Lost in the Jungle of Programming Languages? Find Your Data Science Oasis!

In a nutshell, a programming language is a set of instructions to generate different kinds of outputs. These languages are used to implement various algorithms and programs on computers. There are many languages that can be used in programming for data science; let’s introduce some well-known ones.

Low-Level vs. High-Level Languages

We can divide programming languages according to their levels of implementation on computers. They are divided into two groups: low-level and high-level. You may think that this division is related to their level of quality or popularity, but you are wrong.  

Low-level languages are less complicated and more understandable to computer hardware; in contrast, high-level languages use more abstract concepts and are more human-readable. In general, data scientists prefer to use high-level languages to implement their ideas easily and in a short time; however, when criteria such as performance and memory efficiency are crucial in some applications, low-level languages will be selected.

Common Programming Languages

In the following, some famous high-level languages used by data scientists will be introduced briefly:

1. Python is open-source and the most widely used language in the data science field for its high performance, easy learning curve, and strong libraries for various tasks thanks to its large developers' community.

2. R is another open-source language developed by statisticians. Various implemented libraries for statistics and visualizations can be counted as its strength. However, some believe that it is more complicated to learn R than Python.

3. Java is a general programming language. Its high speed and comprised machine learning libraries make it a good choice for programming in data science. Moreover, big data frameworks such as Hadoop are written in Java.

4. Scala is another general programming language that can be used for many use cases, from web development to machine learning. Its functional programming feature helps to handle big data easily.  

In different circumstances, we may need to use different programming languages in our data science journey. However, Python is the most popular language in various parts of the data science learning path.

Python is the Swiss Army knife of languages by supporting multiple paradigms, including functional programming, object-oriented programming (OOP), structured programming, and procedural programming. It is an extensible and portable programming language that can be run on Unix, Mac, or Windows. Because of its accessibility and portability, it has no shortage of users.

This dynamic language is easy to learn and read, so it's the optimal choice for beginners. There are hundreds of specialized libraries for Python that facilitate different functionalities, including but not limited to: 

• NumPy (adding support for multi-dimensional arrays and matrices, and mathematical functions)
• SciPy (for scientific and technical computing)
• Pandas (for data manipulation and analysis)
• Matplotlib (for creating visualizations) ‍
• Scikit-learn (a comprehensive machine learning library)

Characteristics of Python

Here are the most important characteristics of Python programming which have made it so popular:

• Easy-to-learn: Python has few keywords, a simple structure, and a clearly defined syntax. This allows the student to pick up the language quickly.
• Easy-to-read: Python code is more clearly defined and visible to the eyes.
• Easy-to-maintain: Python's source code is fairly easy-to-maintain. A broad standard library: Python's bulk library is very portable and cross-platform compatible with UNIX, Windows, and Macintosh. Interactive Mode: Python has support for an interactive mode that allows interactive testing and debugging of snippets of code. ‍
• Portable: Python can run on a wide variety of hardware platforms and has the same interface on all platforms.  ‍
• Extendable: You can add low-level modules to the Python interpreter. These modules enable programmers to add to or customize their tools to be more efficient.  ‍
• Databases: Python provides interfaces to all major commercial databases. ‍
• GUI Programming: Python supports GUI applications that can be created and ported to many system calls, libraries, and Windows systems, such as Windows MFC, Macintosh, and the X Window system of Unix.  ‍
• Scalable: Python provides a better structure and support for large programs than shell scripting.  

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