The 5 Most Popular Data Science Languages to Learn in 2026

Data science has exploded in popularity over the past decade, as organizations increasingly rely on data-driven insights to make better decisions and build smarter products. At the heart of this thriving field lies the humble programming language—the tool that enables data scientists to wrangle, analyze, and model complex data sets.

As an artificial intelligence and machine learning expert, I‘ve seen firsthand how the choice of programming language can shape the trajectory of a data science project and even an entire career. In this article, we‘ll take a deep dive into the five most popular and valuable languages for data science in 2023, backed by industry statistics and insights from the front lines of AI and ML.

1. Python: The King of Data Science

Python has firmly established itself as the preeminent language for data science and machine learning. Its readable syntax, gentle learning curve, and comprehensive set of libraries have made it the tool of choice for data scientists across industries and skill levels.

According to the Stack Overflow Developer Survey 2022, Python is the most wanted language among developers, with 64.57% expressing interest in continuing to develop with it[^1]. This popularity is even more pronounced in the data science community. The 2022 Kaggle State of Data Science and Machine Learning report found that 91% of data scientists use Python on a regular basis, far outpacing the next most popular language (SQL at 47%)[^2].

So what makes Python so appealing for data science? First and foremost, its extensive ecosystem of libraries and frameworks. From data manipulation with pandas and numerical computing with NumPy, to machine learning with scikit-learn and deep learning with TensorFlow and PyTorch, Python offers a tool for every task in the data science workflow.

Python‘s versatility is another major selling point. As a general-purpose language, Python is used for everything from web development to DevOps automation. This means that data scientists can use Python not just for analysis and modeling, but also for building data pipelines, deploying models, and creating interactive dashboards and applications.

Industry Adoption:

• Google: Python is one of the official server-side languages at Google, used for everything from data analysis to machine learning[^3].
• Netflix: Python powers many of Netflix‘s data science and machine learning workflows, including its famous recommendation engine[^4].
• Spotify: Python is the primary language for data analysis and backend services at Spotify[^5].

2. R: For Statisticians and Data Visualization Pros

If Python is the jack-of-all-trades, R is the master of one: statistical computing and data visualization. Developed in the 1990s by statisticians Ross Ihaka and Robert Gentleman, R has become an essential tool for data scientists with a strong statistics background.

One of R‘s greatest strengths is its vast collection of packages for statistical modeling and inference. The Comprehensive R Archive Network (CRAN) hosts over 18,000 packages covering everything from econometrics and time series analysis to Bayesian modeling and survival analysis[^6]. For data scientists working in specialized domains like biostatistics, finance, or social science, R‘s domain-specific packages are often the best tool for the job.

R is also renowned for its powerful data visualization capabilities. The ggplot2 package, in particular, has revolutionized the creation of complex, multi-layered graphics in R. With its declarative syntax and fine-grained control over aesthetics, ggplot2 allows data scientists to create publication-quality visualizations with just a few lines of code.

While R has a steeper learning curve than Python, it remains popular among data scientists, particularly those with an academic or research background. In the Kaggle survey, 37% of respondents reported using R, making it the third most popular data science language after Python and SQL[^2].

Industry Adoption:

• Facebook: Data scientists at Facebook use R for a variety of tasks, including exploratory analysis, visualization, and prototyping[^7].
• New York Times: The New York Times data team uses R for statistical analysis and creating interactive graphics[^8].
• Airbnb: Airbnb‘s data science team uses R for statistical modeling and machine learning, particularly in pricing and marketing[^9].

3. SQL: The Data Querying Essential

SQL may not be a general-purpose programming language, but it‘s an essential skill for any data scientist working with structured data. Short for Structured Query Language, SQL is used to interact with relational databases and perform tasks like data querying, filtering, and aggregation.

In the Kaggle survey, 47% of respondents reported using SQL in their data science workflows, second only to Python[^2]. This widespread adoption is not surprising given the prevalence of relational databases in organizations of all sizes. From data warehouses to transactional databases, SQL is the lingua franca for accessing and manipulating structured data.

But SQL is not just for data retrieval. Modern SQL dialects like PostgreSQL and BigQuery support a wide range of analytical functions and even machine learning capabilities. For example, BigQuery‘s ML functionality allows data scientists to create and execute machine learning models directly in SQL, without the need for external tools or languages[^10].

As data volumes continue to grow, SQL is evolving to meet the needs of big data processing. Distributed SQL engines like Presto and Trino enable querying of petabyte-scale data lakes, while data transformation tools like dbt (data build tool) bring software engineering best practices to SQL workflows[^11].

Industry Adoption:

• Uber: Uber‘s data platform relies heavily on SQL for data querying and analysis, with Presto and Hive used to process petabytes of data[^12].
• Shopify: Shopify‘s data team uses SQL for everything from ad-hoc analysis to powering its internal experimentation platform[^13].
• Stitch Fix: SQL is the foundation of Stitch Fix‘s data platform, used for data transformation, feature engineering, and powering machine learning models[^14].

4. Java and Scala: For Big Data and ML at Scale

Java and Scala are the languages of choice for big data processing and machine learning at scale. As the primary languages of the Hadoop and Spark ecosystems, respectively, Java and Scala power some of the world‘s largest data pipelines and ML platforms.

Java, in particular, is ubiquitous in the enterprise. Its mature ecosystem, strong typing, and robust performance make it a popular choice for building production-grade data infrastructure. Many of the core big data technologies, including HDFS, MapReduce, and Kafka, are written in Java.

Scala, on the other hand, has gained traction in the data science community due to its use in the Spark framework. Spark‘s core APIs are written in Scala, making it a natural choice for data scientists and engineers working with Spark. Scala‘s functional programming features, like immutability and higher-order functions, are particularly well-suited for distributed data processing and machine learning.

As data volumes and ML models continue to grow, the need for tools that can handle big data and scale to large clusters becomes increasingly important. This is where Java and Scala shine. Their performance, type safety, and integration with the JVM ecosystem make them essential for data science teams working on large-scale, production-grade systems.

Industry Adoption:

• LinkedIn: LinkedIn‘s data infrastructure is largely built on Hadoop and Scala, with Spark used for a variety of data processing and machine learning tasks[^15].
• Twitter: Twitter‘s data platform relies heavily on Java and Scala, with Hadoop and Spark used for data processing, and Finagle for building high-performance microservices[^16].
• Netflix: Java and Scala power many of Netflix‘s big data and machine learning systems, including its Keystone data pipeline and Meson ML platform[^17].

5. Julia, Go, and Rust: Languages to Watch

While Python, R, SQL, Java, and Scala are the established players in the data science language landscape, there are several up-and-coming languages that are worth keeping an eye on.

Julia, in particular, has generated significant buzz in the data science community. Developed at MIT, Julia is designed to combine the ease of use of Python with the performance of C. Its just-in-time (JIT) compiler and type system enable it to achieve speeds comparable to C/C++, making it an attractive choice for computationally intensive tasks[^18].

Go, a language developed at Google, is also gaining traction in the data science and ML space. Its simplicity, built-in concurrency primitives, and strong typing make it well-suited for building data pipelines and microservices. The GoNum project, a set of libraries for numerical and scientific computing in Go, has made Go more attractive for data science tasks[^19].

Rust, a systems programming language developed at Mozilla, is another language to watch. Its focus on memory safety and concurrency has led to its adoption in a variety of domains, including data science and machine learning. The Rust-ML working group is actively developing tools and libraries for ML in Rust[^20].

While these languages are still niche compared to the established players, they are worth keeping an eye on as they mature and gain adoption in the data science community.

Conclusion: Choosing the Right Language for Your Data Science Career

With so many languages to choose from, it can be overwhelming to decide where to focus your learning efforts. The best language for you depends on your background, interests, and career goals.

If you‘re just getting started with data science, Python is an excellent choice. Its gentle learning curve, extensive resources, and versatility make it well-suited for learning the basics of data manipulation, visualization, and machine learning.

If you have a strong statistics background or work in a specialized domain like biostatistics or finance, R may be a better fit. Its focus on statistical computing and excellent data visualization capabilities make it a great tool for communicating insights.

For data scientists working with large datasets or in production environments, SQL, Java, and Scala are essential skills to have. Their performance, scalability, and integration with big data tools make them well-suited for data science at scale.

Ultimately, the key to success in data science is not just the language you choose, but your ability to solve real-world problems and communicate your findings effectively. By focusing on the fundamentals of data manipulation, visualization, and modeling, and building a strong portfolio of projects, you‘ll be well-positioned for a successful and rewarding career in data science.

[^1]: Stack Overflow Developer Survey 2022, https://survey.stackoverflow.co/2022/
[^2]: Kaggle State of Data Science and Machine Learning 2022, https://www.kaggle.com/code/kaggle/kaggle-survey-2022/report
[^3]: Python at Google, https://opensource.google/projects/python
[^4]: Python at Netflix, https://medium.com/netflix-techblog/python-at-netflix-bba45dae649e
[^5]: Spotify Engineering: Python and Hadoop, https://engineering.atspotify.com/2016/05/11/spotify-engineering-python-and-hadoop/
[^6]: CRAN Packages by Name, https://cran.r-project.org/web/packages/available_packages_by_name.html
[^7]: Facebook‘s Data Scientists, https://newsroom.fb.com/news/2017/05/facebooks-data-scientists/
[^8]: R at The New York Times, http://open.blogs.nytimes.com/2017/01/12/r-at-the-new-york-times/
[^9]: Airbnb Engineering & Data Science, https://airbnb.io/
[^10]: BigQuery ML, https://cloud.google.com/bigquery-ml/docs/introduction
[^11]: dbt (data build tool), https://www.getdbt.com/
[^12]: Presto at Uber, https://eng.uber.com/presto/
[^13]: Building Shopify‘s Data Platform, https://shopify.engineering/building-shopifys-data-platform
[^14]: Stitch Fix Algorithms Tour, https://algorithms-tour.stitchfix.com/
[^15]: LinkedIn‘s Big Data Platform, https://engineering.linkedin.com/teams/data/big-data
[^16]: Twitter‘s Data Platform, https://blog.twitter.com/engineering/en_us/topics/infrastructure/2018/twitters-data-platform.html
[^17]: Netflix: Data Science and Engineering, https://research.netflix.com/research-area/data-science-and-engineering
[^18]: The Julia Language, https://julialang.org/
[^19]: The GoNum Project, https://github.com/gonum
[^20]: Rust-ML Working Group, https://github.com/rust-ml