Programming Languages For Data Scientists

With 256 programming languages available today, choosing which language to learn can be overwhelming and difficult. Some languages work better for building games, while others work better for software engineering, and others work better for data science.

Types of Programming Languages

A low-level programming language is the most understandable language used by a computer to perform its operations. Examples of this are assembly language and machine language. Assembly language is used for direct hardware manipulation, to access specialized processor instructions, or to address performance issues. A machine language consists of binaries that can be directly read and executed by the computer. Assembly languages require an assembler software to be converted into machine code. Low-level languages are faster and more memory efficient than high-level languages.
high-level programming language has a strong abstraction from the details of the computer, unlike low-level programming languages. This enables the programmer to create code that is independent of the type of computer. These languages are much closer to human language than a low-level programming language and are also converted into machine language behind the scenes by either the interpreter or compiler. These are more familiar to most of us. Some examples include Python, Java, Ruby, and many more. These languages are typically portable and the programmer does not need to think as much about the procedure of the program, keeping their focus on the problem at hand. Many programmers today use high-level programming languages, including data scientists.

Programming Languages for Data Science


In a recent worldwide survey, it was found that 83% of the almost 24,000 data professionals used Python. Data scientists and programmers like Python because it is a general-purpose and dynamic programming language. Python seems to be preferred for data science over R because it ends up being faster than R with iterations less than 1000. It is also said to be better than R for data manipulation. This language also contains good packages for natural language processing and data learning and is inherently object-oriented.


R is better for ad hoc analysis and exploring datasets than Python. It is an open-source language and software for statistical computing and graphics. This is not an easy language to learn, and most people find that Python is easier to get the hang of. With loops that have more than 1000 iterations, R actually beats Python using the lapply function. This may leave some wondering if R is better for performing data science on big datasets, however, R was built by statisticians and reflects this in its operations. Data science applications feel more natural in Python.


Java is yet another general-purpose, object-oriented language. This language seems to be very versatile, being used in embedding electronics, in web applications, and desktop applications. It may seem that a data scientist would not need Java, however, frameworks such as Hadoop run on the JVM. These frameworks constitute much of the big data stack. Hadoop is a processing framework that manages data processing and storage for big data applications running in clustered systems. This allows storage for massive amounts of data and enables more processing power with the ability to handle virtually limitless tasks at once. Additionally, Java actually does have a number of libraries and tools for machine learning and data science, it is easily scalable for larger applications, and it is fast.


SQL (Structured Query Language) is a domain-specific language used for managing data in a relational database management system. SQL is somewhat like Hadoop in that it manages data, however, the storage of the data is much different and is explained very well in the above video. SQL tables and SQL queries are critical for every data scientist to know and be comfortable with. While SQL is not able to be exclusively used for data science, it is imperative that a data scientist knows how to work with data in database managing systems.


Julia is another high-level programming language and was designed for high-performance numerical analysis and computational science. It has a very wide range of uses such as web programming for both front and back-end. Julia is able to be embedded in programs using its API, supporting metaprogramming. This language is said the be faster for Python because it was designed to quickly implement mathematical concepts like linear algebra and deals with matrices better. Julia provides the speedy development of Python or R while producing programs that run as fast as C or Fortran programs would.


Scala is a general programming language that provides support for functional programming, object-oriented programming, a strong static type system, and concurrent and synchronized processing. Scala was designed to address many issues that Java has. Once again, this language has many different uses from web applications to machine learning, however, this language only covers front end development. The language is known for being scaleable and good for handling big data as well, as the name itself is an acronym of “scaleable language”. Scala paired with Apache Spark allows the ability to perform parallel processing on a large scale. Furthermore, there are many popular and high-performance data science frameworks written on top of Hadoop to be used in Scala or Java.


In conclusion, Python seems to be the most widely used programming language for data scientists today. This language allows the integration of SQL, TensorFlow, and many other useful functions and libraries for data science and machine learning. With over 70,000 Python libraries, the possibilities within this language seem endless. Python also allows a programmer to create CSV output to easily read data in a spreadsheet. My recommendation to newly aspiring data scientists is to first learn and master Python and SQL data science implementations before looking at other programming languages. It also is apparent that it is imperative that a data scientist has some knowledge of Hadoop.
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