statistical and data tools. These will translate well to Python and scale well for performance-based applications.

C/C++ is also surprisingly useful because it compiles data quickly. It builds highly functional tools and allows for serious fine-tuning. It can be complicated to pick up if you’ve never studied programming languages before.

When to use C/C++ in data science: Web developers with experience in low-level languages could use C/C++ for scalable projects.


Researchers are working hard to develop application-specific tools, programming languages, and frameworks. Scala is one of those inventions to leverage big data processing. Scala programming language is built to implement scale able solutions to crunch big data in order to produce actionable insights.


Julia has many features and resources advantageous to machine-learning and data science. This language was designed with a focus on numerical and scientific computation. Julia’s math-friendly syntax makes it ideal for users of Matlab, Octave, Mathematica, R, among other computing languages and environments.


The good news is R is developed by academics and scientist. It is designed to answer statistical problems, machine learning, and data science. R is the right tool for data science because of its powerful communication libraries.

Some of R’s key features for data science applications include:


  • R provides extensive statistical modelling support.
  • R is a good tool for a variety of data science applications because it has attractive visualisation tools.
  • R is commonly used in data science applications for ETL (Extract, Transform, Load). It serves as a gateway for many.
  • R also includes a number of useful data-wrangling packages.
  • Data scientists can use R to apply machine learning algorithms to predict future events.


Engineers and scientists use MATLAB® to organize, clean, and analyze complex data sets from diverse fields such as climatology, predictive maintenance, medical research, and finance. MATLAB provides: Datatypes and preprocessing capabilities designed for engineering and scientific data.


SAS is a tool for analyzing statistical data. SAS is an acronym for statistical analytics software. The main purpose of SAS is to retrieve, report and analyze statistical data. Each statement in SAS environment ends with a semicolon otherwise the statement will give an error message

In addition, larger industries rely on SAS for data security and stability. SAS is a critical tool for data scientists for these reasons. With the growth of the data science community, there is a shift away from SAS and toward more open-source tools.

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