10 Top Data Science Programming Languages You Should Know

Sharing is Caring : )

Share on facebook
Share on twitter
Share on linkedin
Share on pinterest
Share on tumblr
Share on reddit
Share on whatsapp
Share on facebook
data science programming languages
Sharing is Caring

Suppose you are interested in becoming a data scientist. In that case, you need to have a firm grasp of data science programming languages. In this article, I have described some of the top programming languages for data science.

Data science is a crucial part of any industry these days. A vast volume of data is created every single day. Data science is an interdisciplinary field that uses scientific methods, algorithms, processes, and systems to extract knowledge and visions from many structured and unstructured data. Data science is connected to data mining, machine learning, and big data.

There is not a single language that can solve problems in all areas. You need to be proficient in several languages to achieve the solution to the issues. Therefore I have listed top data science programming languages.

Top Data Science Programming Languages

data science programming languages

Here is the list of most used programming languages for data science.

Python

Python is one of the highly loved and extremely wanted data science programming languages. It is object-oriented, open-source, and effortless to learn a programming language. It has a massive set of libraries and tools designed for data science. Python also has a vast community where developers can ask their queries and answer other questions.

Python plays a vital role in data science. It is often the best choice for various tasks such as Artificial Intelligence, Deep Learning, Machine Learning, etc. It offers powerful data science libraries such as Keras, TensorFlow, matplotlib, Scikit-Learn, etc. Python is one of the most used programming languages for data science.

JavaScript

JavaScript is one of the most used programming languages for web development. Developers can create interactive web pages using JavaScript.

Managing data, asynchronous tasks, and handling of real-time data can be done using JavaScript. It can also be utilized as a visualization tool for data analysis. It also supports several machine learning libraries such as Brain.js, Synaptic, Neataptic, etc.

According to Github repositories contribution, JavaScript is one of the most popular languages in the world.

Java

Java holds an enormous place among desktop, web, and mobile app developers. Java is an object-oriented and class-based programming language that is designed to have a few operations as possible.

Java offers an extensive range of data science tools such as Spark, Hadoop, Hive, etc. Some of the top businesses use Java due to the scalability it offers. It is often considered the best choice to create large-scale machine learning systems. It provides a vast collection of libraries such as DL4J, ADAMS, Java ML, etc.

R

R is another programming language and a software environment for statistical computing. The R programming language is widely used among data miners for data analysis. R is mostly used for handling the statistical and graphical side of data science.

R, which has been here for a very long time it has multiple different packages. It has particular and general-purpose usages. It’s swift and gives very high performance.

R is designed to implement different mathematical concepts like linear algebra quickly. It can deal with concepts such as matrices much better than any other language.

MATLAB

MATLAB is a multi-paradigm numerical computing environment. It’s a computation environment where you can compute different numerical models. That’s what can be very helpful when you are going ahead with data science.

It would help if you created a model, and that model depends on the numerical formula. To make that model, you can use the packages and libraries that are available with MATLAB.

MATLAB has support for many different things like sensors for image video telemetry binary and other real-time formats. You can use all of these different structures. You can import them and perform your statistical analysis on them. It presents a full set of statistics and machine learning functionality plus advanced methods such as nonlinear optimization, system identification, and thousands of pre-built algorithms for image processing. These are different kinds of things.

TensorFlow

TensorFlow, an open-source library for machine learning, emphasizes training and interference of deep neural networks. TensorFlow is an abstract math library based on dataflow and differentiable programming. Tensorflow is one of the best data science programming languages.

Google Brain developed TensorFlow for internal Google use and was released under the Apache license 2.0 in 2015. TensorFlow can run on multiple CPUs and GPUs. It is available on 64-bit Windows, macOS, Linux, and mobile operating systems, including Android and iOS.

Scala

Scala is a general-purpose and open source programming language. It is one of the best-known data science programming languages which runs on JVM. Scala provides support for both object-oriented and functional programming.

Apache Spark, a well-know cluster computing framework, has also written in Scala. As it runs on JVM, it can also be used with Java. It is the best choice if you are working with the high volume data set.

Julia

Julia is a high-level, high performance, dynamic and general-purpose programming language. It can be used to write any application. Most of its features are perfectly suited for numerical analysis and computational science.

Julia is an ideal choice for handling complex projects containing high volume data sets, thanks to its faster performance. It has a built-in package manager.

Julia mainly aims at providing high performance. It is designed for parallel and distributed computing and can offer data visualization and vital tools for deep learning.

SAS

SAS (Statistical Analysis System) is a statistical software suite developed for data management, advanced analytics, business intelligence, and predictive analytics.

It’s used first statistical and data analysis, so it’s an excellent tool for data scientists. However, it is not open-source. It is a paid software suite. For non-technical users, SAS provides a graphical point and click user interface. It can read in data from standard spreadsheet and databases and output the results in the form of tables, graphs and RTF, HTML and PDF.

SQL

SQL stands for “Structural Query Language.” It is designed for managing data in the relational database management system (RDBMS). It can handle structured data, i.e. data incorporating relations among entities and variables.

It reduces turnaround time for online requests by its fast processing time. Most of the organization prefer SQL skillset, as mastering SQL skill can be the biggest asset for machine learning and data science.

Conclusion

Data science is a highly advance and evolving field of study. A data scientist earns a significant amount of money. Corresponding to Glassdoor, a data scientist in the USA makes $113,309 per year. Mastering any one of the skills listed above can kick off your data science career.

Also read:


Sharing is Caring

Sharing is Caring : )

Share on facebook
Share on twitter
Share on linkedin
Share on pinterest
Share on tumblr
Share on reddit
Share on whatsapp
Share on facebook

Related articles

Leave a Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.