Programming Language Trends in Data Science: Python vs. R vs. SQL Usage Stats

Did you know? ​In early 2025, IPython 9.0 introduced enhanced theming with Unicode support, improving user interface customization. Additionally, it integrated Large Language Model (LLM) capabilities into the command-line interface, offering advanced code suggestions and completions. These updates significantly enhance the interactive computing experience for Python users.

In 2025, approximately 90% of data science professionals report using Python regularly, reflecting its widespread adoption across the industry. SQL maintains its critical role, utilized by about 53% of professionals for data management and querying tasks. Conversely, R's usage has declined to around 38%, indicating a shift towards more versatile programming tools in data science.

Looking ahead to 2032, projections suggest that Python will continue to lead due to its extensive libraries and community support. In this blog, you will explore the current trends in data science programming languages, examining the roles of Python, R, and SQL in the advancing data science stack.

Programming in Data Science

When exploring what is the trend of data in science, programming languages are selected for their versatility and performance with analytical tools. Python dominates due to its huge framework, R excels in statistical modeling, and SQL remains essential for database queries. 

Industry-wise, Python leads in AI/ML, R is favored in academia and biotech, while SQL is indispensable in finance and business intelligence. As data science expands, understanding what makes a language suitable is crucial. Let’s get started.

What Makes a Language Suitable for Data Science?

A programming language's suitability in data science depends on its libraries, scalability, and integration capabilities. Below are key factors that define a strong data science language:

• Extensive Libraries & Frameworks – Python’s vast ecosystem, including TensorFlow and Pandas, enables rapid AI model deployment in industries like healthcare and finance. R’s ggplot2 and dplyr simplify statistical analysis, making it essential in academia and research.
• Statistical & Mathematical Strength – R dominates bioinformatics and healthcare research with CRAN’s specialized statistical packages, such as Bioconductor for genomics. Python’s SciPy and NumPy support engineering simulations, ensuring precise mathematical modeling in aerospace and manufacturing.
• Database Interaction – SQL’s structured querying, especially with MySQL and PostgreSQL, is crucial in banking and e-commerce, where real-time transaction processing is essential. Python’s SQLAlchemy enhances database handling in web applications and analytics platforms.
• Scalability & Performance – Julia’s high-speed execution and Rust’s memory safety make them ideal for real-time analytics in stock trading and cybersecurity. Their ability to handle large-scale computations gives them an edge in performance-critical applications.
• Integration with Big Data & Cloud – Python’s PySpark seamlessly processes massive datasets in distributed environments, making it indispensable in cloud-based AI applications. SQL’s Snowflake optimizes data warehousing, supporting scalable analytics in business intelligence platforms.

With these capabilities in mind, let's explore how programming language usage has shifted from 2020 to 2025.

How Programming Language Usage Has Shifted (2020–2025)?

The period from 2020 to 2025 has witnessed notable shifts in the adoption of programming languages within the data science community. Python has solidified its dominance, while other languages have experienced varying trend of data in science​.​

Key trend of data in science​ and statistics from this period include:

• Python's Ascendancy – By 2024, Python became the most widely used language among data scientists, driven by its versatility and vast ecosystem. With libraries like TensorFlow and Pandas, Python supports applications ranging from machine learning to automation, cementing its role across industries like finance and healthcare.
• SQL's Steady Importance – SQL remained essential, ranking as the third most requested language in 2024. Its necessity in managing large-scale relational databases has made it indispensable in banking, e-commerce, and enterprise analytics.
• R’s Decline, But Continued Niche – While R dropped from 8th place in August 2020 to 19th by November 2023 in the TIOBE index, this decline does not directly reflect its role in data science. R remains crucial in academia, bioinformatics, and research, where its CRAN packages are highly valued for statistical modeling and data visualization.
• Emergence of New Languages – Go has gained traction, ranking 7th in the TIOBE index by April 2025. Its efficiency in handling concurrent processing makes it increasingly popular for large-scale data applications, particularly in cloud computing and backend systems.

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Understanding what is the trend of data in science reveals how programming language preferences grow based on industry demands and community support. Now, let’s have a look at Python vs SQL vs R​.

Python vs R vs SQL: Are We Moving Toward a Polyglot Data Science Stack?

The data science field in 2025 is increasingly polyglot, with Python, R, and SQL serving distinct but complementary roles. Rather than replacing one another, these languages excel in different domains. Python dominates AI and automation, R remains vital in research, and SQL is indispensable for database management and business intelligence.

Here is the feature comparison of Python vs SQL vs R​ in data science (2025 usage projections):

Also Read: Mastering Pandas: Important Pandas Functions For Your Next Project

As Python remains the standard, R and SQL continue to find relevance in their respective niches. Let’s explore why Python dominates the data science industry.

Python for Data Science: Why It’s the Industry Standard? 

Python’s dominance in data science comes from its flexibility, ease of use, and strong environment. Below are key reasons why it remains the top choice:

1. Rich Libraries & Frameworks – Python supports machine learning (TensorFlow, Scikit-learn), big data (PySpark), and automation, enabling companies like PayPal and JPMorgan Chase to enhance fraud detection and financial analytics.
2. Seamless Integration – Python works with SQL databases, cloud platforms, and AI tools, benefiting industries like e-commerce (Amazon’s recommendation engine) and banking (Goldman Sachs’ risk analysis models).
3. Community & Enterprise Support – Backed by a vast developer community, Python sees frequent updates, making it a go-to for IT giants like Google and Microsoft, which use it for scalable cloud solutions and AI research.
4. Versatility Beyond Data Science – Python excels in web development (Django, Flask), web scraping (BeautifulSoup, Scrapy), and finance, where firms like Robinhood use it for algorithmic trading and real-time stock analysis.
5. Scalability for AI & ML – Optimized for deep learning and AI, Python’s frameworks like PyTorch enable real-time analytics in tech and retail, as seen in Tesla’s autonomous vehicle technology and Netflix’s content recommendation systems.

While Python dominates AI and ML, R remains a powerful tool for statistical computing and specialized data analysis.

R for Statistical Computing: Where It Excels

R remains a go-to language for statistical computing, advanced analytics, and data visualization. It is widely used in academia, research, and industries requiring in-depth statistical modeling.

Below are the key strengths of R in data science:

• Advanced Statistical Analysis – Supports regression modeling, hypothesis testing, and time-series forecasting in healthcare and finance.
• Powerful Data Visualization – Offers ggplot2 and Shiny for interactive charts, widely used in market research and journalism.
• Specialized in Bioinformatics & Research – Essential for genomic studies, epidemiology, and pharmaceutical analytics.
• Strong Community for Statistical Computing – Backed by statisticians and researchers, ensuring constant government and policy analysis updates.
• Seamless Integration with Data Science Tools – Works with Python, SQL, and cloud platforms, enabling hybrid workflows in education and fintech.

R still dominates fields like bioinformatics and social science research, where its advanced statistical capabilities remain unmatched.

Also Read: Why Learn to Code Now and How? Top 4 Reasons To Learn

Let’s now explore SQL, the core of data management in data science.

SQL: The Foundation of Data Management

SQL remains the core of data science by enabling efficient data storage, retrieval, and manipulation. It is crucial for handling structured data across industries, ensuring seamless database interaction.

Below are the key aspects that make SQL indispensable in data science:

• Data Querying & Management – Optimized for relational databases like MySQL and PostgreSQL, used in fintech and telecom.
• Scalability for Big Data – Integrated with data warehouses like Snowflake and Google BigQuery for large-scale analytics in retail and healthcare.
• Business Intelligence & Reporting – Powering dashboards in Tableau and Power BI for finance and e-commerce.
• Seamless Integration with Programming Languages – Works with Python and R, enabling hybrid workflows in AI-driven applications.
• Real-Time Data Processing – Used in event-driven architectures with Kafka and Apache Flink for streaming analytics in IoT and logistics.

Now, let’s see how SQL contributes to large-scale data management and analytics.

The Impact of SQL in Enterprise-Level Data Science Solutions

SQL continues to play a critical role in large-scale data-driven decision-making. Below is an overview of how enterprises utilize SQL for data science:

Moving forward, let’s analyze how NoSQL and cloud solutions are challenging traditional SQL approaches.

How NoSQL & Cloud Databases Are Changing SQL’s Role?

As data science moves toward unstructured and real-time analytics, NoSQL and cloud databases are enhancing SQL’s role.

Below are the key ways NoSQL and cloud technologies are influencing SQL:

• Handling Unstructured Data – NoSQL databases like MongoDB and Cassandra support flexible data storage in social media and IoT.
• Scalability in Cloud Environments – SQL-compatible cloud databases like Google Spanner provide elasticity for high-traffic web applications.
• Hybrid Database Solutions – Enterprises use SQL and NoSQL together in multi-cloud strategies for streaming analytics and cybersecurity.
• Real-Time Data Processing – Cloud-native solutions like AWS Aurora enable real-time fraud detection and logistics decision-making.
• AI-Driven Query Optimization – Machine learning-based SQL engines, such as Oracle Autonomous Database, enhance performance in e-commerce and banking.

With SQL shifting alongside NoSQL, the future of data science programming is becoming more diverse. Now, let’s explore how Python, R, and SQL are practically applied in real-world data science scenarios.

Practical Use Cases of Python, R, and SQL in Data Science

Python has emerged as the dominant language, with its demand increasing significantly. R has maintained a steady presence, particularly in academic and research settings, while SQL has remained a fundamental skill due to its critical role in data management.​

In 2020, proficiency among data scientists was reported as 74% for Python, 56% for R, and 51% for SQL. By 2025, these figures have shifted to approximately 85% for Python, 45% for R, and 60% for SQL. This reflects the industry's growing emphasis on Python and SQL.

Let's explore high-demand applications and industry use cases.

1. Python:

• Machine Learning and AI: Extensively used in developing predictive models and AI applications across industries like finance for fraud detection and healthcare for diagnostic algorithms.​
• Data Analysis and Visualization: Utilized for data manipulation and creating insightful visualizations in marketing analytics and business intelligence.​
• Web Development: Employed in building data-driven web applications and dashboards in tech companies and e-commerce platforms.​

2. R:

• Statistical Analysis: Preferred for conducting complex statistical computations and hypothesis testing in academic research and pharmaceuticals.​
• Bioinformatics: Applied in analyzing genomic data and conducting clinical trials in the biotechnology sector.​
• Data Visualization: Used to create detailed graphical representations of data in fields like sociology and environmental studies.​

3. SQL:

• Database Management: Essential for querying and managing relational databases in virtually all industries, including retail and telecommunications.​
• ETL Processes: Integral in Extract, Transform, Load operations to consolidate data from various sources into data warehouses.​
• Business Intelligence: Supports the backend of BI tools, enabling data retrieval for reporting and decision-making in corporate environments.​

Notably, approximately 60% of data science job roles explicitly require SQL proficiency, underscoring its importance in the field. 

After exploring their practical applications, let’s now compare the salary trends for Python, R, and SQL professionals.

Data Science Salary Trends: Python vs R vs SQL

The demand for data science professionals has increased globally, leading to competitive salaries across various roles. Proficiency in programming languages such as Python, R, and SQL significantly influences earning potential. 

Below is a comparative overview of average annual salaries for these roles in selected countries:​

Source: Glassdoor

Note: The above figures are approximate and may vary based on experience, location, and company.

Top-paying industries for these professionals include:

• Financial Services: High demand for data analysis and risk assessment skills.​
• Technology: Focus on software development and data-driven decision-making.​
• Healthcare: Utilization of data for patient care and medical research.​
• E-commerce: Analysis of consumer behavior and logistics optimization.​
• Telecommunications: Management of large datasets for network optimization and customer insights.

Also Read: Top 30 Machine Learning Skills for ML Engineer in 2024

Moving forward, let’s analyze if Julia, Rust, or Go could become the future of data science.

Beyond Python & R: Will Julia, Rust, or Go Take Over?

Data science is witnessing the emergence of new programming languages like Julia, Rust, and Go. Each offers unique features that address specific data analysis and machine learning challenges.​

Below is a comparison of these emerging languages in the context of data science:​

​Rust's developer community has grown from 2 million in Q1 2022 to 4 million in Q1 2024, indicating a significant rise in its adoption. However, as of 2024, Python and R remain the leading languages in data science, with no clear indication that Rust, Julia, or Go have surpassed them in this domain.​

While Julia, Rust, and Go are gaining attention, they currently complement rather than replace established languages like Python and R. Their adoption is often driven by specific project requirements, such as high performance, safety, or concurrency.

Also Read: Importance of Data Science in 2025 [A Simple Guide]

Beyond new programming languages, AI is changing how developers approach data science coding. Let’s see how.

How AI is Changing the Way You Write Code in Data Science?

​By 2028, it's projected that 75% of enterprise software engineers will utilize AI code assistants, a significant increase from less than 10% in early 2023. Platforms like GitHub Copilot and CodeWhisperer are redefining how programmers approach coding, making AI an integral part of software development.

Below are key ways AI is shaping data science programming.

• AI-Powered Code Completion – GitHub Copilot suggests entire functions, reducing debugging time in Jupyter Notebook and VS Code.
• Automated Data Cleaning – AI-driven tools like PandasAI automate data preprocessing, streamlining ETL pipelines in finance and healthcare.
• Language Translation – Models like Codex convert SQL queries to Python, aiding database management in enterprise data analytics.
• AI-Optimized Algorithms – Deep learning libraries like TensorFlow AutoML generate efficient models with minimal manual tuning.
• Enhanced Documentation – AI tools like Mintlify generate explanations for complex ML workflows in retail and cybersecurity.

Also Read: Top Data Analytics Tools Every Data Scientist Should Know About

Beyond AI-driven coding, let’s explore the growing scene of programming in data science.

What’s Next? The Future of Programming in Data Science

​In 2025, Python continues to dominate the data science field, with 66% of data scientists using it regularly. However, emerging trends such as low-code platforms, AI-driven development, and NoSQL databases are changing how professionals interact with data. 

Below are key trends shaping the future of programming in data science.

• Python’s Continued Dominance – Despite new languages, Python’s extensive libraries will keep it a top choice for ML and AI applications.
• Low-Code & No-Code Growth – Tools like DataRobot and KNIME allow non-programmers to build models with minimal coding.
• SQL vs. NoSQL Shift – While SQL remains crucial, NoSQL databases like MongoDB are gaining traction in real-time analytics.
• Polyglot Data Science Stacks – Enterprises are increasingly using Python, R, and SQL together for hybrid analytics solutions.
• AI-Driven Development – AI-powered tools are expected to automate coding tasks, influencing programming language adoption.

Conclusion

Data science programming constantly grows, with Python, R, and SQL leading the industry while new languages like Julia and Rust gain traction. As AI and automation change coding practices, staying ahead in this dynamic field requires continuous learning and upskilling. 

But how can you learn these skills effectively and stay competitive in the job market?

This is where upGrad comes in. With 500+ industry-relevant courses and expert-led sessions, upGrad provides you with a structured learning path.  

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• Data Structures & Algorithms
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