In a recent survey of 2,300 data scientists, Python emerged as the clear favorite, securing nearly 66% of the votes as the top tool for analytics, data science, and machine learning. Major organizations like Google, NASA, CERN, Spotify, and Facebook rely on Python for a wide range of tasks, including complex data science operations.Introduced over 30 years ago, Python's widespread and enduring popularity is a testament to its robust design, logical structure, and adaptability—making it a cornerstone of modern data science.

Python is a versatile programming language that supports multiple paradigms, including object-oriented, structured, and functional programming, making it adaptable for a wide range of applications. Whether it’s data mining, web application scripting, scientific computing, or even running embedded systems, Python excels as a unified language for diverse tasks.

Julia, introduced in 2012, was designed to combine the usability of Python, the computational power of Matlab, and the speed of C. As a general-purpose language, Julia can handle many types of applications but truly shines in numerical analysis and computational science due to its speed and efficiency.

So, which one is better for data science? Python’s widespread adoption, extensive libraries, and robust community make it an excellent choice for most use cases. However, Julia’s superior performance for mathematical computations and large-scale simulations positions it as a strong contender for specific data science applications.

Python for data science: Benefits and business value

Simplicity and tons of free libraries

While Python may not inherently excel in statistical analysis, its straightforward design and ease of use make it a favorite among data scientists. Python’s simplicity allows even beginners to pick it up quickly, and the vast array of free analytical libraries—like NumPy, Pandas, and SciPy—means data scientists can find ready-made tools to handle almost any task with minimal setup.

Easy to build out and integrate

Python’s extensibility and versatility have made it a cornerstone of many organizations' tech stacks. Its ability to integrate seamlessly with other systems and applications is a significant advantage for data science workflows. Companies like Facebook, for instance, have standardized on Python for its all-purpose capabilities. As Burc Arpat, Quantitative Engineering Manager at Facebook, explains:“We like to stay in the Python ecosystem. We have a lot of systems inside Facebook, or infrastructure that allows us to either use Python to talk to those systems, integrate with Python very easily, or is written in Python.”

Low startup overhead

Python’s widespread familiarity among programmers significantly lowers the barriers to entry for data science projects. With most engineers already comfortable using Python, the learning curve is minimal, allowing teams to quickly focus on problem-solving instead of learning new tools. The abundance of free libraries further reduces costs, eliminating the need to evaluate and test multiple competing packages. Additionally, onboarding new talent is faster, as many developers are already proficient in Python, streamlining the integration process.

Helpful community assistance

Additionally, the massive Python open-source community is known for being both lively and helpful making it a rich source of information and guidance.

Julia for data science: Benefits and business value

General purpose, high performance

Julia is a high-level, dynamic programming language designed for numerical and scientific computing. Its just-in-time (JIT) compilation via the LLVM compiler framework allows Julia to deliver runtime performance comparable to statically compiled languages like C or C++. This makes it a standout choice for data-intensive tasks requiring both speed and precision.

Light and efficient

Julia’s lightweight architecture enables it to run complex scripts without demanding significant computational resources. Even lightweight computers can handle computationally intensive tasks in real time, optimizing resource usage and ensuring efficiency in operations.

Scales fast

Julia excels at scaling operations quickly and easily, even without big data frameworks. By simply adding more system resources and running essential commands on the Julia kernel, scripts can scale up efficiently to meet growing computational demands.

Julia is friendly

Julia is highly compatible with other popular programming languages like C, Python, and R, allowing seamless integration and facilitating versatile data science workflows. Additionally, Julia’s high-level syntax makes it easy to read and understand, even for those new to the language, enhancing accessibility for non-experts.

Python vs. Julia: Which is better for data science?

Performance

Julia’s performance rivals that of low-level languages like C and Fortran, but with the added benefit of being far easier to write in. Julia allows developers to go from prototyping to production without switching languages. This eliminates the common problem of building a proof of concept (POC) in one language (e.g., Python or R) and re-implementing it in a faster language (e.g., C++ or Java) for production. Julia handles both seamlessly, making it a standout for performance.

Speed

Julia’s JIT compilation and type declarations make it significantly faster than pure, unoptimized Python. While Python’s speed can be improved with external libraries, JIT compilers like PyPy, or optimization tools like Cython, these enhancements add complexity and extra work. Julia, in contrast, delivers speed out of the box. While Python is evolving, with projects like mypyc that translate type-annotated Python into native C, Julia still takes the crown in raw speed.

Versatility

Both Python and Julia are highly versatile, general-purpose languages. Julia shines in scientific computing and data processing, its primary focus, while Python excels across a broader range of applications, from data mining and scientific computing to website scripting and backend systems (e.g., Spotify’s use case). While Python’s overall versatility might give it a slight edge, for data processing and science, both languages are equally capable.

Libraries

Python’s decades-long presence in the programming world has allowed it to amass a vast ecosystem of libraries, with an estimated 137,000 libraries available. While not all of these are relevant to data science, Python offers a specialized library for nearly every data science task. From NumPy and Pandas to Scikit-learn and TensorFlow, Python’s robust library ecosystem makes it the go-to choice for data scientists.

Support & community

Python’s immense and mature community easily towers over the competition. With decades of development and adoption, Python offers a vast network of forums, resources, and contributors that provide robust support for users of all skill levels. This engaged and helpful community makes it easy to find solutions, troubleshoot issues, and stay updated with best practices.Julia’s community, while much younger, is growing rapidly. Since its 2012 launch, Julia has been downloaded by users in over 10,000 companies, with 20,000,000 downloads recorded as of September 2020. The Julia community is friendly, enthusiastic, and actively expanding, but it still has a way to go before it can match Python’s sheer size and maturity.

Which one's better? Python or Julia?

If performance and speed are your top priorities, Julia is an excellent choice. Its just-in-time (JIT) compilation and focus on numerical computing make it ideal for tasks requiring raw computational power.On the other hand, if you need to expand your engineering team quickly or rely on an extensive library ecosystem, Python might be the better option. Python’s popularity and familiarity make it easier to onboard talent, and its vast array of libraries ensures flexibility for nearly any task.

Additionally, if your organization already relies heavily on existing Python scripts and systems, sticking with Python can help maintain consistency and avoid unnecessary complexity.Ultimately, both Python and Julia are exceptional tools, each with its strengths. Whether you choose Python or Julia, you can be confident that either will meet the demands of a skilled data scientist.