Python vs Julia: Which programming language is best to learn first for Statisticians
Programming language is practically the backbone of data science and in the modern technologies, we have a lot of languages available at our expense. But the question is which one among them is the most suitable for a data scientist. Looking for some of the best programming languages for statisticians? Julia is a multi-paradigm, primarily functional programming language that was created for machine-learning and statistical programming. Python is another multi-paradigm programming language that is used for machine learning, though generally Python is considered to be object-oriented. Julia, on the other hand, is more based on the functional paradigm. Julia is a new buzz in the IT sector, recognized primarily for its speed, and is gaining appeal among Data Scientists and Statisticians. Now for the question, Python vs Julia: which programming language should statisticians learn in 2023?
Python vs Julia: Advantages of Python
Released in 1991, Python is a programming language that is used for web development, software development, mathematics, and systematic scripting. In Python, the first element of an array is accessed with a zero such as string  in Python for the first character in a string. It helps in the way of adoption by a more general-use audience with ingrained programming habits.
Python is popular among developers because of its strength, adaptability, and understandable syntax that is simple to comprehend and master. Almost 70% of developers say they use Python to build high-performance AI and ML algorithms for Natural Language Processing and sentiment analysis. Python, along with R, is the language of choice for Data Science. The breadth and usefulness of Python’s culture of third-party packages remains one of the language’s biggest attractions.
Aside from gaining improvements to the Python interpreter (including improvements to multi-core and parallel processing), Python has become easier to speed up. The mypyc project translates type-annotated Python into native C, far less clunkily than Cython. It typically yields four-fold performance improvements, and often much more for pure mathematical operations.
Python vs Julia: Advantages of Julia
First appearing in 2012, Julia is a high-level, high-performance, dynamic programming language. While it is a general-purpose language and can be used to write any application, many of its features are well suited for numerical analysis and computational science. Julia’s JIT compilation and type declarations mean it can routinely beat “pure,” unoptimized Python by orders of magnitude. Python can be made faster by way of external libraries, third-party JIT compilers (PyPy), and optimizations with tools like Cython, but Julia is designed to be faster right out of the gate.
A major target audience for Julia is users of scientific computing languages and environments like Matlab, R, Mathematica, and Octave. Julia’s syntax for math operations looks more like the way math formulas are written outside of the computing world, making it easier for non-programmers to pick up on. Flux is a machine learning library for Julia that has many existing model patterns for common use cases. Since it’s written entirely in Julia, it can be modified as needed by the user, and it uses Julia’s native just-in-time compilation to optimize projects from the inside out.
Julia is a dynamic, high-level, high-performance programming language designed primarily for technical computing that has a syntax similar to Python. Because linear algebra is an essential component of this language, it is commonly utilized in Machine Learning, Data Science, data mining, numerical analysis, and any mathematical purpose.
Julia’s simplicity, excellent performance, and speed are its selling features for dealing with complicated data models. However, the potential of converting Science’s formulaic language to code is a deal-breaker for scientists: Julia supports the usage of Greek letters, allowing for the direct use of mathematical formulae in the code rather than translating such recipes into coding language.