6 Powerful Programming Languages for Data Science (Beyond Python and R)

Introduction

As a data scientist, you‘re likely very familiar with Python and R – the two most popular and widely used programming languages in the field. Their extensive libraries, active communities, and widespread adoption make them essential tools in any data scientist‘s toolkit.

However, the world of data science programming extends far beyond just Python and R. At the rapid pace the field is evolving, it pays to be a multi-lingual data scientist and leverage the unique strengths of other languages. Being proficient in multiple languages allows you to attack a wider variety of data challenges, collaborate with more teams, and future-proof your skill set.

In this article, we‘ll dive into 6 powerful programming languages that every data scientist should know about and consider learning. While not as ubiquitous as Python or R, these languages bring unique capabilities and advantages that make them invaluable in certain data science applications and environments.

For each language, we‘ll give an overview of its key features, explore why it‘s useful for data science, look at some example use cases, and point you to top libraries and resources to get started. By the end, you‘ll have a much more complete picture of the broader ecosystem of data science languages and be inspired to go beyond Python and R.

Let‘s jump in and expand your data science toolkit!

1. Scala

Scala is a powerful, modern language that combines the best of object-oriented and functional programming paradigms. It was designed to be highly scalable and run efficiently on the Java Virtual Machine (JVM). If you‘re familiar with Java, you‘ll find Scala code to be much more concise and expressive while being interoperable with existing Java libraries.

So why use Scala for data science? The main reasons are its high performance, scalability, and big data capabilities. Scala is up to 10 times faster than Python, making it excellent for processing large datasets and computationally intensive tasks. It‘s the language of choice for many distributed computing frameworks like Apache Spark.

Speaking of Spark, Scala is an ideal language for doing data science on Spark clusters. Its functional programming style is a natural fit for the parallel processing of big data. Scala‘s static typing and JVM foundation also make it more suited for building robust, production-grade data pipelines and machine learning workflows.

Some common use cases for Scala in data science include:

• Processing and analyzing big data with Spark
• Building scalable data pipelines and production machine learning systems
• Doing complex analytics on streaming data
• Deploying parallel machine learning algorithms

To get started doing data science in Scala, here are some top libraries to know:

• Breeze: Breeze is Scala‘s primary scientific computing library, providing data structures and algorithms for analytics and numerical processing
• Vegas: A library for statistical data visualization
• Smile: Statistical Machine Intelligence and Learning Engine (Smile) is a versatile machine learning library for Scala
• DeepLearning.scala: A library for building and training complex neural networks in Scala

2. Julia

Julia is a rising star in the world of data science languages. It was purpose-built from the ground up by a team of computer scientists and mathematicians to be both high-performance and easy to learn. Julia code can approach or match the speed of C while having syntax that is as intuitive as Python or R.

Julia‘s key advantages for data science are its speed, dynamism, and mathematical expressiveness. Its unique type system and just-in-time (JIT) compiler allow it to optimize code on the fly to rival the performance of statically typed, compiled languages. At the same time, it preserves the interactive, iterative workflow loved by data scientists and statisticians.

Julia‘s syntax and many of its popular libraries mirror those from Python and R ecosystems, making it easy to pick up if you‘re already familiar with those languages. In fact, there are Julia versions of essential data science libraries like DataFrames and scikit-learn. Julia is especially suited for tasks involving numerical computing, advanced analytics, and scientific simulations.

Data science use cases for Julia include:

• Analyzing large time-series datasets
• Training machine learning models with complex, custom loss functions
• Conducting Monte Carlo simulations and advanced statistical analysis
• Developing high performance, parallelized data science and AI applications

Key libraries in the Julia data science ecosystem include:

• DataFrames.jl: A library for working with tabular data similar to Pandas DataFrames
• JuliaDB: A distributed analytical database built in Julia
• Flux.jl: A library for building and training machine learning models in Julia
• Plots.jl: A common plotting API and toolset for data visualization in Julia

3. JavaScript

JavaScript is one of the world‘s most popular programming languages, but it‘s often overlooked in the context of data science. While it may not be the first language that comes to mind for training machine learning models, modern JavaScript has evolved to be a surprisingly capable and useful tool for data science.

JavaScript‘s main strengths lie in its versatility, ubiquity, and rich ecosystem for data interactivity and visualization. As a high-level scripting language that can run anywhere, including in web browsers, JavaScript makes it easy to create data-driven applications, dashboards, and tools accessible to anyone.

JavaScript‘s event-driven paradigm and JSON-based data format also make it a natural fit for working with real-time streaming data and doing analysis on unstructured web data. With libraries like TensorFlow.js, you can now define, train, and deploy machine learning models completely in the browser with JavaScript.

Some applications of JavaScript in data science include:

• Building interactive data visualizations and dashboards for the web
• Analyzing clickstream data and user interactions on websites
• Creating and deploying browser-based machine learning applications
• Processing and collecting IoT sensor data for real-time analytics

Essential JavaScript libraries for data science include:

• D3.js: A powerful library for creating interactive data visualizations in the browser
• TensorFlow.js: A JavaScript version of the popular TensorFlow library for in-browser machine learning
• Brain.js: A JavaScript library for training neural networks
• Stdlib: A standard library for JavaScript and Node.js with many mathematical and statistical functions

4. Swift

Swift is a fast, safe, and expressive general-purpose programming language created by Apple as a replacement for Objective-C. While originally used mainly for developing iOS and macOS applications, Swift has found new life in the world of data science and machine learning.

Swift‘s clean, modern syntax makes it very approachable for data scientists used to dynamic languages like Python and R. However, as a statically typed, compiled language, it also offers significant performance gains and type safety. This allows data scientists to write high performance code without sacrificing development speed and ease of use.

The killer feature of Swift for data science is its ability to be used for differentiable programming. Libraries like Swift for TensorFlow make it easy to build and train custom machine learning models using Swift, with the benefits of static typing and first-class automatic differentiation. You can define complex algorithms and neural network architectures in Swift, and get the free performance gains from static compilation.

Example applications of Swift in data science include:

• Designing and training custom deep learning models
• Developing high-performance numerical computing and optimization algorithms
• Building data science and machine learning functionality into iOS/macOS apps
• Doing differentiable programming and architecture search

Key Swift libraries and frameworks for data science:

• Swift for TensorFlow: A full-stack deep learning framework written in Swift
• Surge: A library for Swift that uses hardware-accelerated vector computation
• SwiftPlot: A multi-platform plotting library for Swift
• Swift AI: A high-performance deep learning and machine learning library in Swift

5. Go

Go (or GoLang) is a statically typed, compiled programming language created at Google. Its design focuses on simplicity, reliability, and efficiency. While it‘s more commonly used for systems programming and web development, Go is gaining popularity as a language for doing data science and machine learning.

Go‘s main benefits for data science come from its excellent support for concurrency and its ability to efficiently handle large amounts of data. Its goroutine and channel features make it easy to write concurrent programs that can parallelize data processing tasks. And as a low-level language, it can process high volumes of data with minimal overhead.

Go also has a simple, easy-to-learn syntax and compiles to a single binary, making it attractive for data scientists who want to create portable, performant command line tools for data processing and analysis. Its strong typing and suite of built-in testing tools also promote writing more robust, maintainable code.

Data science use cases for Go include:

• Processing and analyzing big data from distributed systems
• Building high-performance, concurrent machine learning pipelines
• Creating compiled command line tools for data transformation and analysis
• Gathering and processing high-volume API data and logs

Some popular Go libraries for data science:

• Gonum: The main library for scientific and numeric computing in Go, covering common data science tasks
• GoLearn: A machine learning library for Go with algorithms for classification, regression, clustering and more
• Gorgonia: A comprehensive library for building and executing mathematical models in Go, similar to Python‘s NumPy
• golang-data-science-toolbox: A collection of common data science functions and algorithms implemented in Go

6. SQL

SQL (Structured Query Language) is a slightly unconventional entry on this list, as it‘s a domain-specific language for managing and querying relational databases rather than a general-purpose programming language. However, its importance in the data science workflow cannot be overstated.

At its core, data science is all about extracting insights from data – and very often, that data lives in SQL databases. Being proficient in SQL allows a data scientist to efficiently pull, filter, join, and aggregate data from databases for further analysis. Advanced SQL knowledge helps optimize queries and manage SQL-based data pipelines.

SQL is also the foundation of many Big Data querying engines like Hive and Impala. Distributed computing frameworks like Spark have their own SQL APIs and query optimizers. Understanding how to structure efficient SQL queries and interact with data warehouses is key to being an effective data scientist.

SQL-related data science tasks include:

• Extracting features for machine learning models from a data warehouse
• Running exploratory queries and aggregations on large datasets
• Joining disparate datasets together for analysis
• Scheduling and optimizing recurring SQL-based ETL jobs

While SQL is more of a querying language than a typical programming language, there are some SQL extensions and related libraries that aid in data science:

• Apache Drill: A distributed query engine that supports SQL queries on non-relational data
• dplyr: An R library for data manipulation that abstracts common SQL database operations
• SQLAlchemy: A popular Python SQL toolkit and Object-Relational Mapping (ORM) library
• BigQuery ML: A feature of Google Cloud that allows training machine learning models using SQL queries

Conclusion

As we‘ve seen, the world of data science programming is much larger than just Python and R. Scala, Julia, JavaScript, Swift, Go, and even SQL all have unique features and strengths that make them invaluable for certain data science applications.

Learning these languages in addition to Python and R will make you a more well-rounded, adaptable data scientist. You‘ll be able to tackle a wider variety of data challenges, contribute to more diverse codebases and projects, and future-proof your skill set in a rapidly evolving field.

Of course, we‘re not suggesting you need to become an expert in all of these languages. Rather, being aware of their key features and example use cases will help you choose the right tool for the job at hand. You may focus on one or two to specialize in, based on the type of data science work you do.

Ultimately, an expanding toolkit of programming languages is a competitive advantage for modern data scientists. Don‘t be afraid to venture beyond Python and R – you never know what insights and innovations you may unlock by thinking outside the traditional box.

Whatever languages you choose to learn, the most important thing is to practice, build your skills with real-world datasets and challenges, and never stop learning. Hopefully this tour through these powerful data science languages has inspired you to go deeper and keep expanding your knowledge. Happy coding!