Data science is a vast domain that includes a number of tasks, from collecting data to transforming them into actionable insights. And at the core of these tasks lies the fundamental data science skill – the programming languages.

Currently, the demand for skilled data science professionals is soaring. Organizations are looking to maximize the power of data science and extract meaningful insights out of huge amounts of data they have gathered over time to help them with data-driven decision-making. By 2026, around 11.5 million data science jobs will be available in the US. Do you have the right data science skills and expertise to grab these opportunities?

Well, your knowledge and experience of using a variety of programming languages for data science will help you distinguish from the crowd and secure your position in highly paid data science jobs.

So, here we bring the list of top programming languages for data science mastering which will help you succeed in your data science career. Let’s start.

1. Python

Python is known to be the most popular programming language in the world of data science with 66% of data scientists using it in their job. It has a huge community of practitioners and beginners helping and supporting Python users with all their problems. Moreover, its readability, simplicity, and extensive libraries further make it a popular choice among all kinds of data science professionals and developers.

Top features:

• Readability – Has clean syntax offering better clarity and is also easy to maintain.
• Versatility – It can perform all sorts of tasks, from manipulation, and analysis, to data visualization.
• Libraries – Has NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, etc.

Best for:

• Data cleaning and processing
• Exploratory data analysis (EDA)
• Data Visualization
• Machine learning and deep learning, etc.

2.  R

R is widely known for its statistical power. To be clear, it has been designed specifically to handle large statistical computations and graphics. Users can also enjoy the flexibility and depth needed for statistical analysis with R.

Strengths and features

• Statistical power with powerful packages and libraries
• Can create high-quality customizable plots
• It is open source, freely available, and has a strong community as in Python

Best suited for:

• Statistical modeling and inference
• Data mining and machine learning
• Biostatistics and bioinformatics
• Time series analysis

3. SQL

It is not a general-purpose programming language like Python or R, but SQL, or Structured Query Language is an indispensable language for data scientists working with relational databases. It can easily query, manipulate, and extract data for all kinds of data-driven projects. Therefore, it is one of the go-to data science programming languages.

Strengths and features:

• It can query and retrieve data from large datasets at very high speed and accuracy.
• Helps with manipulation, modification, and deletion of data within databases.
• It also ensures that data is consistent and reliable.

Best suited for:

• Data extraction and loading (ETL)
• Data warehousing and business intelligence
• Database administration and optimization

4. Julia

Though it is a newer programming language for data science, it is getting quite popular in the data science industry because of its exceptional performance and ease of use. Many data science certifications now focus on Julia's fundamentals because of its future potential.

Strengths and features:

• Performs faster than Python and R for numerical computations and other tasks
• Its syntax is similar to Python and thus can be easily used by Python users as well.
• It has excellent support ability to do tasks requiring linear algebra, differential equations, and optimization.
• Machine learning libraries make performing various kinds of machine learning tasks easier.

Best suited for:

• High-performance computing
• Scientific computing and simulations
• Machine learning research and development

5. Scala

Scala runs on Java Virtual Machine (JVM) and offers both functionality and object-oriented programming. It also has great support for concurrent and distributed programming. Thus, it is highly suitable for big data and machine learning applications.

Strengths and features:

• It is more specific and concise than Java
• It also supports various functional programming concepts and offers cleaner code.
• It can easily process large-scale data

Best suited for:

• Big data processing and analytics
• Machine learning
• Distributed systems

These are some of the top programming languages in data science. Apart from these, there are some other languages that are used in data science such as:

• C/C++
• JavaScript
• Swift
• Go
• MATLAB
• SAS

Mastering these languages is definitely going to help you advance in your data science career rapidly.

Conclusion

There are several programming languages in data science, each with its own unique features, strengths, and weaknesses. Mastering all of them is quite difficult. However, you can master any one specific language that you are comfortable with and can familiarize yourself with other popular languages. This will help you perform your data science job in all kinds of situations and resources. It is also recommended to upgrade your data science skill with top data science certifications to understand how to choose the right programming language for you as well as learn their full applications effectively.