If you are considering a career in data science, learning to code is a crucial and non-negotiable step. But getting started with programming can be daunting, especially if you have no prior coding experience. To choose the right language, we must first understand what a data scientist does. A data scientist is a technical expert who uses mathematical and statistical techniques to manipulate, analyze, and extract valuable information from data. Programming is the tool that makes this possible.
Programming is the technique that allows data scientists to interact with computers, send them instructions, and manage vast amounts of information. While data science theory involves math and statistics, programming is the practical skill that brings these theories to life. It allows you to clean messy data, build complex statistical models, and deploy machine learning algorithms at a scale that is impossible to do by hand.
The Modern Data Scientist’s Role
The field of data science is broad and encompasses many specializations. These can range from machine learning and deep learning, which involve creating predictive models, to network analysis, natural language processing, and geospatial analysis. To accomplish their diverse tasks, data scientists must rely on the power of modern computers. They need to be able to tell a computer, with very specific instructions, how to process, analyze, and visualize data.
There are hundreds of programming languages, each designed for a different purpose. Some are better suited to data science, offering high productivity and strong performance for processing large amounts of data. Even within this group, there is still a significant number of languages to choose from. This series will examine the leading programming languages in data science for , presenting the strengths and capabilities of each one to help you make an informed choice.
The Undisputed Leader: Python
Python’s popularity has exploded in recent years, and it remains the most popular and dominant programming language in the data science community. It consistently ranks first in several major programming language popularity indices. Python is an open-source, general-purpose language. This “general-purpose” nature is one of its greatest strengths. It means Python is used not only for data science but also in other fields, such as web development, task automation, and even video game development.
This versatility allows a data scientist to do more than just analyze data. They can use Python to build a web application that serves their machine learning model, create automated data-cleaning pipelines, or integrate their analysis into a larger enterprise application. This ability to handle the entire workflow, from initial data exploration to final production, makes Python an incredibly valuable and efficient tool.
Why Python is Perfect for Beginners
Due to its simple, clean, and readable syntax, Python is often considered one of the easiest programming languages for beginners to learn and use. The code reads almost like plain English, which lowers the barrier to entry. This design philosophy emphasizes readability, allowing developers to write clear and logical code for projects both small and large. For someone new to programming, this means less time spent battling complex syntax and more time learning the core concepts of data analysis.
If you are just starting your journey in data science and do not know which language to learn first, Python is overwhelmingly one of the best and safest options. Its massive community, extensive documentation, and wealth of free tutorials mean that you will never be stuck on a problem for long. An answer is almost always just a quick search away.
The Rich Ecosystem of Python Libraries
Virtually every data science task you can think of can be accomplished with Python. This is not because of the core language itself, but because of its rich and mature ecosystem of third-party libraries. With thousands of powerful packages supported by a huge global user community, Python can perform all sorts of operations. These range from data preprocessing, visualization, and statistical analysis to deploying complex machine learning and deep learning models.
These libraries are free, open-source, and continuously updated by a dedicated community of developers and academics. This means Python’s capabilities are always expanding and keeping pace with the latest industry and research trends. A data scientist using Python is standing on the shoulders of giants, leveraging powerful tools that have been built and refined over many years.
Core Data Science Libraries in Python
The foundation of data science in Python rests on a few key libraries. The first is NumPy, which is a popular package that offers a vast collection of advanced mathematical functions. More importantly, it provides a powerful object called the NumPy array. This data structure allows for highly efficient, high-performance numerical computing and is the foundation upon which many other libraries are built.
The next is pandas, which is a key library in data science. It is used to perform all kinds of data manipulation, also known as DataFrame manipulation. The DataFrame is the central object in pandas, representing a two-dimensional table of data, similar to a spreadsheet. It allows data scientists to easily load, clean, filter, join, and aggregate data, which are tasks that often consume the majority of a project’s time.
Visualization and Machine Learning Libraries
Once data is cleaned, it needs to be visualized. Matplotlib is the standard, foundational Python library for data visualization. It provides the tools to create a wide variety of static, animated, and interactive charts, such as line graphs, histograms, and scatter plots. While other, more modern libraries exist, Matplotlib remains a critical tool for basic plotting.
For machine learning, scikit-learn is the most popular library. Built on top of NumPy, it has become the gold standard for developing a wide range of machine learning algorithms. It features a simple and consistent interface for tasks like classification, regression, and clustering. It provides all the tools a data scientist needs to build, train, and evaluate predictive models.
Python for Deep Learning
In the realm of deep learning, which powers the most advanced artificial intelligence today, Python also dominates. TensorFlow, which was developed by Google, is a powerful computing framework for developing large-scale machine learning and deep learning algorithms. It is used by researchers and companies around the world to build and deploy sophisticated neural networks.
Keras is another open-source library designed to train high-performance neural networks. It is famous for its user-friendly and intuitive interface, which runs on top of other frameworks like TensorFlow. Keras makes it much easier for developers to build and experiment with deep learning models, lowering the barrier to entry for this complex field.
The Evolving Python Landscape
Python’s ecosystem is not static; it is constantly evolving. New libraries are always emerging to improve performance and productivity. For example, Polars is a new DataFrame library that has gained significant attention. It is built in a different language, Rust, and offers much faster performance than pandas for many common operations, making it ideal for larger datasets.
Other libraries are focused on automation. PyCaret is an open-source, low-code machine learning library that automates end-to-end machine learning workflows. It allows data scientists to get from data preparation to a deployed model in a fraction of the time. Finally, the Hugging Face ecosystem, particularly its transformer library, has become the standard for cutting-edge natural language processing applications, enabling the development of powerful models like chatbots and text summarizers.
Python’s Dominance in and Beyond
Python’s combination of a simple syntax, a massive and well-supported library ecosystem, and its general-purpose versatility makes it the undisputed top choice for data science in . It is the one language that can take you from learning your first line of code to deploying a complex deep learning model into a production web application. This comprehensive power is why it has become the industry standard.
For aspiring data scientists, this is a clear signal. Learning Python is a strategic and future-proof investment in your career. It opens the most doors, is supported by the largest community, and provides the most comprehensive set of tools to tackle any data science challenge that comes your way.
The Statistical Powerhouse: R
While Python holds the top spot in general popularity, R remains a top choice and a core language for aspiring data scientists. It is frequently presented in data science forums and academic circles as Python’s main competitor. For many, learning either of these two languages is an essential step to succeeding in the field. R is an open-source, domain-specific language. Unlike Python, which is a general-purpose language, R was explicitly designed from the ground up for data science.
Its creation was driven by statisticians, for statisticians. This makes it an incredibly powerful and expressive language for statistical computing, data analysis, and visualization. It is very popular in financial and academic circles, where rigorous statistical analysis is paramount. R is a perfect language for manipulating, processing, and visualizing data, as well as for statistical computing and machine learning.
R’s Rich Statistical Ecosystem
Like Python, R has a large and dedicated user community. Its strength comes from a vast collection of packages, or libraries, specializing in every conceivable form of data analysis. R’s package repository is known as CRAN, the Comprehensive R Archive Network, and it hosts thousands of packages dedicated to topics from econometrics and bioinformatics to machine learning and high-performance computing. If a new statistical method is published in an academic paper, it is very likely that an R package implementing it will be available shortly after.
This makes R an unparalleled tool for deep statistical research and analysis. For a data scientist who needs to go beyond standard machine learning and apply more nuanced statistical models, R often provides a more robust and complete toolkit than Python.
The Tidyverse: A Modern R Framework
One of the most notable developments in the R community is the Tidyverse. This is a collection of data science packages that share an underlying design philosophy, grammar, and data structures. It was created to make data science in R more efficient, consistent, and intuitive. This ecosystem includes dplyr, a powerful library for data manipulation. It provides simple and consistent “verbs” for filtering, selecting, and summarizing data.
Another core component is the powerful ggplot2, which has become the standard library for data visualization in R. It is famous for its “grammar of graphics,” a layered approach that allows you to build complex and beautiful plots from simple components. Many data scientists prefer ggplot2 over all other visualization tools for its elegance and power.
Machine Learning in R
R is also a formidable platform for machine learning. Libraries such as caret and the newer mlr3 provide comprehensive frameworks that simplify the development of machine learning algorithms. These packages provide a unified interface for hundreds of different models, making it easy to train, test, and compare the performance of various algorithms. R’s strength in statistics also means it has exceptional support for classical statistical learning models that are often overlooked in other ecosystems.
For tasks in a business or research context that demand deep statistical insight and high-quality visualizations, R is often the preferred choice. Its entire design is centered on making the life of a data analyst or statistician as productive as possible.
The RStudio Environment
A discussion of R is incomplete without mentioning RStudio. While it is possible to work with R directly from the command line, it is far more common to use RStudio. This is a powerful third-party interface, or integrated development environment (IDE), that is designed specifically for R. It integrates various essential features into a single, cohesive application. This includes a text editor for writing code, the R console for executing it, a data viewer, a plot viewer, and a debugger.
This all-in-one environment makes the process of data analysis in R incredibly smooth and efficient. It is beloved by the R community and is a primary reason why many data scientists find the workflow in R to be so productive.
The Unsung Hero: SQL
Most of the world’s structured data is stored in databases. SQL, which stands for Structured Query Language, is a domain-specific language that allows programmers and analysts to communicate with, modify, and extract data from these databases. While Python and R are used for analysis and modeling, SQL is used to get the data in the first place. For this reason, a working knowledge of databases and SQL is absolutely essential for any data scientist.
Knowing SQL will allow you to work with a wide variety of relational databases. These include popular and powerful systems like SQLite, MySQL, and PostgreSQL. Despite some minor differences in “dialect” between these database systems, the syntax of basic SQL queries is standardized and highly similar. This makes SQL a very versatile and transferable skill.
Why Data Scientists Cannot Avoid SQL
In an ideal world, a data scientist might receive a clean, perfectly prepared CSV file. In the real world, this almost never happens. Data is spread across multiple tables in large production databases. For example, customer information might be in one table, their orders in another, and product details in a third. It is the data scientist’s job to go into that database and get the data they need.
SQL is the language that allows you to perform this task. You use SQL to “query” the database. You can write a query to select only the columns you need, filter for the specific rows you want, and, most importantly, join these different tables together to create a single, unified dataset for your analysis. This is a daily task for most data scientists.
SQL’s Role in the Data Science Workflow
SQL’s role is different from that of Python or R. It is not typically used for statistical modeling or machine learning. Instead, it is used for the data extraction and manipulation steps that come before the analysis. SQL is a declarative language, which means you tell the database what you want, and the database engine figures out the most efficient how to get it. This makes it very powerful for data filtering and aggregation.
You can use SQL to perform complex aggregations, like calculating the total sales per customer or the average order value per month, directly on the database server. This is far more efficient than pulling millions of raw transactions into Python or R and trying to do the same calculation in your computer’s memory.
The Perfect Partnership: SQL with Python and R
The most effective data science workflow involves a partnership between languages. A data scientist will start by writing a SQL query to extract and pre-process the data. This query might join several large tables and perform initial aggregations, reducing a massive dataset down to a smaller, more manageable size. They then import the results of this query directly into their Python or R environment.
Once the data is in a pandas or Tidyverse DataFrame, the data scientist can then use the powerful statistical and machine learning libraries of those languages to perform their analysis and build their models. For this reason, whether you choose Python or R to begin your data science journey, you should also consider learning SQL. Thanks to its simple, declarative syntax, SQL is much easier to learn than other languages and will be a great asset throughout your career.
The Enterprise Powerhouse: Java
Java is one of the most popular and enduring programming languages in the world. It consistently ranks near the top of indices like the TIOBE and PYPL, although its popularity has seen a slight decline over the last decade as interest in languages like Python has skyrocketed. Java is an open-source, object-oriented language that is legendary for its top-notch performance, stability, and efficiency. Countless large-scale enterprise technologies, complex software applications, and major websites rely on the robust Java ecosystem.
While Java remains a preferred choice for building enterprise-grade applications from scratch, it has gained significant traction in the data science industry, particularly in the big data space. It is not typically a language for initial exploration or visualization, but it is a critical language for building the production systems that power data-intensive applications at a massive scale.
The Java Virtual Machine (JVM)
To understand Java’s role in data science, one must first understand the Java Virtual Machine, or JVM. The JVM is an abstract computing machine that enables a computer to run a Java program. When Java code is compiled, it is transformed into a “bytecode” that can be run on any device equipped with a JVM, regardless of the underlying hardware or operating system. This is the source of Java’s famous “write once, run anywhere” philosophy.
The JVM is a masterpiece of engineering. It is highly optimized, stable, and provides a robust and efficient framework for executing code. This high-performance virtual environment is precisely why it was chosen as the foundation for many of the most popular and critical big data tools used in data science today, including Hadoop, Spark, and Scala.
Java in the Big Data Ecosystem
Java’s primary role in data science is not as a tool for analysis, but as the engine for big data. The foundational big data framework, Apache Hadoop, is written almost entirely in Java. Hadoop is a system that allows for the distributed storage and processing of massive datasets across clusters of computers. Any company working with data at the petabyte scale is likely using a Hadoop-based system.
Because Hadoop’s core is Java, many of the tasks for interacting with it, particularly for writing custom data processing jobs, were traditionally done in Java. This makes Java a critical skill for data engineers and data scientists who are working in these large, established enterprise environments.
Java for ETL and Production Machine Learning
Due to its high performance and stability, Java is a very suitable language for developing large-scale ETL (Extract, Transform, Load) pipelines. These are the systems that collect data from various sources, transform it into a usable format, and load it into a data warehouse. Java’s robust nature makes it ideal for building these mission-critical, 24/7 data pipelines.
Furthermore, Java is excellent for deploying machine learning models into production. A data scientist might prototype a model in Python, but the final, customer-facing application might be written in Java. In this case, the model needs to be deployed within that Java application. Java has its own machine learning libraries, such as Weka and Deeplearning4j, allowing companies to build and serve high-performance models directly within their existing Java-based infrastructure.
The Big Data Specialist: Scala
Although it is not as common to see Scala in the top-ten rankings of programming languages, it is mandatory to talk about this language in the context of data science. In recent years, Scala has become one of the best and most important languages for machine learning and big data. Released in 2004, Scala is a multi-paradigmatic language that was explicitly designed to be a clearer, more concise, and less verbose alternative to Java, while also incorporating features from functional programming.
Scala’s name comes from “scalable language,” and it was designed to grow with the user’s demands. It blends the object-oriented paradigm of Java with the elegant, clean syntax and powerful features of functional programming. This combination makes it a highly expressive language for building complex systems.
Scala’s Relationship with the JVM
Scala’s greatest strength is that it also runs on the Java Virtual Machine. This means Scala code is compiled into the same “bytecode” as Java code. This full interoperability with Java is a game-changing feature. It means a Scala program can use any of the thousands of existing Java libraries, and Scala code can be seamlessly integrated into any Java application. It gets all the performance and stability benefits of the JVM.
For developers, this means they can get the best of both worlds. They can use the modern, functional syntax of Scala to write clean and concise code, while still leveraging the mature, powerful, and proven ecosystem of Java. This is why Scala became the perfect language for the next generation of big data tools.
Scala and Apache Spark
If Hadoop was the tool that made big data possible, Apache Spark is the tool that made it fast. Apache Spark is a high-speed, unified analytics engine for large-scale data processing. It is often hundreds of times faster than Hadoop for many tasks because it processes data in-memory rather than on disk. Apache Spark is the dominant tool for big data analytics today, and it is written almost entirely in Scala.
This makes Scala the native language of Spark. While Spark provides user-friendly APIs for Python (PySpark) and R (SparkR), the core engine is Scala. For data engineers and data scientists who need to fine-tune Spark, access its most advanced features, or write high-performance custom functions, knowing Scala is a significant advantage. The best performance in Spark is often achieved by writing code in Scala.
The Pros and Cons of Scala
Scala’s primary advantage is its powerful combination of functional and object-oriented programming, its clean syntax, and its native relationship with Apache Spark. It is an excellent, high-performance language for building sophisticated data pipelines and machine learning models on massive datasets. It is a highly sought-after skill in any company that has a serious big data operation.
The main disadvantage is its learning curve. Scala is a complex and powerful language, and it can be much more difficult for a beginner to learn than Python. Its community, while dedicated, is also significantly smaller than Python’s, meaning there are fewer tutorials, examples, and libraries available. It is a language for specialists, but for those specialists, it is an invaluable tool.
When to Choose a JVM Language
For an aspiring data scientist, neither Java nor Scala is a recommended starting language. The journey should almost always begin with Python or R. However, as you advance in your career, you may find yourself working with massive datasets that require tools like Spark. At this point, learning a JVM language becomes a strategic career move.
If you are working in a large corporation that has a heavy investment in Java, learning Java can help you integrate your data science models into the company’s core applications. If your primary job becomes managing and analyzing petabyte-scale data using Apache Spark, learning Scala will unlock the full power of the tool and make you an incredibly valuable data or machine learning engineer.
The Need for Raw Speed: C and C++
In the world of data science, Python and R are beloved for their flexibility and ease of use. However, they are “interpreted” languages, which means they are relatively slow from a computational perspective. For most data analysis tasks, this is not a problem. But when you are working with computationally intensive tasks, such as training a massive neural network or running complex simulations on huge datasets, that “slowness” becomes a critical bottleneck.
This is where low-level, “compiled” languages come in. C and its close relative, C++, are considered to be two of the most optimized and fastest programming languages in existence. They provide a level of control over system hardware, like memory, that other languages do not. This makes them the ultimate candidates for developing high-performance applications where speed is the most important factor.
Why Low-Level Languages Matter in Data Science
The high-level languages that data scientists love, like Python, are not fast on their own. The secret to their success is that their most important libraries are not actually written in Python. The core components of the most popular machine learning libraries, including foundational tools like NumPy and SciPy, as well as complex deep learning frameworks like PyTorch and TensorFlow, are written in C++.
Python acts as a “wrapper” or a “glue” language. It provides a simple, user-friendly interface that allows data scientists to control these high-speed C++ components. When you call a function in NumPy to multiply two large arrays, Python is just a switchboard. The actual mathematical computation is being executed by an underlying, highly-optimized C++ or C function. This is how you get the best of both worlds: Python’s ease of use and C++’s performance.
When Would a Data Scientist Use C++ Directly?
Given that Python already provides a friendly wrapper, a typical data scientist will almost never need to write C++ code. This task is usually reserved for a more specialized role, such as a machine learning engineer or a quantitative researcher. These specialists might use C++ to implement a novel, custom algorithm that is not yet available in a standard library.
For example, in high-frequency financial trading, firms write their entire trading systems in C++ to achieve the lowest possible latency, where every nanosecond counts. Similarly, a researcher developing a new type of neural network layer might first write it in C++ to ensure it runs as fast as possible. They might then create Python “bindings” so that other data scientists can use their new invention.
The Steep Learning Curve
Due to their low-level nature, C and C++ are among the most difficult programming languages to learn. They require the programmer to manually manage memory, which is a complex task that can lead to significant bugs if not done correctly. For this reason, they are not recommended as a first choice for someone starting out in data science. They are advanced tools for specialized problems.
However, once you have a solid understanding of programming fundamentals from a language like Python, mastering C++ can be a smart decision that makes a big difference to your resume. It signals to employers that you have a deep understanding of computation and are capable of tackling the most challenging, performance-critical tasks.
The Rising Star: Julia
For decades, data scientists have lived with the “two-language problem.” They perform their initial research and prototyping in a slow but easy-to-use language like Python or R. Then, when the model is ready for production, they have to hand it off to an engineer to be completely rewritten in a fast but difficult language like C++ or Java. This process is slow, expensive, and a major source of friction. Julia was created to solve this problem.
Released in 2011, Julia is a modern, high-performance programming language designed specifically for scientific and numerical computing. It was designed from the ground up to be both easy to use and incredibly fast. It aims to be the single language that can take a data scientist from initial research to final production, eliminating the two-language problem.
Julia’s Promise: Fast and Easy
Julia’s creators sometimes refer to it as the “heir to Python.” It is a highly effective tool that offers clear, readable syntax, making it easy to learn for those with a background in other dynamic languages. Unlike Python, however, Julia is a “just-in-time” compiled language. This means it achieves performance on par with compiled languages like C and C++ without sacrificing the interactive, user-friendly feel of a language like Python.
This makes it an ideal candidate for data science. A data scientist can write their code in a simple, expressive way, and Julia’s compiler will automatically optimize it to run at high speeds. This allows for rapid prototyping of complex, computationally-heavy models, such as those used in scientific simulations or advanced financial modeling.
The Julia Ecosystem and Community
Julia’s main drawback is its youth. It is one of the youngest languages on this list, and as a result, its community and ecosystem of libraries are much smaller than those of Python and R. While it has powerful libraries for data manipulation (DataFrames.jl) and visualization, the sheer breadth of packages available for Python, especially in machine learning and deep learning, is still far greater.
Despite this, Julia has already impressed the digital computing world and has been adopted by several large organizations, particularly in the financial and scientific computing industries. It is not as widely adopted as its main competitors, but it is a very promising language for the future of data science. Its speed, clear syntax, and versatility make it a language to watch closely in the coming years.
When to Choose a Performance Language
For the vast majority of aspiring data scientists, Python or R is the correct starting point. These languages and their libraries are more than sufficient for 95% of the data science tasks you will encounter in a typical business setting. The need for the extreme performance of C++ or Julia only arises in very specialized, computationally-bound domains.
If your career path leads you to quantitative finance, robotics, or cutting-edge AI research, then learning one of these performance languages will be essential. Julia, in particular, offers an exciting glimpse into the future, where the trade-off between “easy” and “fast” may no longer exist.
Data Science on the Web: JavaScript
JavaScript is one of the most popular and ubiquitous programming languages in the world, consistently ranking in the top three of both the PYPL and TIOBE indices. It is a versatile, multi-paradigm language that is the undisputed king of web development. It is the language that runs in your web browser, allowing for the creation of rich, interactive, and dynamic web pages. For decades, it was almost exclusively the domain of front-end and back-end web developers.
However, as the language and its ecosystem have matured, JavaScript has gained prominence in the data science field. This is not because it is the best tool for offline statistical analysis, but because it is the only tool for running data science applications directly in the browser. This has opened up a new frontier for interactive, private, and low-latency machine learning.
Machine Learning in the Browser
The most significant development for JavaScript in data science is the rise of libraries like TensorFlow.js. This is a version of Google’s popular deep learning framework that is designed to run directly in the user’s web browser. This capability has profound implications. First, it enables a new class of interactive web applications where machine learning models can run in real-time, responding to user input without a-round trip to a server.
Second, it is a massive win for user privacy. The data, which could be sensitive, never has to leave the user’s computer. The model is downloaded to the browser, and the computation happens locally. This is ideal for applications processing personal or medical data. It also reduces server costs, as the computational work is being done by the end-user’s device.
Interactive Data Visualization with D3
Long before machine learning in the browser was feasible, JavaScript was already a powerhouse in one key area of data science: data visualization. While Python’s Matplotlib and R’s ggplot2 are excellent for static charts, JavaScript is the king of interactive visualizations. The most powerful and well-known library for this is D3.js.
D3 is not a simple charting library; it is a framework for binding data to web page elements and applying data-driven transformations to them. It gives the developer complete control over the final visual, allowing for the creation of beautiful, complex, and highly interactive charts, maps, and diagrams. For any data scientist who needs to build a public-facing, interactive data dashboard, a working knowledge of JavaScript and D3 is an invaluable skill.
An Entry Point for Web Developers
The rise of data science tools in JavaScript has a significant secondary effect: it dramatically lowers the barrier to entry for millions of existing web developers. Front-end and back-end programmers, who are already experts in JavaScript, can now explore the world of data science without having to learn an entirely new language like Python or R from scratch. They can leverage their existing skills to build and deploy machine learning models. This cross-pollination of skills is a major reason for JavaScript’s growing prominence in the field.
Data Science on Mobile: Swift
One of the major drawbacks of Python and R is that neither was designed for mobile devices. In the coming years, we can expect even greater growth in mobile computing, wearable devices, and the Internet of Things (IoT). These devices generate a massive amount of data, and they also represent a new platform for running machine learning models. Swift is the modern programming language developed by Apple to facilitate application creation for its ecosystem, including iOS, macOS, and watchOS.
Shortly after its release in 2014, it became clear that Swift was not just for building apps, but also for powering the next generation of on-device machine learning. Apple and Google began collaborating to make it a key tool in the interaction between mobile computing and machine learning.
Why On-Device AI is a Growing Field
Running AI models directly on a mobile device, known as “on-device AI,” has many of the same benefits as browser-based AI. It is extremely fast, as there is no network latency. It is private, as the user’s data never has to be uploaded to a server to be processed. This is essential for features like real-time language translation, camera filters, and health monitoring on a smartwatch.
Swift is the primary language for building these features within the Apple ecosystem. Apple has invested heavily in its own machine learning frameworks, like Core ML, which are optimized to run high-performance models directly on the iPhone’s or Mac’s specialized hardware. A data scientist who can train a model in Python and then convert it for use in a Swift application is incredibly valuable.
Swift’s Data Science Potential
Swift is a modern language that was designed for performance, safety, and readability. It is now open-source and can run on Linux, extending its reach beyond just the Apple ecosystem. It is now compatible with TensorFlow and is designed to be interoperable with Python, allowing developers to leverage the best of both worlds.
While the data science community for Swift is still very small compared to Python’s, its importance will grow as on-device AI becomes more common. For a mobile developer who is curious about data science, or a data scientist who is interested in deploying models on wearables and mobile phones, Swift is the right tool for the job. It represents a specialized but rapidly expanding frontier in the field.
The Web and Mobile Specialization
JavaScript and Swift are not traditional data science languages. You would not use them for a deep, offline statistical analysis of a large dataset. Python and R are far superior for those tasks. Instead, JavaScript and Swift are “last mile” languages. They are for deploying and integrating AI models into the platforms that billions of people use every day: the web browser and the mobile phone.
For a data scientist, knowing these languages represents a powerful specialization. It allows you to be a “full-stack” data scientist, capable of not only building a model but also deploying it directly to the end-user. As AI becomes more interactive and integrated into our daily devices, the value of these skills will only continue to increase.
The New Infrastructure Contender: Go
Go, also known as GoLang, is an increasingly popular programming language that is gaining traction for machine learning projects. It currently holds a strong position in the top popularity rankings. Go was introduced by Google in 2009. It was designed to have a syntax and layout similar to the C language but with modern features like simple concurrency and memory safety. Many developers consider Go to be the 21st-century version of C.
More than a decade after its launch, Go has become extremely popular in the world of infrastructure and backend development. This is thanks to its flexible and easy-to-understand language, its high performance, and its outstanding support for concurrency, which is the ability to run many tasks simultaneously.
Go’s Role in the Data Science Ecosystem
In the specific context of data science, Go is not typically used for the analysis or modeling itself. Python and R have vastly superior libraries for those tasks. Instead, Go is a valuable tool for building the infrastructure that supports machine learning tasks. Because it is fast and excellent at handling concurrent network requests, Go is an ideal language for writing the high-performance API servers that “serve” a machine learning model to users.
While the Go data science community is still relatively small, its potential for building the robust, scalable data pipelines and production services that surround a model makes it a valuable tool for data engineers and machine learning engineers.
The Academic and Engineering Giant: MATLAB
MATLAB is a language and interactive environment that was primarily designed for numerical computation. It has been a dominant force in universities and scientific research since its launch in 1984. MATLAB provides a massive, integrated toolkit of powerful functions for performing advanced mathematical and statistical operations. It is widely adopted in many fields of engineering, physics, and applied mathematics.
For decades, MATLAB was the default tool for data science tasks. It is an ideal candidate for data science, with a rich set of “toolboxes” for signal processing, image analysis, and even machine learning. It provides a single, cohesive environment for performing complex calculations and visualizing the results.
The Proprietary Drawback of MATLAB
MATLAB has one significant drawback that has led to its decline in popularity in the broader data science community: it is proprietary software. Unlike open-source languages like Python and R, which are free for anyone to use, MATLAB requires a license. Depending on the use case (whether it is academic, personal, or professional), you may have to pay a substantial fee for this license.
This creates a high barrier to entry for new users, startups, and individual developers. This is the primary reason why open-source languages, which are free and backed by a larger, more collaborative community, have become the preferred choice for most data scientists today.
A Legacy of Analytical Excellence
SAS, short for Statistical Analytical System, represents one of the earliest and most influential platforms in the world of data analysis. Developed in the 1970s, SAS became synonymous with enterprise-level analytics, long before the rise of modern data science tools. It was purpose-built to meet the needs of large organizations—offering reliability, scalability, and a vast suite of statistical and reporting capabilities. For decades, SAS set the standard for what corporate analytics could achieve.
Engineered for Business Intelligence and Statistical Power
SAS was designed as a complete environment for data management, statistical modeling, and business intelligence. It offered an integrated suite of tools that allowed companies to collect, clean, transform, and analyze massive datasets—all within a single system. Its built-in procedures for regression, forecasting, and time-series analysis made it indispensable for organizations that required precision and consistency in their reporting and decision-making.
Adoption Across Core Industries
Large, established corporations—particularly in banking, insurance, pharmaceuticals, and healthcare—have long relied on SAS as their primary analytical platform. These industries value SAS for its ability to handle sensitive data securely, comply with strict regulations, and produce auditable results. Over time, this led to the creation of a vast professional ecosystem, where proficiency in SAS became a highly sought-after skill for analysts, statisticians, and data managers.
Reliability as a Defining Feature
SAS’s biggest strength has always been its reliability. It rarely crashes, handles large datasets efficiently, and maintains data integrity across complex pipelines. For organizations where accuracy is paramount—such as financial risk modeling or clinical trials—SAS offers the confidence that results will be consistent and verifiable. This reliability helped SAS earn its reputation as the “corporate gold standard” of analytics software.
Built for Scale and Compliance
Beyond performance, SAS is engineered to meet the operational needs of enterprise environments. Its architecture supports centralized data governance, access control, and compliance with industry regulations such as HIPAA and GDPR. For regulated sectors, this level of security and traceability has been a decisive advantage over open-source alternatives that require additional layers of configuration and oversight.
The Workforce Behind SAS
Because of its dominance in corporate environments, SAS created a strong and enduring job market for skilled users. For years, learning SAS was considered a ticket to stable, high-paying roles in analytics and data management. Many universities and professional training programs included SAS in their curriculum to align with corporate demand. Even today, a large number of job postings in traditional sectors list SAS expertise as a preferred or required qualification.
Why Enterprises Still Trust SAS
Enterprises continue to invest in SAS because it provides an all-in-one ecosystem with robust customer support and long-term vendor accountability. Unlike open-source tools that depend on community support, SAS offers dedicated technical assistance and certified updates. For mission-critical operations—where downtime or software bugs can result in significant financial or regulatory consequences—this reliability and support structure remain a strong incentive to stay with SAS.
Integration and Modernization Efforts
Despite being a legacy system in many respects, SAS has evolved over the years to stay relevant. The company has introduced newer interfaces, cloud-based offerings, and compatibility with open-source languages like Python and R. This hybrid approach allows enterprises to modernize their data workflows without fully abandoning their existing SAS infrastructure. It also makes SAS professionals more versatile, as they can integrate modern data science tools into traditional analytics pipelines.
Challenges in the Modern Landscape
However, SAS’s proprietary nature poses a growing challenge in a world increasingly driven by open-source innovation. The cost of licensing and the slower pace of community-driven evolution make it less appealing to startups, researchers, and independent data scientists. As a result, while SAS remains strong in legacy corporate environments, its influence is gradually diminishing among new generations of professionals who prefer open and flexible ecosystems.
The Enduring Relevance of SAS Expertise
Even with the rise of open-source alternatives, SAS skills continue to command respect in the corporate world. Many large organizations have decades of historical data stored in SAS systems, and transitioning away entirely can be both costly and risky. This ensures that SAS professionals will remain in demand for maintaining, upgrading, and integrating existing systems. For anyone targeting a career in enterprise analytics, especially in finance or pharmaceuticals, mastering SAS can still provide a competitive advantage.
A Symbol of Corporate Stability
Ultimately, SAS embodies the qualities that large corporations value most—stability, security, and accountability. It may no longer be at the cutting edge of innovation, but it remains a cornerstone of enterprise analytics. While the broader industry moves toward open-source, SAS continues to serve as a reliable foundation for organizations that prioritize control, compliance, and long-term consistency over rapid experimentation.
The Changing Landscape of Data Science Tools
The field of data science has undergone a significant transformation over the past decade. Once dominated by proprietary tools such as SAS and MATLAB, it is now led by open-source languages like Python and R. This shift is driven by accessibility, cost, and the rapid pace of innovation within open-source communities. As more organizations embrace flexible and collaborative technologies, the preference for open-source tools continues to grow across industries.
The Cost Barrier of Proprietary Software
One of the main reasons for the decline of proprietary platforms is their cost. Tools like SAS require expensive licensing fees, making them less attractive to students, startups, and small companies. In contrast, open-source languages are free to download, use, and modify. This affordability encourages experimentation, learning, and widespread adoption. As new data scientists enter the field, they naturally gravitate toward the tools that are both accessible and industry-relevant.
The Rise of Python and R
Python and R have emerged as the leading languages for modern data science. Both are open-source and supported by extensive libraries that make data analysis, visualization, and machine learning easier than ever. Python, in particular, stands out for its readability and versatility, while R remains popular for advanced statistical modeling. Their strong community support ensures continuous development and adaptation to new technologies, which keeps them ahead of proprietary competitors.
Community-Driven Innovation
The open-source model thrives on collaboration. Thousands of developers contribute to improving libraries, frameworks, and tools that make Python and R more powerful each year. This collective innovation allows open-source ecosystems to evolve rapidly, incorporating the latest advances in artificial intelligence, automation, and cloud computing. Proprietary software, constrained by corporate development cycles, often struggles to keep pace with this level of agility and creativity.
Flexibility and Integration Advantages
Open-source tools offer flexibility that proprietary systems cannot match. They integrate seamlessly with other technologies, cloud platforms, and programming environments. Developers can customize and extend functionality according to project needs without waiting for vendor updates. This adaptability makes open-source software ideal for modern workflows that demand scalability, automation, and interoperability across multiple systems and data sources.
Educational and Research Accessibility
Open-source languages have revolutionized education and research in data science. Students can learn Python or R without financial barriers, while researchers can freely share their code and reproduce results across institutions. This openness fosters collaboration, transparency, and innovation in academic and industrial research. As a result, most data science courses and online certifications now focus on open-source technologies rather than proprietary platforms.
The Decline of MATLAB and SAS Among New Professionals
While MATLAB and SAS once dominated academic and corporate analytics, their influence is waning among new professionals. The younger generation of data scientists is trained primarily in open-source environments, where learning resources are abundant and free. This generational shift accelerates the decline of proprietary tools, as more organizations adopt the languages their workforce already knows. Over time, this creates a feedback loop that reinforces open-source adoption.
Why SAS Still Matters in Certain Sectors
Despite its decline, SAS remains relevant in specific industries such as banking, healthcare, and government. These sectors rely on SAS for its stability, regulatory compliance, and established data governance frameworks. Many legacy systems are deeply integrated with SAS, and organizations may be reluctant to replace them due to the cost and complexity of migration. Therefore, having SAS expertise can still open opportunities in traditional corporate environments.
Balancing Legacy Systems with Modern Tools
In many large organizations, SAS continues to coexist with open-source tools. Companies are gradually modernizing their analytics stacks by integrating Python and R into existing workflows rather than replacing SAS entirely. This hybrid approach allows them to preserve the reliability of established systems while benefiting from the innovation and cost-effectiveness of open-source solutions. Professionals who understand both environments are particularly valuable in facilitating this transition.
The Broader Impact of Open-Source Adoption
The move toward open-source software is not just a technical shift—it reflects a broader cultural change. It encourages transparency, shared learning, and global collaboration. Open-source tools empower individuals and organizations to innovate without financial or legal constraints. This democratization of technology has made data science more inclusive and accelerated progress across disciplines, from artificial intelligence to business intelligence and beyond.
The Future of Proprietary Analytics Platforms
The future of proprietary platforms like SAS and MATLAB depends on their ability to adapt to open ecosystems. Many are now offering integration with open-source languages, cloud support, and flexible licensing models to remain competitive. However, unless they fully embrace openness and community-driven innovation, their influence will continue to decline. The momentum clearly favors open, collaborative technologies that evolve in real time with user needs.
How to Choose
We have navigated the rich and varied landscape of programming languages in data science. There is no single language that is the absolute best for solving every problem. Choosing a preferred language is subjective and often depends on a data scientist’s background or their company’s current technology stack. Data science is increasingly focused on Python and SQL for programming, although R is still very popular and Julia is on the rise.
If you are new to data science, starting with Python or R is the best strategy. Python is arguably the safer, more versatile bet, as it is a general-purpose language that opens more doors. You can sign up for introductory tutorials for both to see which one you like best. From there, the key to success is patience and practice.
The Final Word
Once you feel comfortable with your chosen language, whether it is Python or R, you should take your skills to the next level by pursuing solid training in SQL. This is not optional; it is a fundamental skill for accessing the data you will need to analyze. Knowing both an analysis language and a query language is the baseline for a successful data scientist.
From there, anything is possible. Knowing several programming languages is a powerful asset. The ability to switch between languages depending on your organization’s needs will help you become a more versatile and successful data scientist. You might start with Python, add SQL, and then learn Scala to work on your company’s big data platform. This adaptability will define your career.