Top Data Science Programming Languages

In today’s ever-growing competitive technology market, numerous data is being generated each day. Presently, 5 billion consumers are interacting with data daily. According to IDC, this is expected to reach 6 billion by 2025, i.e., nearly 76 percent of the..Read More

Top Programming Languages for Data Science
  • Updated on 10th Sep, 20
  • 107 Views

Data Science is no doubt a dynamic field in the present market, and it is vast in its area of expertise. Every month, new Data Science projects are flooding the market. Hence, one needs to upgrade towards these advancements. Data Scientists must upskill themselves to match up to the changing industry standards. To stay ahead of the peers, one must add up Data Science programming language within their profile. It is important to learn at least one programming language to excel in the field of Data Science.

In this blog, we shall concentrate on:

 

Top Programming Languages for Data Science

To ease your search, we have compiled a list of top programming languages for Data Science that are sure to improve your career. The order in which we have listed the languages is according to each Data Science language’s popularity among Data Scientists.

The video below will help you get a clear understanding of the topic:

 

1. Python

Python is one of the popular Data Science languages. It is considered as the best programming language for Data Science as it is open-source, used for general purpose, and is object-oriented. This flexible language offers multiple libraries, which makes it easier for programmers to conduct data manipulations, data analysis, and processing. Moreover, the Python community forum is huge. Any Data Scientist or developer can post queries and find relevant solutions on this platform.

Python is faster than R as it offers less than 1,000 iterations, which makes it a cakewalk for programmers.

Level of difficulty: Though this Data Science programming language is robust,it is also considered to be easy to learn and implement. Even a beginner can code an algorithm with ease, mainly due to its readable syntax.

Data Science tasks Python performs:

  • Conducts data mining
  • Carries out Machine Learning (ML) algorithms
  • Possesses designated libraries for data preservation as well as for data preprocessing
 

2. R

R is an open-source, high-level programming language built by statisticians primarily to perform statistical computing. However, this flexible language offers multiple libraries and applications for Data Science as well.

Just in a short period, R has outpaced its counterparts as it can perform numerous functions in Data Science applications. R differs from other Data Science languages due to its unique features. Nearly 70 percent of data miners use R. It is robust with specialized packages and displays data visualizations in the form of plots, graphics, and charts. This seems ideal for papers and research reports.

Level of difficulty: As compared to Python, R includes more than 1,000 iterations; hence, it is complex to learn. However, with the foundation in Machine Learning algorithms, one can pick up R very easily. To get started, it needs limited experience with programming.

Data Science tasks R performs:

  • Provides data visualization
  • Conducts Data Analytics
  • Executes statistical problems through a dataset
  • Easily connects to databases using RStudio
  • Analyzes huge data arrays
 

3. Scala

Scala was initially built for JVM (Java Virtual Machine) and hence is an extension of Java. This programming language for Data Science addresses most of the issues that Java possesses. Applications of Scala range from web programming to Machine Learning. It is scalable and effective enough to handle big data. Many high-performance Data Science frameworks are designed to be used specifically in Scala.

Scala, in combination with Apache Spark, makes an irreplaceable tool that can deal with big data efficiently. This is the programming language much required for Data Science.

Level of difficulty: Scala is relatively easy to learn because of its OOP functionality.

Data Science tasks Scala performs:

  • Eases the performance on high datasets
  • Ideal for dealing with large volumes of data
  • Can sculpt data in any form
  • Able to learn parallel processes while working with data arrays
  • Can perform single operations in varied modes
 

4. Julia

Julia is a specific Data Science programming language that was purposefully developed for performing numerical analysis and computational science. This exceptional language is quick in dealing with mathematical concepts, such as matrices and linear algebra.

Julia is rapidly gaining momentum in recent times. This technology goes well with both simple general-purpose programming and complex numerical analysis. It is the fastest scripting language as compared to others on the list as it is efficient in performing web programming at both frontend and backend.

Level of difficulty: Though it is a recently introduced one, the ease of learning Julia is similar to that of Python.

Data Science tasks Julia performs:

  • Conducts risk analysis for financial organizations
  • Solves mathematical problems at high speed
  • Considered effective for performing Data Analytics
  • Works with data faster than R and Python
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5. Java

Java finds its versatile usage in web and desktop applications. Hadoop, the processing framework that runs on JVM, manages data processing and applications. Due to this feature, Java is considered to be the prime programming language for Data Science activities.

Java works fast and is scalable even for larger applications. It is known for its extraordinary tools and libraries, which are offered for Data Science. Java is preferred by enterprises, over its peers, mainly due to its scalability. Once a project is launched, Java can scale it without much compromise.

Level of difficulty: It is relatively easy for a beginner to learn Java through Intellipaat as it is a readable language.

Data Science tasks Java performs:

  • Constructs large-scale Machine Learning applications
  • A wise option for IoT and Big Data
  • Secure enough in working with sensitive data
  • Best choice for Machine Learning algorithms
 

6. SQL

SQL, short for Structured Query Language, is one of the popular domain-specific programming languages for data management. SQL is somewhat similar to Hadoop at managing data. Though it is not used primarily for Data Science operations, it can still come handy while working on database management systems.

This programming language is considered as one of the primary requirements for Data Scientists. Additionally, in Data Science, SQL comes into the picture for data retrieval and the extraction of data from databases.

Level of difficulty: SQL queries and tables are comparatively arduous to learn for Data Scientists. Nonetheless, it acts as a crucial module for managing databases.

Data Science tasks SQL performs:

  • Updates and queries information stored in databases
  • Manages large databases
  • Compliant toward Data Science workflow
  • Retrieves enormous data from relational databases
  • Extracts and wrangles data from databases
  • Manages data for both online and offline applications
 

7. MATLAB

MATLAB is considered as the best option by Data Scientists for performing intense mathematical operations. As Data Science deals majorly with mathematics, MATLAB is proven to be a handy tool in executing mathematical modeling, data analysis, and image processing.

However, with the invention of Python and R, MATLAB has been experiencing a slight decline in its usage.

Level of difficulty: MATLAB programs are comparatively simple in their simulation scripts.

Data Science tasks MATLAB performs:

  • Best for performing profound mathematical operations
  • Considered as a data analysis programming language as it executes mathematical modeling that enhances the language’s priority
  • Highly specialized in working with big data
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8. JavaScript

JavaScript is a versatile object-oriented programming language. It is adaptable toward handling multiple tasks at a time. The language is a master in data visualization. JavaScript holds numerous libraries that provide a solution for each kind of problem that a programmer may encounter.

Level of difficulty: JavaScript iseasy to use. Even aspiring Data Scientists can have access to the models in a web browser.

Data Science tasks JavaScript performs:

  • Sets up data visualizations perfectly
  • Can solve the problems of big data due to its enormous availability of native libraries
  • Good fit for the projects based on web and Big Data technologies
 

9. SAS

SAS is short for Statistical Analysis System. It is considered to be the must-have language for those who want to enter the analytics industry. This tool is highly reliable in handling complex statistical operations and is considered to be stable for conducting analytical operations.

Before digging deep into this tool, it is important to note that SAS is not advisable for beginners as it is made to address advanced business issues. Therefore, it is the most popular Data Science language among enterprises.

Level of difficulty: It is commonly used in disciplines like BI (Business Intelligence). Its user-friendly GUI makes it an easy language to learn for enthusiasts.

Data Science tasks SAS performs:

  • Manipulates and manages data
  • Administers data analysis through statistical models
  • Used for accessing data that comes in multiple formats
 

10. C++

Although C++ is a low-level Data Science programming language, it acts as a foundation for executing the actual high-level programming languages. It is extremely simple yet powerful. It is a must-have in every Data Scientist’s toolkit as it enables a broader command over Data Science applications.

Level of difficulty: It is a bit hard language to learn for beginners. It is difficult due to its multi-paradigm nature.

Data Science tasks C++ performs:

  • Used in Big Data in combination with Java
  • Acts simple, yet powerful, in the Data Science space
  • Comes to rescue while computing large datasets
Data Science Tasks Performed by Different Programming Languages
 

Which among the above is the best programming language for Data Science?

Among all the data analysis programming languages, Python is here to stay for at least the next 5 years and can be used for every problem that a Data Scientist may come across. Just having Python in the toolset can help you work on various use cases. 70 percent of Data Scientists can be constantly seen using Python within their instances. Often, both Python and R go hand in hand to possibly implement unique projects. However, soon, Julia can be foreseen as a tough competitor for both Python and R.

 

Learn about the difference between Kotlin and Flutter in our comparison blog on Kotlin vs Flutter.

Final Thoughts

To grab opportunities better as a Data Scientist, the knowledge of programming languages is a must-have. The above-described languages are the Data Science languages to learn since they are frequently used in Data Science either individually or in combination.

Knowledge of the application of these Data Science languages amplifies the profile of a Data Scientist. Hence, it is essential to master at least two languages to solve the issues you may face in your career as a Data Scientist. It is preferable to weigh the pros and cons of each language and experiment with it according to the requirements before making a wise decision.

There is no denying the fact that Python is expected to remain as the top choice for Data Scientists as it is reckoned to be the best language for Data Science. However, the other mentioned languages have their specialties in executing various use cases of the field as well. Hence, every programming language we have discussed in this blog can act like a pro in its area of expertise.

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