7 Best Language for Data Science 2024

Data science jobs are booming in every organization and its no wonder if you’re interested in the field. The foremost prerequisite to learning data science is that you should know one of the programming languages. Then comes the question “which is the best language for data science?”

While there are more than 300 programming languages, choosing the ideal one for specific career matters. 

There are a few key things that you should take into consideration when choosing a programming language for data science. 

Before we get started with the suitable programming languages for the data science career path. Let’s see the frequently asked question about data science.

Which is the best programming language for data science?

Let’s explore the 7 programming languages that are used in Data Science.

Note: You don’t need to know all the programming languages on the list to become a data scientist. Learning one programming language should be enough, to begin with.

But, you can start learning the second programming language to enhance your skillset and career.

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1. Python Programming Langauge

A most data scientist uses Python. In a recent worldwide survey, it was found that 83% of 24000 data professionals used Python.

Python is an excellent language for data science since it follows the ETL process (extraction-transformation-loading). This makes Python the ideal candidate.

Programmers and data science enthusiasts prefer to learn Python because it is a dynamic and general-purpose language. 

Mainly because Python is easier to learn and performs faster than R when it comes to iterations less than 1000. For data manipulation operations, Python is better than R.

Python programming language also includes good packages for machine learning, AI, and natural language processing. Some of the best Python training in Coursera are listed here and the top Python courses in edX.

Let’s look at the pros and cons of the Python language.

Pros:

• One of the easiest programming language and a brilliant choice for beginners.
• Python is a dynamic and general-purpose language. 
• It has many inbuilt and third-party libraries for most of the tasks. 
• Python API is provided by many online services.
• Some of the popular packages scikit-learn, pandas, and Tensorflow is used for advanced machine learning applications.

Cons:

• R’s excellent statistical and data analysis packages dwarf the Python language.
• Python is a dynamically typed language. Which means you must show due care when typing. You can expect a Type error from time to time.

Conclusion: A great all-rounder programming language.

2. R Programming Language

R is an open-source programming language. It is an excellent choice for statistical computing and data visualization. We already know that statistics is the basic component of becoming an excellent data scientist. 

With R’s excellent support for statistics, it is quite easy to judge that R is an excellent choice for data science. But, one should also beware that is it is not an easy language to learn. 

The recent growth of R is a testament to how effective it is in data science.

Python is easier to learn when compared to R. Let’s look at the pros and cons of the R language.

Pros:

• R program has an excellent range of domain-specific packages. You can find a package to do anything. 
• It has packages that support the most quantitative and statistical applications.
• The base installation of R comes with in-built statistic functions and methods. Even the matrix algebra is handled well.
• The core strength of R is a data visualization that uses the ggplot2 library.

Cons:

• Slower when compared to Python. The speed of the R language is slow.
• Since it is excellent for statistics and data science but inferior for general-purpose programming.

Conclusion: Brilliant for what it is designed to perform. 

Python and R are 2 of the 7 best languages for data science. See the rest of the 5 programming languages suitable for data science. 

3. SQL – Structured Query Language

SQL is a Structured Query Language that is domain-specific. The relational database management system uses SQL to manage data. 

The data in SQL is stored in the form of tables. Every data scientist should be comfortable in handling mission-critical SQL tables and SQL queries.

You don’t have to know the complete SQL, a basic understanding of how to work with data in DBMS should be enough.

It is convenient as a data processing language than an advanced analytical tool. Since data science depends on the ETL process, SQL is very useful for data scientists.

Let’s see the pros and cons of SQL.

Pros: 

• SQL is efficient in querying, updating, and manipulating data in the database management system.
• Since SQL follows the declarative syntax one can read it with ease.
• It is used in a range of applications to handle the data efficiently.
• Using the SQLAlchemy module, one can integrate SQL with other languages.
• Experienced programmers find it effortless to learn SQL.

Cons:

• SQL’s analytical capability is limited. Your options become limited beyond counting, aggregating, and averaging data.
• There is various implementation of SQL such as MariaDB, SQLite, and PostgreSQL. They are different which makes the inter-operability difficult.

Conclusion: It is an efficient and timeless language.

4. Java Programming Language

Java is object-oriented, general-purpose language. It has become one of the most versatile programming languages.

They are used in web applications, desktop applications, mobile apps, and embedding electronics.  

Data scientists may not need to know Java, but the frameworks such as Hadppm run on JVM. Hadoop framework is used to manage data processing and storage for big data applications.

Also, Java has numerous libraries and frameworks for data science and machine learning. And is easy and fast to scale for larger applications.

There are multiple advantages to learning Java for data science. Also, Java’s type safety and performance are undeniable advantages.

Since Java lacks statistics-specific packages, it would be better to consider if you already know R or Python.

Let’s look at the pros and cons of using Java.

Pros:

• They are everywhere. Many software and applications already built or run upon Java back-end. Hence integrating data science methods into existing codebase would be easier.
• Java is a type safety programming language.
• It is a general-purpose, high-performance, and compiled language. Which is suitable to write efficient ETL production code.

Cons:

• Java is not the first choice when it comes to dedicated statistical applications and ad-hoc analyses. Python and R shines in this aspect and offers greater productivity
• Compared to R, Java doesn’t have a good number of libraries to support statistical methods.

Conclusion: A great contender for data science.

5. Scala Programming Language

Scala is an open-source modern multi-paradigm programming language.

It stands for “Scalable Language” and is known for handling big data very well. Scala language is designed to express common programming standards in a brief, elegant, and type-safe way.

If you already know the Java program’s syntax, learning Scala will be effortless. Additionally, if you know Python, C or C++ then learning Scala will be smooth.

One can perform parallel processing on a large scale by combining Scala and Apache Spark. Scala and Spark (combined) are fantastic solutions when using cluster computing to work with Big Data.

 When it comes to using cluster computing to work with Big Data, then Scala + Spark are fantastic solutions. 

Let’s take a look at the pros and cons of the Scala programming language.

Pros:

• Scala is an ideal choice for working with high volume data sets
• Combining Scala with Spark will result in high-performance cluster computing.
• Scala supports both functional and object-oriented programming.
• Scala is interoperability with the Java language. This makes Scala the powerful general-purpose language.

Cons:

• Scala’s syntax and type system are described as complex. Hence, like C/C++ it requires a steep learning curve.
• It is not recommended if you’re a beginner in a programming language. 

Conclusion: Scala is suitable for Big data.

6. Julia Programming Langauge

Julia is a high-level programming language. It is designed for high-performance numerical analysis and computational science. 

Both front and back-end web programming can be built using Julia.

Using Julia’s API, It can be embedded in programs making it support for metaprogramming. Metaprogramming is a technique where computer programs can treat other programs as their data. 

Julia was designed to implement linear algebra and matrices better.

Thus making it faster for Python. 

Since Julia is a new programming language, it cannot compete with the likes of Python and R.

Let’s look at the pros and cons of the Julia programming language.

Pros:

• Julia was designed for numerical analysis, but it is also capable of general-purpose programming.
• It is easy to read, thus it makes one of the best readability programming languages.
• Julia is a just-in-time compiled language. It offers dynamic typing, simplicity, and scripting capabilities like Python.

Cons:

• As Julia is a recently developed programming language, users report instability when using packages. But the core language is stable and has no issues.
• Julia as limited packages. It will take a few years for Julia to grow and become established languages such as Python and R.

Conclusion: Ignore and leave it for the future.

7. Swift Programming Language

Swift is an open-source and easy-to-learn programming language.

It is mainly used to develop applications for Apple devices. Swift is one of the easiest programming languages for beginners. Because it uses simple syntax and is super fast to run the apps.

Swift recently started getting traction among the data science community.

It has several libraries for performing tasks like digital signal processing, machine learning, matrix math, numerical computation, etc.

Let’s take a look at Swift’s pros and cons.

Pros:

• Swift is the scalable programming language. One can add many new features to it. Apple focused and relying on Swift than C.
• Like Python, it has a simple syntax that is close to natural English.
• Compared to C, speed and performance are 40% more in Swift.

Cons:

• Every new version of Apple os causes Swift to be unstable.

Conclusion: Swift is slowly gaining popularity when it comes to Data Science.

Resources to learn the best programming language for Data Science

I’ve mentioned some of the best resources to learn the programming language and data science below.

Coursera and Edureka both offer a Data Science course along with the programming language. Feel free to explore them before subscribing to the learning.

On the other hand, DataCamp also offers a range of courses for a monthly/annual membership. Feel free to check them out as well.

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Summary

This brings us to our conclusion where we reveal the best language for data science. Python is a clear winner and the best language to learn data science. 

Python is powerful by integrating it with SQL and TensorFlow. It has over 70,000 libraries and offers endless possibilities when working with Python.

Using Python, users can also create a CSV file to output the data. Data in a spreadsheet is easy to read for humans.

I recommend to aspiring data scientists is to learn Python and then continue to learn SQL. The combined power of SQL and Python gives you a better chance to highlight your skill in your resume. 

If you’re still stuck on deciding, I hope this guide on why to learn python will be helpful.

If you loved this guide, please share it with your friends and colleagues. It might be helpful for someone who is aspiring to become a data scientist.