The demand for data scientists in every industry is growing substantially. For the development of every business, there is a need to assess the data you gather. And data scientists require both the right tools and perfect skill set to enable you to produce better results with your information. Data Mining also plays an important role here.

Data science is a concept of bringing together statistics, data analysis, and their related strategies to understand and analyze real wonders with data. It engages theories and techniques drawn from various fields within the wide regions of statistics, mathematics, computer science, and information science.

With the advancement of machine learning, data science is gaining more popularity. In order to understand and become a data scientist, you must learn at least one programming language (although knowing more than one is advantageous to job seekers). You have many options to choose from.      

Looking forward to becoming a Data Scientist? Check out the Data Science Bootcamp Program and get certified today.

The 2 Major Types of Programming language are for Data Scientists:

A low-level programming language is most understanding language use by a computer to perform its operations. Examples of this are assembly language and machine language. Assembly language is use for direct hardware manipulation, to access specializ processor instructions, or to address performance issues.

A machine language consists of binaries that can be directly read and execute by the computer. Assembly languages require an assembler software to be convert into machine code. Low-level languages are faster and more memory efficient than high-level languages.

A 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


Top Programming Languages a Data Scientists should Master :


Python holds a special place among all other programming languages. It is an object-oriented, open-source, flexible, and easy to learn a programming language and has a rich set of libraries and tools designed for data science.

Also, Python has a huge community base where developers and data scientists can ask their queries and answer queries of others. Data science has been using Python for a long time and it is expected to continue to be the top choice for data scientists and developers. Leran about in detail by opting for a Machine Learning with Python online course.


It 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 apply 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 Course applications feel more natural in Python.


Referred to as the ‘meat and potatoes of Data Science’, SQL is the most important skill that a Data Scientist must possess. SQL or ‘Structured Query Language’ is the database language for retrieving data from organized data sources called relational databases.

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In Data Science, SQL is for updating, querying, and manipulating databases. As a Data Scientist, knowing how to retrieve data is the most important part of the job. SQL is the ‘sidearm’ of Data Scientists meaning that it provides limited capabilities but is crucial for specific roles. It has a variety of implementations like MySQL, SQLite, PostgreSQL, etc.

In order to be a proficient Data Scientist, it is necessary to extract and wrangle data from the database. For this purpose, knowledge of SQL is a must. SQL is also a highly readable language, owing to its declarative syntax. For example SELECT name FROM users WHERE salary > 20000 is very intuitive.


Julia is a recently developed programming language that is best suited for scientific computing. It is popular for being simple like Python and has the lightning-fast performance of C language. This has made Julia an ideal language for areas requiring complex mathematical operations.

As a Data Scientist, you will work on problems requiring complex mathematics. Julia is capable of solving such problems at a very high speed. While Julia faced some problems in its stable release due to its recent development, it has been now widely being recognized as a language for Artificial Intelligence.

Flux, which is a machine learning architecture, is a part of Julia for advanced AI processes. A large number of banks and consultancy services are using Julia for Risk Analytics.


TensorFlow is an excellent open-source software library for numerical computation. It is a machine learning framework suitable for large-scale data. It works on the basic concept. For instance, if you want to perform a graph of computations in Python, once you defined, then TensorFlow will run it by utilizing a set of tuned C++ code.

One of the most significant advantages of TensorFlow is that the graph can be broke into many chunks that can keep running in parallel over various GPUs or CPUs. And also supports distributed computing; thus, you will be able to train huge neural networks on immense training sets in a short time.

TensorFlow is the second generation system from Google Brain.  It powers a large number of Google’s large-scale services, like Google Search, Google Photos and Google Cloud Speech.


This is a general programming language that provides support for functional programming, object-oriented programming, a strong static type system, and concurrent and synchronized processing. It was design 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 scalable and good for handling big data as well as the name itself is an acronym of “scalable 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 use in scala or java.

Top Scala Libraries for Data Scientists

  • Breeze: Breeze is a library for numerical processing, like probability and statistic functions, optimization, linear algebra, etc.
  • Vegas: Scala library for data visualization.
  • Smile: Statistical Machine Intelligence and Learning Engine (Smile) is a modern machine learning library.
  • DeepLearning.scala: It is a simple library for creating complex neural networks from object-oriented and functional programming constructs.
  • SAS

Like R, you can use SAS for Statistical Analysis. The only difference is that SAS is not open-source like R. However, it is one of the oldest languages designed for statistics. The developers of the SAS language developed their own software suite for advanced analytics, predictive modeling, and business intelligence.

SAS is highly reliable and has been highly approved by professionals and analysts. Companies looking for a stable and secure platform use SAS for their analytical requirements.

While SAS may be a closed source software, it offers a wide range of libraries and packages for statistical analysis and machine learning. SAS has an excellent support system meaning that your organization can rely on this tool without any doubt.

However, SAS falls behind with the advent of advanced and open-source software. It is a bit difficult and very expensive to incorporate more advanced tools and features in SAS that modern programming languages provide.



The landscape of data science is evolving quickly, tools used for extracting value from data science have also increased in numbers. Learning any one of the above-mentioned programming languages will kick off your data science career.

Though, there is no specific order to this list of popular languages for data science, Python and R fighting for the top spot. However, having more than one language skills give you versatility and competence as a data scientist.

Also, 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.


Before choosing a programming language, you need to consider several things:

  • Kind of data science tasks will you need to perform
  • Your organization use data science
  • Your company objectives
  • What are your career interests?
  • Programming languages do you already know
  • Level of difficulty are you ready to tackle
  • Your educational ambitions

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