An Overview of Data Science: Programming Languages Insights

According to research conducted by Northeastern University, an average internet user produces around 1.7MB of data every second, and additionally, 2.5 quintillion bytes of data are created every day.

A massive amount of data is now available in structured or unstructured forms. However, organizations widely use this enormous accumulation of data to predict certain evaluations on prior collections. Data science is used to make these calculations and predictions.

What is Data Science?

Data science is the process of extracting, organizing, storing, and analyzing data for valuation purposes. A vast collection of data worldwide is constantly being used by tech giants such as Facebook, Netflix, Google, and others.

Data science uses the basics of math and statistics, programming languages, artificial intelligence, and machine learning to perform computations against the collected data to discover and predict insights from any organization’s data. However, these evaluations can be further used to make decisions and future strategies.

Data Science helps organizations undertake business decisions based on data collection and analysis. This field has enabled organizations to solve complex algorithms and devise strategies to improve results.

According to The US Bureau of Labor Statistics, due to an increased need for data scientists, there will be a 27.9% increase in job opportunities through 2026.

So, if you are already in a technical domain or you want to build a career in data science, then these five programming languages are among the

Best to learn for a career in Data Science:


Python remains on the top of the list for the best programming language used for data science. There are multiple reasons for its wide use and popularity among data scientists. The key reason is its easy syntax and friendly service. Furthermore, it also provides essential tools for data collection, exploration, modeling, and visualization.

Python incorporates some powerful libraries that make data science much easier with it. Some powerful and popular libraries are TensorFlow, Keras, and Matplotlib.


R is a scripting language dedicatedly built and used by statisticians. It is primarily used for statistical computing and graphics. It has the ability to handle complex and large amounts of data. In addition, its data science application includes numerous libraries for data science.


Databases are an essential part of data sciences, so SQL (Structured Query Language), widely used for database management, makes it a necessary player in this domain.

Data science mainly involves playing with data, so SQL helps scientists do the same. SQL provides access to data and statistics, making it a valuable tool for data science. Anyone working with big data will need a strong understanding of SQL to perform database queries.


JavaScript, along with its wide use in web development, is also used in data science. Primarily, it is a scripting language used for building interactive webpages, and it also performs well when creating visualizations for big data.

Although it is a worthy language to master, it is more beneficial for novices in data science than primary data science programming languages.


C is often called the mother of all languages because of its popularity. C/C++ is still widely used today. But it is surprising how these languages have found their way into data science.

Both of these languages, C/C++, pave the proper way for data science. It is due to their quick and efficient compiling of data. So, these two languages enable data scientists to grip the language better.

Apart from the wide use of data science in multiple fields, every business may benefit from data science, but cybersecurity may be the field where it is most crucial. The global cybersecurity company Kaspersky is using science and machine learning to identify hundreds of thousands of new malware samples daily.

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